The document contains summaries of multiple research papers related to spine and scoliosis diagnosis and classification using machine learning and deep learning techniques. The papers propose and evaluate different algorithms using metrics like accuracy, sensitivity and F1-score. Methodologies involved include convolutional neural networks, random forest, SVM, deep learning models like U-Net etc. applied to X-ray, MRI and CT image datasets. The papers demonstrate high performance of these techniques for tasks like vertebrae segmentation, curvature measurement, deformity detection, and intervertebral disc classification, with most achieving accuracy above 85%. Limitations and scope for future work are also discussed.

1. AUTHOR &YEAR TOPIC OBJECTIVE METHODOLOGY METRICS USED REMARKS
Peiji Chen , Zhangnan
Zhou,Haixia Yu,2 Kun
Chen , and Yun Yang
(2022)
Computerized-Assisted
Scoliosis Diagnosis
Based on Faster RCNN
and ResNet for the
Classification of Spine
X-Ray
The paper proposes a
new algorithm that uses
Faster R-CNN and
ResNet to locate and
classify scoliosis
diseases from X-ray
images, without manual
intervention.
R-CNN and ResNet X-ray data The paper compares the proposed
algorithm with other machine learning
methods that use texture features and
SVM, and shows that the proposed
algorithm has higher precision,
sensitivity, and specificity.
Rizki Tri Prasetio
and Dwiza Riana
(2015)
A Comparison of
Classification Methods
in Vertebral Column
Disorder with the
Application of Genetic
Algorithm and Bagging
The paper compares
three classification
methods (naïve bayes,
neural networks, and k-
nearest neighbour) for
detecting spine
abnormalities using MRI
images. The paper also
proposes a combination
of genetic algorithm and
bagging technique to
improve the accuracy
and handle the class
imbalance problem.
Genetic algorithm,
bagging techniques.
Vertebral column
dataset from the
UCI machine
learning
repository.
The paper also shows that k-nearest
neighbour has the best accuracy among
the three classifiers after applying
genetic algorithm and bagging
technique.
The paper concludes that genetic
algorithm and bagging technique are
effective for solving the class
imbalance problem and improving the
classification of vertebral column
disorders32.

2. AUTHOR &YEAR TOPIC OBJECTIVE METHODOLOGY METRICS USED REMARKS
Sinta Kusuma
Wardani , Riyanto
Sigit ,
Setiawardhana,
Seffiana Manik Syah
Putri , DindaAyu
Yunitasari(2018)
Measurement of
Spinal Curvature for
Scoliosