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Copyright © 2016 American Society of Plastic Surgeons. Unauthorized reproduction of this article is prohibited.
www.PRSJournal.com890e
I
n the era of evidence-based medicine, a
vast amount of information is collected on
patients.1,2
This information has become
increasingly useful in guiding treatment and opti-
mizing clinical outcomes in medical care. The
result is an ever-expanding volume of data con-
taining complex patterns that may extend beyond
the physician’s ability to use traditional data pro-
cessing techniques such as regression and multi-
variate analysis for interpretation.1,2
As innovators,
plastic surgeons must then adapt to the grow-
ing trend of “big data,” and find ways to tap its
resources to deliver more efficient health care
and improved surgical outcomes.
The answer may lie in “machine learning.”
A  subfield of artificial intelligence, machine
learning involves generating algorithms capa-
ble of knowledge acquisition through historical
examples. Machine learning has already been
applied successfully to big data problems in vari-
ous sectors, with applications including speech
recognition and search engine optimization.3
In
medicine, the IBM Watson Health (International
Business Machines Corp., Armonk, N.Y.) cogni-
tive computing system has used machine learning
approaches to create a decision support system
for physicians treating cancer patients, with the
intention of improving diagnostic accuracy and
reducing costs. Initially trained at Memorial Sloan
Kettering Cancer Center using large volumes of
patient cases and over 1 million scholarly arti-
cles, the project now has 14 participating cancer
centers.4,5
All of these centers contribute to an
ever-expanding corpus of information that helps
Disclosure: None of the authors has a financial
interest in any of the products, devices, drugs or
­procedures mentioned in this article.
Copyright © 2016 by the American Society of Plastic Surgeons
DOI: 10.1097/PRS.0000000000002088
Jonathan Kanevsky, M.D.
Jason Corban, B.Sc.
Richard Gaster, M.D., Ph.D.
Ari Kanevsky
Samuel Lin, M.D.
Mirko Gilardino, M.D.,
M.Sc.
Montreal, Quebec, Canada; Boston,
Mass.; and Albany, N.Y.
Summary: Medical decision-making is increasingly based on quantifiable data.
From the moment patients come into contact with the health care system,
their entire medical history is recorded electronically. Whether a patient is in
the operating room or on the hospital ward, technological advancement has
facilitated the expedient and reliable measurement of clinically relevant health
metrics, all in an effort to guide care and ensure the best possible clinical
outcomes. However, as the volume and complexity of biomedical data grow,
it becomes challenging to effectively process “big data” using conventional
techniques. Physicians and scientists must be prepared to look beyond clas-
sic methods of data processing to extract clinically relevant information. The
purpose of this article is to introduce the modern plastic surgeon to machine
learning and computational interpretation of large data sets. What is machine
learning? Machine learning, a subfield of artificial intelligence, can address
clinically relevant problems in several domains of plastic surgery, including
burn surgery; microsurgery; and craniofacial, peripheral nerve, and aesthetic
surgery. This article provides a brief introduction to current research and sug-
gests future projects that will allow plastic surgeons to explore this new frontier
of surgical science.  (Plast. Reconstr. Surg. 137: 890e, 2016.)
From the Division of Plastic and Reconstructive Surgery,
Faculty of Medicine, McGill University; the Division of
Plastic and Reconstructive Surgery, Harvard University, the
Division of Plastic and Reconstructive Surgery, Beth Israel
Deaconess Medical Center; and the Department of Biological
Sciences, University at Albany.
Received for publication May 22, 2015; accepted December
22, 2015.
Big Data and Machine Learning in Plastic
Surgery: A New Frontier in Surgical Innovation
Supplemental digital content is available for
this article. Direct URL citations appear in the
text; simply type the URL address into any Web
browser to access this content. Clickable links
to the material are provided in the HTML text
of this article on the Journal’s Web site (www.
PRSJournal.com).
TECHNOLOGY & Innovations
Copyright © 2016 American Society of Plastic Surgeons. Unauthorized reproduction of this article is prohibited.
Volume 137, Number 5 • Big Data and Machine Learning
891e
Watson fine tune its ability to suggest optimal
treatment options for cancer patients based on
the nature of their specific illness.4,5
Although similar to data mining, which tradi-
tionally involves knowledge acquisition through
analysis of preexisting data sets, machine learning
places a greater emphasis on descriptive modeling
and outcome prediction for novel data.5
Further-
more, machine learning algorithms are capable
of improving or “learning” when exposed to more
information.5,6
As the algorithm attempts to find
the most appropriate hypotheses for a given data
set, within the computational boundaries of the spe-
cific machine learning approach used, it statistically
assesses how each model compares to each other
and models that have been assessed previously.6
The
result of this process is the creation of data models
that are either predictive or descriptive in nature.
Predictive machine learning models fall
under the domain of supervised learning, where
the algorithm has been trained using examples
of both inputs and desired outputs, allowing for
mapping of future inputs to outputs.6,7
The goal
of this process is a unique mathematical model
capable of predicting desired target values from
novel data (Fig. 1). For example, a recent surgi-
cal application involved the use of data from the
American College of Surgeons National Surgical
Quality Improvement Program to train a support
vector machine to quantify procedural complex-
ity and risk associated with different procedures
based on Current Procedural Terminology codes.8
Using data from 2005 to 2009, the support vector
machine was trained to determine the association
between Current Procedural Terminology and
mortality, morbidity, Clavien classification type
IV complications, and surgical-site infection to
produce an algorithm capable of generating pro-
cedural risk scores.8
When tested using National
Surgical Quality Improvement Program data
from 2010, the support vector machine approach
achieved a greater level of discrimination for
determining surgical complications compared
with other measures of procedural complexity.8
Descriptive models, in contrast, fall under the
category of “unsupervised learning.” Unsupervised
learning analyzes data that are unlabeled, and the
system discovers structure in the data for interpre-
tation (Fig. 2).6,9
(See Video, Supplemental Digital
Content 1, which highlights the difference between
supervised and unsupervised machine learning
using hypothetical machine learning algorithms
capable of processing visual data for the detection
and differentiation of different types of craniosyn-
ostosis. Available in the “Related Videos” section of
the full-text article on PRSJournal.com or, for Ovid
users, available at http://links.lww.com/PRS/B706.)
This type of machine learning has been applied
in the field of molecular genetics and genomics
to organize and interpret vast amounts of genetic
information.10
Following the application of leu-
kemic blasts from pediatric acute lymphoblastic
Fig. 1. Graphic representation of supervised machine learning. In supervised learning, original prepro-
cessed data sets, containing known variables and targets, are divided into training data and test data.
(Above) During the training phase, the training data are used to train a learning algorithm in an attempt
to develop an accurate predictive model. (Center) To validate the model, the test data are then applied
to the model and predictive accuracy is assessed. (Below) Once validated, new data are input into the
model in an attempt to make new predictions.
Copyright © 2016 American Society of Plastic Surgeons. Unauthorized reproduction of this article is prohibited.
892e
Plastic and Reconstructive Surgery • May 2016
leukemia patients to microarrays, unsupervised
clustering of the data identified six new clinical
subtypes of acute lymphoblastic leukemia.10
As illustrated above, machine learning has
already been applied, with great success, to pro-
cess large amounts of complex data in medicine
and surgery. With the volumes of patient data gen-
erated in all domains of plastic surgery and the
emergence of large databases such as the National
Surgical Quality Improvement Program and Track-
ing Operations and Outcomes for Plastic Surgeons
for storing this information, plastic surgeons stand
to benefit from similar objective and data-driven
machine learning approaches. This article presents
a selection of preliminary investigations in the fields
of burn surgery; microsurgery; and craniofacial,
peripheral nerve, hand, and aesthetic surgery, and
proposes future applications in an effort to demon-
strate how machine learning may be used to lever-
age complex, clinically derived data into improved
efficiency and better clinical outcomes in plastic
surgery. Institutional review board exemption was
granted by our medical center review board.
CURRENT AND FUTURE APPLICATIONS
OF MACHINE LEARNING IN PLASTIC
SURGERY
Burn Surgery
An early application of machine learning
related to plastic surgery was the development of
a method to accurately determine healing time in
burn injury.11
Using reflectance spectrometry and
an artificial neural network, researchers devel-
oped a model to predict whether a burn would
take more or less than 14 days to heal, ultimately
Fig. 2. Graphic representation of unsupervised machine learning. In unsupervised learning, raw data,
containing unknown patterns and targets, are presented to an algorithm. The algorithm attempts to
develop descriptive models for the data based on regularities detected. (Adapted from Hudson Legal.
Unsupervisedlearning.Availableat:http://us.hudson.com/portals/US/images/blogs/legal/wp/2011/09/
Unsupervised-Learning1.jpg. Accessed October 6, 2014.)
Video. Supplemental Digital Content 1 highlights the difference
between supervised and unsupervised machine learning using
hypothetical machine learning algorithms capable of process-
ing visual data for the detection and differentiation of different
types of craniosynostosis. Available in the “Related Videos” sec-
tion of the full-text article on PRSJournal.com or, for Ovid users,
available at http://links.lww.com/PRS/B706.
Copyright © 2016 American Society of Plastic Surgeons. Unauthorized reproduction of this article is prohibited.
Volume 137, Number 5 • Big Data and Machine Learning
893e
serving as a proxy for the assessment of burn
depth for surgical planning. Artificial neural net-
works consist of input nodes, representing the
data to be used for prediction, intermediate or
“hidden nodes” that calculate predictions based
on the inputs, and output nodes that represent
the predictions themselves.6
During training, arti-
ficial neural networks are tuned through a pro-
cess of “back-propagation” where the accuracy of
the output values is compared to the actual target
values (Fig. 3).6
In this investigation, normalized
spectral data served as input nodes, whereas the
two output nodes distinguished between spec-
tra associated with burns that healed in less than
14 days and those associated with burns that took
more than 14 days to heal.11
After examining
reflectance spectrometry data from 41 wounds,
the investigators determined that their model had
an average predictive accuracy of 86 percent, sug-
gesting that it may serve as an effective screening
tool for assessing burn depth and a superior alter-
native to direct visualization.11
Another task within burn care that might lend
itself to machine learning is the accurate and pre-
cise quantification of burn size (total body sur-
face area). Current methods, such as the “rule
of nines,” are limited by the asymmetry of burns,
surface area variations related to patient age, and
interobserver variability. By pairing images of
burns to precise measurements of the percent-
age of body area affected, an algorithm could be
trained to rapidly and accurately predict the per-
centage of burned tissue (Fig. 4). From these mea-
surements, more accurate resuscitation protocols
could be generated in addition to surgical plan-
ning strategies for autografting or allografting.
Microsurgery
Postoperative monitoring after microsurgery is
crucial for achieving desired clinical outcomes. In
light of this, researchers have recently developed
a postoperative microsurgery monitoring applica-
tion, the SilpaRamanitor, capable of quantifying
free-flap tissue perfusion.12
Using 60 smartphone
images of middle and index fingers exposed to
varying degrees of vascular compromise to mimic
vascular occlusion, a k-nearest neighbor algorithm
was trained to categorize flap tissue into different
classes: normal, venous outflow occlusion, and
arterial inflow occlusion.12
In cases of occlusion,
the degree was further categorized as partial or
complete. The k-nearest neighbor algorithm is
a non–parameter supervised learning approach
whereby the classification rules are generated
by the training samples themselves and do not
require the input of additional information.13
The
Fig. 3. Graphic representation of an artificial neural network. Modeled after biological neural
networks, artificial neural networks use input nodes, representing data input into the model;
hidden nodes, responsible for making the predictions); and output nodes, representative of
the predictions being made (Oncologists partner with Watson on genomics. Cancer Discov.
2015;5:788). During training, artificial neural networks, in a fashion similar to biological neurons,
take part in a process called back-propagation, whereby the weight of the connections between
nodes is adjusted based on the difference between the artificial neural networks output values
and known target values. This process ensures that the output of the artificial neural network is
as close as possible to the desired target values. (Adapted from Meyfroidt G, Güiza F, Ramon J,
Bruynooghe M. Machine learning techniques to examine large patient databases. Best Pract Res
Clin Anaesthesiol. 2009;23:127–143.)
Copyright © 2016 American Society of Plastic Surgeons. Unauthorized reproduction of this article is prohibited.
894e
Plastic and Reconstructive Surgery • May 2016
algorithm assigns a test sample, which in this study
was an image of a free flap, to a category, which
was the type and degree of occlusion.12
This classi-
fication is based on features of the test sample that
are the most similar, or the “nearest neighbors,” to
those from the training set.12
The overall sensitiv-
ity and specificity of this application were found
to be 94 percent and 98 percent, respectively,
with an accuracy of 95 percent.12
Thus, through
the accurate and rapid monitoring of free-flap
perfusion, the SilpaRamanitor application is an
example of how machine learning can be used to
potentially increase the success of detecting early
anastomotic failure or thrombotic issues and con-
comitant free-flap salvage.12
However, the spectrum of potential applica-
tions of machine learning for microsurgery are
not only limited to postoperative monitoring.
Machine learning could also benefit preoperative
consultation and surgical planning for microsur-
gery. Through the collection of detailed infor-
mation such as the size and location of defects,
the type of flap used, patient age, body mass
index, smoking status, and resultant complica-
tions in large-scale databases (such as Tracking
Operations and Outcomes for Plastic Surgeons),
machine learning algorithms could be trained
to assess a particular defect in a selected patient
and suggest the reconstructive approach with the
highest chance of a favorable outcome.
Craniofacial Surgery
Currently, machine learning is being explored
to facilitate the automated diagnosis of non-
syndromic craniosynostosis. Examining com-
puted tomographic scans from 141 subjects, of
which 50 had either sagittal, metopic, or coronal
craniosynostoses, a regularized linear discrimi-
nant analysis algorithm was trained to diagnose
craniosynostosis and distinguish between differ-
ent types using an index of cranial suture fusion
along with deformation and curvature discrep-
ancy averages across five cranial bones and six
suture regions.14
Regularized linear discriminant
analysis is frequently used for high-dimensional
data when there are a small number of samples,
as was the case in this investigation.15
The result
of this machine learning process is an automated
classifier capable of differentiating types of cra-
niosynostosis based on computed tomographic
scans, with a sensitivity of 92.3 percent and a speci-
ficity of 98.9 percent.14
With accuracy comparable
to trained radiologists, the authors propose that
their algorithm may provide an objective standard
capable of reducing interobserver variability and
providing a quantitative measure of procedural
success.14
Although the automation of this process has
many benefits, we propose that machine learn-
ing theoretically has the potential to enable
surgeons to bypass the use of computed tomo-
graphic imaging for routine diagnostics. Using
three-dimensional surface photographs of differ-
ent plagiocephalies, an algorithm could be devel-
oped to differentiate between cases of synostotic
and deformational plagiocephaly. Together with
clinical examination, the goal would be to further
reduce the need for ionizing radiation in these
children.
Another potential machine learning applica-
tion for craniofacial surgery involves the identifi-
cation of candidate genes in nonsyndromic cases
of cleft lip and palate.16
Through a combination
of genomewide association studies and other
Fig. 4. By pairing images of burns to precise measurements of the per-
centage of body area affected, an algorithm could be trained to use
images to rapidly and accurately predict the percentage of burned tissue
and automate part of assessment of burn patients.
Copyright © 2016 American Society of Plastic Surgeons. Unauthorized reproduction of this article is prohibited.
Volume 137, Number 5 • Big Data and Machine Learning
895e
investigations, several genetic factors have been
elucidated for this condition for which the cause is
poorly understood.16
However, our understanding
of the molecular pathogenesis of nonsyndromic
cleft lip and palate remains far from comprehen-
sive. Using methods similar to those used previ-
ously in the field of genomics, machine learning
has the potential to uncover previously unknown
candidate genes and regulatory sequences for
nonsyndromic cleft lip and palate, allowing for an
improved understanding of the pathogenesis of
this condition.16
Hand and Peripheral Nerve Surgery
Research in the field of hand and peripheral
nerve surgery has the potential to benefit from
machine learning. As an example, investigators
have recently developed an artificial neural net-
work capable of predicting the outcome of differ-
ent tissue-engineered peripheral nerve grafts used
in research applications.17
Using over 30 inde-
pendent variables to describe tissue-engineering
materials, artificial neural networks were trained
to predict the success of various grafts.17
The suc-
cess of the grafts, placed in a rat model, was quan-
tified using the critical regeneration length, along
with a unitless parameter, the ratio of the actual
length to the critical length.17
After application
of the validation data, the predicative accuracy of
artificial neural networks was 92.59 percent and
90.85 percent for the ratio of the actual length to
the critical length and the critical regeneration
length, respectively.17
Although preliminary, the
results of this investigation highlight the poten-
tial role for machine learning in the analysis and
development of tissue-engineering strategies for
peripheral nerve repair.
The biomechanics of the upper extremities
are particularly complex, involving multivari-
ate nonlinear relationships that are theoretically
amenable to modeling by machine learning. In
light of this, artificial neural networks have been
used to develop automated controllers for a vari-
ety of neuroprostheses, including those that are
used to restore hand grasp and wrist control
along with more proximal upper extremity func-
tion in patients with C5/C6 spinal cord injury.18,19
Although the results of these initial investigations
were mixed, they highlight the potential for arti-
ficial neural networks, along with other machine
learning techniques, in the development of neu-
roprosthetic controllers for the hand and wrist.
Along with the examples presented above,
machine learning has the potential to provide
additional innovation in hand and nerve surgery.
For example, using information from previous
cases and radiographic images of fractures, an
algorithm could be developed to anticipate the
positioning of Kirschner wires, plates, and screws
in preoperative planning for hand surgery. Fur-
thermore, using databases containing informa-
tion derived from sensorial mapping following
peripheral nerve repair, patterns of regrowth
could be used to develop an algorithm capable of
prognosticating the degree of sensory and motor
restoration based on location, mechanism of
nerve injury, and physical examination findings.
Aesthetic Surgery
Machine learning also has potential applica-
tions in more subjective areas of plastic surgery,
such as aesthetics. Using a form of supervised
learning, an automated classifier for facial beauty
was trained using extracted facial features from
165 images of attractive female faces that were
also independently graded by human referees.20
Decision tree algorithms assess a set of descrip-
tive attributes, which in this particular investiga-
tion included different facial ratios, and attempt
to determine attractive facial features most
closely related to postoperative target variables—
the human classification of facial beauty.20
When
subjected to the testing set of images, the auto-
mated classifier was shown to have a high accu-
racy at approximating human referee scores.20
In
light of these results, this application may serve
as a predictive tool for estimating a patient’s per-
ceived beauty following aesthetic surgery, provid-
ing a quantitative measure to set expectations
and possibly discourage patients from undergo-
ing procedures that offer marginal improvement.
In conjunction with optical head-mounted
display technology, machine learning also has
the potential to facilitate intraoperative visual-
ization of surgical outcomes. One such applica-
tion could be in reconstructive breast surgery,
whereby machine learning software incorporated
into a head mount display could predict what a
breast might appear like in three-dimensional
space based on potential changes in implant posi-
tion. The system could be trained to identify fea-
tures of breast aesthetics such as symmetry, nipple
position, superior pole fullness, and degree of
ptosis, optimizing aesthetic results while mini-
mizing trauma and operating time. Although
these potential tools may not replace the trained
human eye, they would aid the plastic surgeon by
providing a higher degree of objectivity to aes-
thetic surgery.
Copyright © 2016 American Society of Plastic Surgeons. Unauthorized reproduction of this article is prohibited.
896e
Plastic and Reconstructive Surgery • May 2016
Resident Evaluation and Teaching
Currently, competency in plastic surgery train-
ing is assessed through written and oral exami-
nations coupled with case logs and subjective
evaluation by attending physicians and higher
level residents.21
However, more objective methods
for assessing “surgical competency” are required,
which may be facilitated by machine learning.
Recently, with the development of head-mounted
cameras for resident teaching and wearable tech-
nology, such as Google Glass (Google, Inc., Moun-
tain View, Calif.), trainees are able record their
cases and compare their performance to previous
recordings of themselves and expert attending
surgeons.21
By tracking various metrics, trainees
can be graded on their surgical skills and realize
methods for improvement. However, this tech-
nology could be enhanced through the addition
of sensors for precisely tracking eye movement,
hand motion, and effective use of instruments
and assistants. Machine learning could be used to
identify salient aspects from the expert recordings
that were absent or present in that of the train-
ees, allowing for systematic and objective assess-
ment of areas that need improvement in real time
(Fig. 5). In addition, a set of objective milestones
can be developed for systematic grading of surgi-
cal competency.
Postoperative follow-up is also of paramount
importance for an outcomes-based specialty
such as plastic surgery and is an aspect of surgi-
cal residency training that needs improvement.
By integrating intraoperative recording of the
steps and techniques used during surgery with
postoperative findings and images, machine
learning could be used to identify the surgical
techniques that lead to a particular outcome.
This information would ultimately be linked to
the resident involved in the procedure, ensuring
appropriate long-term follow-up and facilitat-
ing targeted feedback for future cases, some-
thing that is difficult with the current pedagogic
model, where residents rotate to different sites
and services.
LIMITATIONS
Although the potential applications and ben-
efits of machine learning for the field of plastic
surgery are evident, to safely apply the findings
obtained using this technology, clinicians must be
aware of its limitations. Most importantly, machine
learning has been criticized for exhibiting “black
box” characteristics, with algorithms that pro-
vide little or no justification for the outputs they
provide.22
Furthermore, the learning behavior of
some machines has been shown to be difficult to
reproduce when similar training data are applied
to different machine learning algorithms.23
To overcome these challenges, it has been sug-
gested that future machine learning algorithms
could be programmed to include justifications
for their decisions.24
However, certain measures
can be taken to demonstrate the diagnostic valid-
ity of currently available algorithms. As demon-
strated by many of the applications presented
here, results obtained using machine learning
can be compared to those derived from current
gold standards for diagnosis. Another interesting
approach is the emerging trend of “crowdsourc-
ing analytics.”24
Through the use of multiple algo-
rithms, or “crowdsourcing,” to address a specific
problem, more accurate models of data can be
obtained and simultaneously allow for the evalua-
tion of the strengths and weaknesses of the differ-
ent algorithms being used.24
Another concern is that some investigators
may apply machine learning without the expertise
to critically assess their results.23
To overcome this,
investigators who wish to use machine learning
to tackle complex problems should work closely
with data scientists who are capable of accurately
evaluating the validity of the outputs obtained.26
Ultimately, this would ensure that the results
obtained using machine learning technology are
interpreted correctly and are being applied in a
safe and clinically relevant manner.
Fig. 5. A mock recording of a resident carrying out a carpal tun-
nel release using a wearable technological device.The recording
has been optimized using unsupervised learning approaches to
identify salient features from expert recordings that are either
absent or present in that of the trainees, allowing for direction
and correction in real time.
Copyright © 2016 American Society of Plastic Surgeons. Unauthorized reproduction of this article is prohibited.
Volume 137, Number 5 • Big Data and Machine Learning
897e
CONCLUSIONS
This introductory review of machine learning
highlights the potential this technology has for
catalyzing a paradigm shift in research and clini-
cal practice in plastic surgery. Machine learning
has already demonstrated great success in a vari-
ety of fields, including several medical disciplines.
In plastic surgery, we have demonstrated that
machine learning has the potential to become
a powerful tool, allowing surgeons to harness
complex clinical data to help guide key clinical
decision-making. Although a potentially power-
ful tool, computer-generated algorithms will not
replace the trained human eye. However, these
are tools that may help us not only in the deci-
sion-making process but also in finding patterns
that might not be evident in analysis of smaller
data sets or anecdotal experience. By embracing
machine learning, modern plastic surgeons may
be able to redefine the specialty while solidifying
their role as leaders at the forefront of scientific
advancement in surgery.
Mirko Gilardino, M.D., M.Sc.
Montreal Children’s Hospital
2300 Tupper Street, Room C-1135
Montreal, Quebec H3H 1P3, Canada
mirkogilardino@hotmail.com
Samuel Lin, M.D.
Beth Israel Deaconess Medical Center
110 Francis Street, Suite 5A
Boston, Mass. 02215
sjlin@bidmc.harvard.edu
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  • 1. Copyright © 2016 American Society of Plastic Surgeons. Unauthorized reproduction of this article is prohibited. www.PRSJournal.com890e I n the era of evidence-based medicine, a vast amount of information is collected on patients.1,2 This information has become increasingly useful in guiding treatment and opti- mizing clinical outcomes in medical care. The result is an ever-expanding volume of data con- taining complex patterns that may extend beyond the physician’s ability to use traditional data pro- cessing techniques such as regression and multi- variate analysis for interpretation.1,2 As innovators, plastic surgeons must then adapt to the grow- ing trend of “big data,” and find ways to tap its resources to deliver more efficient health care and improved surgical outcomes. The answer may lie in “machine learning.” A  subfield of artificial intelligence, machine learning involves generating algorithms capa- ble of knowledge acquisition through historical examples. Machine learning has already been applied successfully to big data problems in vari- ous sectors, with applications including speech recognition and search engine optimization.3 In medicine, the IBM Watson Health (International Business Machines Corp., Armonk, N.Y.) cogni- tive computing system has used machine learning approaches to create a decision support system for physicians treating cancer patients, with the intention of improving diagnostic accuracy and reducing costs. Initially trained at Memorial Sloan Kettering Cancer Center using large volumes of patient cases and over 1 million scholarly arti- cles, the project now has 14 participating cancer centers.4,5 All of these centers contribute to an ever-expanding corpus of information that helps Disclosure: None of the authors has a financial interest in any of the products, devices, drugs or ­procedures mentioned in this article. Copyright © 2016 by the American Society of Plastic Surgeons DOI: 10.1097/PRS.0000000000002088 Jonathan Kanevsky, M.D. Jason Corban, B.Sc. Richard Gaster, M.D., Ph.D. Ari Kanevsky Samuel Lin, M.D. Mirko Gilardino, M.D., M.Sc. Montreal, Quebec, Canada; Boston, Mass.; and Albany, N.Y. Summary: Medical decision-making is increasingly based on quantifiable data. From the moment patients come into contact with the health care system, their entire medical history is recorded electronically. Whether a patient is in the operating room or on the hospital ward, technological advancement has facilitated the expedient and reliable measurement of clinically relevant health metrics, all in an effort to guide care and ensure the best possible clinical outcomes. However, as the volume and complexity of biomedical data grow, it becomes challenging to effectively process “big data” using conventional techniques. Physicians and scientists must be prepared to look beyond clas- sic methods of data processing to extract clinically relevant information. The purpose of this article is to introduce the modern plastic surgeon to machine learning and computational interpretation of large data sets. What is machine learning? Machine learning, a subfield of artificial intelligence, can address clinically relevant problems in several domains of plastic surgery, including burn surgery; microsurgery; and craniofacial, peripheral nerve, and aesthetic surgery. This article provides a brief introduction to current research and sug- gests future projects that will allow plastic surgeons to explore this new frontier of surgical science.  (Plast. Reconstr. Surg. 137: 890e, 2016.) From the Division of Plastic and Reconstructive Surgery, Faculty of Medicine, McGill University; the Division of Plastic and Reconstructive Surgery, Harvard University, the Division of Plastic and Reconstructive Surgery, Beth Israel Deaconess Medical Center; and the Department of Biological Sciences, University at Albany. Received for publication May 22, 2015; accepted December 22, 2015. Big Data and Machine Learning in Plastic Surgery: A New Frontier in Surgical Innovation Supplemental digital content is available for this article. Direct URL citations appear in the text; simply type the URL address into any Web browser to access this content. Clickable links to the material are provided in the HTML text of this article on the Journal’s Web site (www. PRSJournal.com). TECHNOLOGY & Innovations
  • 2. Copyright © 2016 American Society of Plastic Surgeons. Unauthorized reproduction of this article is prohibited. Volume 137, Number 5 • Big Data and Machine Learning 891e Watson fine tune its ability to suggest optimal treatment options for cancer patients based on the nature of their specific illness.4,5 Although similar to data mining, which tradi- tionally involves knowledge acquisition through analysis of preexisting data sets, machine learning places a greater emphasis on descriptive modeling and outcome prediction for novel data.5 Further- more, machine learning algorithms are capable of improving or “learning” when exposed to more information.5,6 As the algorithm attempts to find the most appropriate hypotheses for a given data set, within the computational boundaries of the spe- cific machine learning approach used, it statistically assesses how each model compares to each other and models that have been assessed previously.6 The result of this process is the creation of data models that are either predictive or descriptive in nature. Predictive machine learning models fall under the domain of supervised learning, where the algorithm has been trained using examples of both inputs and desired outputs, allowing for mapping of future inputs to outputs.6,7 The goal of this process is a unique mathematical model capable of predicting desired target values from novel data (Fig. 1). For example, a recent surgi- cal application involved the use of data from the American College of Surgeons National Surgical Quality Improvement Program to train a support vector machine to quantify procedural complex- ity and risk associated with different procedures based on Current Procedural Terminology codes.8 Using data from 2005 to 2009, the support vector machine was trained to determine the association between Current Procedural Terminology and mortality, morbidity, Clavien classification type IV complications, and surgical-site infection to produce an algorithm capable of generating pro- cedural risk scores.8 When tested using National Surgical Quality Improvement Program data from 2010, the support vector machine approach achieved a greater level of discrimination for determining surgical complications compared with other measures of procedural complexity.8 Descriptive models, in contrast, fall under the category of “unsupervised learning.” Unsupervised learning analyzes data that are unlabeled, and the system discovers structure in the data for interpre- tation (Fig. 2).6,9 (See Video, Supplemental Digital Content 1, which highlights the difference between supervised and unsupervised machine learning using hypothetical machine learning algorithms capable of processing visual data for the detection and differentiation of different types of craniosyn- ostosis. Available in the “Related Videos” section of the full-text article on PRSJournal.com or, for Ovid users, available at http://links.lww.com/PRS/B706.) This type of machine learning has been applied in the field of molecular genetics and genomics to organize and interpret vast amounts of genetic information.10 Following the application of leu- kemic blasts from pediatric acute lymphoblastic Fig. 1. Graphic representation of supervised machine learning. In supervised learning, original prepro- cessed data sets, containing known variables and targets, are divided into training data and test data. (Above) During the training phase, the training data are used to train a learning algorithm in an attempt to develop an accurate predictive model. (Center) To validate the model, the test data are then applied to the model and predictive accuracy is assessed. (Below) Once validated, new data are input into the model in an attempt to make new predictions.
  • 3. Copyright © 2016 American Society of Plastic Surgeons. Unauthorized reproduction of this article is prohibited. 892e Plastic and Reconstructive Surgery • May 2016 leukemia patients to microarrays, unsupervised clustering of the data identified six new clinical subtypes of acute lymphoblastic leukemia.10 As illustrated above, machine learning has already been applied, with great success, to pro- cess large amounts of complex data in medicine and surgery. With the volumes of patient data gen- erated in all domains of plastic surgery and the emergence of large databases such as the National Surgical Quality Improvement Program and Track- ing Operations and Outcomes for Plastic Surgeons for storing this information, plastic surgeons stand to benefit from similar objective and data-driven machine learning approaches. This article presents a selection of preliminary investigations in the fields of burn surgery; microsurgery; and craniofacial, peripheral nerve, hand, and aesthetic surgery, and proposes future applications in an effort to demon- strate how machine learning may be used to lever- age complex, clinically derived data into improved efficiency and better clinical outcomes in plastic surgery. Institutional review board exemption was granted by our medical center review board. CURRENT AND FUTURE APPLICATIONS OF MACHINE LEARNING IN PLASTIC SURGERY Burn Surgery An early application of machine learning related to plastic surgery was the development of a method to accurately determine healing time in burn injury.11 Using reflectance spectrometry and an artificial neural network, researchers devel- oped a model to predict whether a burn would take more or less than 14 days to heal, ultimately Fig. 2. Graphic representation of unsupervised machine learning. In unsupervised learning, raw data, containing unknown patterns and targets, are presented to an algorithm. The algorithm attempts to develop descriptive models for the data based on regularities detected. (Adapted from Hudson Legal. Unsupervisedlearning.Availableat:http://us.hudson.com/portals/US/images/blogs/legal/wp/2011/09/ Unsupervised-Learning1.jpg. Accessed October 6, 2014.) Video. Supplemental Digital Content 1 highlights the difference between supervised and unsupervised machine learning using hypothetical machine learning algorithms capable of process- ing visual data for the detection and differentiation of different types of craniosynostosis. Available in the “Related Videos” sec- tion of the full-text article on PRSJournal.com or, for Ovid users, available at http://links.lww.com/PRS/B706.
  • 4. Copyright © 2016 American Society of Plastic Surgeons. Unauthorized reproduction of this article is prohibited. Volume 137, Number 5 • Big Data and Machine Learning 893e serving as a proxy for the assessment of burn depth for surgical planning. Artificial neural net- works consist of input nodes, representing the data to be used for prediction, intermediate or “hidden nodes” that calculate predictions based on the inputs, and output nodes that represent the predictions themselves.6 During training, arti- ficial neural networks are tuned through a pro- cess of “back-propagation” where the accuracy of the output values is compared to the actual target values (Fig. 3).6 In this investigation, normalized spectral data served as input nodes, whereas the two output nodes distinguished between spec- tra associated with burns that healed in less than 14 days and those associated with burns that took more than 14 days to heal.11 After examining reflectance spectrometry data from 41 wounds, the investigators determined that their model had an average predictive accuracy of 86 percent, sug- gesting that it may serve as an effective screening tool for assessing burn depth and a superior alter- native to direct visualization.11 Another task within burn care that might lend itself to machine learning is the accurate and pre- cise quantification of burn size (total body sur- face area). Current methods, such as the “rule of nines,” are limited by the asymmetry of burns, surface area variations related to patient age, and interobserver variability. By pairing images of burns to precise measurements of the percent- age of body area affected, an algorithm could be trained to rapidly and accurately predict the per- centage of burned tissue (Fig. 4). From these mea- surements, more accurate resuscitation protocols could be generated in addition to surgical plan- ning strategies for autografting or allografting. Microsurgery Postoperative monitoring after microsurgery is crucial for achieving desired clinical outcomes. In light of this, researchers have recently developed a postoperative microsurgery monitoring applica- tion, the SilpaRamanitor, capable of quantifying free-flap tissue perfusion.12 Using 60 smartphone images of middle and index fingers exposed to varying degrees of vascular compromise to mimic vascular occlusion, a k-nearest neighbor algorithm was trained to categorize flap tissue into different classes: normal, venous outflow occlusion, and arterial inflow occlusion.12 In cases of occlusion, the degree was further categorized as partial or complete. The k-nearest neighbor algorithm is a non–parameter supervised learning approach whereby the classification rules are generated by the training samples themselves and do not require the input of additional information.13 The Fig. 3. Graphic representation of an artificial neural network. Modeled after biological neural networks, artificial neural networks use input nodes, representing data input into the model; hidden nodes, responsible for making the predictions); and output nodes, representative of the predictions being made (Oncologists partner with Watson on genomics. Cancer Discov. 2015;5:788). During training, artificial neural networks, in a fashion similar to biological neurons, take part in a process called back-propagation, whereby the weight of the connections between nodes is adjusted based on the difference between the artificial neural networks output values and known target values. This process ensures that the output of the artificial neural network is as close as possible to the desired target values. (Adapted from Meyfroidt G, Güiza F, Ramon J, Bruynooghe M. Machine learning techniques to examine large patient databases. Best Pract Res Clin Anaesthesiol. 2009;23:127–143.)
  • 5. Copyright © 2016 American Society of Plastic Surgeons. Unauthorized reproduction of this article is prohibited. 894e Plastic and Reconstructive Surgery • May 2016 algorithm assigns a test sample, which in this study was an image of a free flap, to a category, which was the type and degree of occlusion.12 This classi- fication is based on features of the test sample that are the most similar, or the “nearest neighbors,” to those from the training set.12 The overall sensitiv- ity and specificity of this application were found to be 94 percent and 98 percent, respectively, with an accuracy of 95 percent.12 Thus, through the accurate and rapid monitoring of free-flap perfusion, the SilpaRamanitor application is an example of how machine learning can be used to potentially increase the success of detecting early anastomotic failure or thrombotic issues and con- comitant free-flap salvage.12 However, the spectrum of potential applica- tions of machine learning for microsurgery are not only limited to postoperative monitoring. Machine learning could also benefit preoperative consultation and surgical planning for microsur- gery. Through the collection of detailed infor- mation such as the size and location of defects, the type of flap used, patient age, body mass index, smoking status, and resultant complica- tions in large-scale databases (such as Tracking Operations and Outcomes for Plastic Surgeons), machine learning algorithms could be trained to assess a particular defect in a selected patient and suggest the reconstructive approach with the highest chance of a favorable outcome. Craniofacial Surgery Currently, machine learning is being explored to facilitate the automated diagnosis of non- syndromic craniosynostosis. Examining com- puted tomographic scans from 141 subjects, of which 50 had either sagittal, metopic, or coronal craniosynostoses, a regularized linear discrimi- nant analysis algorithm was trained to diagnose craniosynostosis and distinguish between differ- ent types using an index of cranial suture fusion along with deformation and curvature discrep- ancy averages across five cranial bones and six suture regions.14 Regularized linear discriminant analysis is frequently used for high-dimensional data when there are a small number of samples, as was the case in this investigation.15 The result of this machine learning process is an automated classifier capable of differentiating types of cra- niosynostosis based on computed tomographic scans, with a sensitivity of 92.3 percent and a speci- ficity of 98.9 percent.14 With accuracy comparable to trained radiologists, the authors propose that their algorithm may provide an objective standard capable of reducing interobserver variability and providing a quantitative measure of procedural success.14 Although the automation of this process has many benefits, we propose that machine learn- ing theoretically has the potential to enable surgeons to bypass the use of computed tomo- graphic imaging for routine diagnostics. Using three-dimensional surface photographs of differ- ent plagiocephalies, an algorithm could be devel- oped to differentiate between cases of synostotic and deformational plagiocephaly. Together with clinical examination, the goal would be to further reduce the need for ionizing radiation in these children. Another potential machine learning applica- tion for craniofacial surgery involves the identifi- cation of candidate genes in nonsyndromic cases of cleft lip and palate.16 Through a combination of genomewide association studies and other Fig. 4. By pairing images of burns to precise measurements of the per- centage of body area affected, an algorithm could be trained to use images to rapidly and accurately predict the percentage of burned tissue and automate part of assessment of burn patients.
  • 6. Copyright © 2016 American Society of Plastic Surgeons. Unauthorized reproduction of this article is prohibited. Volume 137, Number 5 • Big Data and Machine Learning 895e investigations, several genetic factors have been elucidated for this condition for which the cause is poorly understood.16 However, our understanding of the molecular pathogenesis of nonsyndromic cleft lip and palate remains far from comprehen- sive. Using methods similar to those used previ- ously in the field of genomics, machine learning has the potential to uncover previously unknown candidate genes and regulatory sequences for nonsyndromic cleft lip and palate, allowing for an improved understanding of the pathogenesis of this condition.16 Hand and Peripheral Nerve Surgery Research in the field of hand and peripheral nerve surgery has the potential to benefit from machine learning. As an example, investigators have recently developed an artificial neural net- work capable of predicting the outcome of differ- ent tissue-engineered peripheral nerve grafts used in research applications.17 Using over 30 inde- pendent variables to describe tissue-engineering materials, artificial neural networks were trained to predict the success of various grafts.17 The suc- cess of the grafts, placed in a rat model, was quan- tified using the critical regeneration length, along with a unitless parameter, the ratio of the actual length to the critical length.17 After application of the validation data, the predicative accuracy of artificial neural networks was 92.59 percent and 90.85 percent for the ratio of the actual length to the critical length and the critical regeneration length, respectively.17 Although preliminary, the results of this investigation highlight the poten- tial role for machine learning in the analysis and development of tissue-engineering strategies for peripheral nerve repair. The biomechanics of the upper extremities are particularly complex, involving multivari- ate nonlinear relationships that are theoretically amenable to modeling by machine learning. In light of this, artificial neural networks have been used to develop automated controllers for a vari- ety of neuroprostheses, including those that are used to restore hand grasp and wrist control along with more proximal upper extremity func- tion in patients with C5/C6 spinal cord injury.18,19 Although the results of these initial investigations were mixed, they highlight the potential for arti- ficial neural networks, along with other machine learning techniques, in the development of neu- roprosthetic controllers for the hand and wrist. Along with the examples presented above, machine learning has the potential to provide additional innovation in hand and nerve surgery. For example, using information from previous cases and radiographic images of fractures, an algorithm could be developed to anticipate the positioning of Kirschner wires, plates, and screws in preoperative planning for hand surgery. Fur- thermore, using databases containing informa- tion derived from sensorial mapping following peripheral nerve repair, patterns of regrowth could be used to develop an algorithm capable of prognosticating the degree of sensory and motor restoration based on location, mechanism of nerve injury, and physical examination findings. Aesthetic Surgery Machine learning also has potential applica- tions in more subjective areas of plastic surgery, such as aesthetics. Using a form of supervised learning, an automated classifier for facial beauty was trained using extracted facial features from 165 images of attractive female faces that were also independently graded by human referees.20 Decision tree algorithms assess a set of descrip- tive attributes, which in this particular investiga- tion included different facial ratios, and attempt to determine attractive facial features most closely related to postoperative target variables— the human classification of facial beauty.20 When subjected to the testing set of images, the auto- mated classifier was shown to have a high accu- racy at approximating human referee scores.20 In light of these results, this application may serve as a predictive tool for estimating a patient’s per- ceived beauty following aesthetic surgery, provid- ing a quantitative measure to set expectations and possibly discourage patients from undergo- ing procedures that offer marginal improvement. In conjunction with optical head-mounted display technology, machine learning also has the potential to facilitate intraoperative visual- ization of surgical outcomes. One such applica- tion could be in reconstructive breast surgery, whereby machine learning software incorporated into a head mount display could predict what a breast might appear like in three-dimensional space based on potential changes in implant posi- tion. The system could be trained to identify fea- tures of breast aesthetics such as symmetry, nipple position, superior pole fullness, and degree of ptosis, optimizing aesthetic results while mini- mizing trauma and operating time. Although these potential tools may not replace the trained human eye, they would aid the plastic surgeon by providing a higher degree of objectivity to aes- thetic surgery.
  • 7. Copyright © 2016 American Society of Plastic Surgeons. Unauthorized reproduction of this article is prohibited. 896e Plastic and Reconstructive Surgery • May 2016 Resident Evaluation and Teaching Currently, competency in plastic surgery train- ing is assessed through written and oral exami- nations coupled with case logs and subjective evaluation by attending physicians and higher level residents.21 However, more objective methods for assessing “surgical competency” are required, which may be facilitated by machine learning. Recently, with the development of head-mounted cameras for resident teaching and wearable tech- nology, such as Google Glass (Google, Inc., Moun- tain View, Calif.), trainees are able record their cases and compare their performance to previous recordings of themselves and expert attending surgeons.21 By tracking various metrics, trainees can be graded on their surgical skills and realize methods for improvement. However, this tech- nology could be enhanced through the addition of sensors for precisely tracking eye movement, hand motion, and effective use of instruments and assistants. Machine learning could be used to identify salient aspects from the expert recordings that were absent or present in that of the train- ees, allowing for systematic and objective assess- ment of areas that need improvement in real time (Fig. 5). In addition, a set of objective milestones can be developed for systematic grading of surgi- cal competency. Postoperative follow-up is also of paramount importance for an outcomes-based specialty such as plastic surgery and is an aspect of surgi- cal residency training that needs improvement. By integrating intraoperative recording of the steps and techniques used during surgery with postoperative findings and images, machine learning could be used to identify the surgical techniques that lead to a particular outcome. This information would ultimately be linked to the resident involved in the procedure, ensuring appropriate long-term follow-up and facilitat- ing targeted feedback for future cases, some- thing that is difficult with the current pedagogic model, where residents rotate to different sites and services. LIMITATIONS Although the potential applications and ben- efits of machine learning for the field of plastic surgery are evident, to safely apply the findings obtained using this technology, clinicians must be aware of its limitations. Most importantly, machine learning has been criticized for exhibiting “black box” characteristics, with algorithms that pro- vide little or no justification for the outputs they provide.22 Furthermore, the learning behavior of some machines has been shown to be difficult to reproduce when similar training data are applied to different machine learning algorithms.23 To overcome these challenges, it has been sug- gested that future machine learning algorithms could be programmed to include justifications for their decisions.24 However, certain measures can be taken to demonstrate the diagnostic valid- ity of currently available algorithms. As demon- strated by many of the applications presented here, results obtained using machine learning can be compared to those derived from current gold standards for diagnosis. Another interesting approach is the emerging trend of “crowdsourc- ing analytics.”24 Through the use of multiple algo- rithms, or “crowdsourcing,” to address a specific problem, more accurate models of data can be obtained and simultaneously allow for the evalua- tion of the strengths and weaknesses of the differ- ent algorithms being used.24 Another concern is that some investigators may apply machine learning without the expertise to critically assess their results.23 To overcome this, investigators who wish to use machine learning to tackle complex problems should work closely with data scientists who are capable of accurately evaluating the validity of the outputs obtained.26 Ultimately, this would ensure that the results obtained using machine learning technology are interpreted correctly and are being applied in a safe and clinically relevant manner. Fig. 5. A mock recording of a resident carrying out a carpal tun- nel release using a wearable technological device.The recording has been optimized using unsupervised learning approaches to identify salient features from expert recordings that are either absent or present in that of the trainees, allowing for direction and correction in real time.
  • 8. Copyright © 2016 American Society of Plastic Surgeons. Unauthorized reproduction of this article is prohibited. Volume 137, Number 5 • Big Data and Machine Learning 897e CONCLUSIONS This introductory review of machine learning highlights the potential this technology has for catalyzing a paradigm shift in research and clini- cal practice in plastic surgery. Machine learning has already demonstrated great success in a vari- ety of fields, including several medical disciplines. In plastic surgery, we have demonstrated that machine learning has the potential to become a powerful tool, allowing surgeons to harness complex clinical data to help guide key clinical decision-making. Although a potentially power- ful tool, computer-generated algorithms will not replace the trained human eye. However, these are tools that may help us not only in the deci- sion-making process but also in finding patterns that might not be evident in analysis of smaller data sets or anecdotal experience. By embracing machine learning, modern plastic surgeons may be able to redefine the specialty while solidifying their role as leaders at the forefront of scientific advancement in surgery. Mirko Gilardino, M.D., M.Sc. Montreal Children’s Hospital 2300 Tupper Street, Room C-1135 Montreal, Quebec H3H 1P3, Canada mirkogilardino@hotmail.com Samuel Lin, M.D. Beth Israel Deaconess Medical Center 110 Francis Street, Suite 5A Boston, Mass. 02215 sjlin@bidmc.harvard.edu references 1. Murdoch TB, Detsky AS. The inevitable application of big data to health care. JAMA 2013;309:1351–1352. 2. Cleophas TJ, Zwinderman AH. Machine Learning in Medicine: Cookbook. New York: Springer; 2014. 3. Bose I, Mahapatra RK. Business data mining: A machine learning perspective. Inform Manage. 2001;39:211–225. 4. Malin JL. Envisioning Watson as a rapid-learning system for oncology. J Oncol Pract. 2013;9:155–157. 5. Oncologists partner with Watson on genomics. Cancer Discov. 2015;5:788. 6. 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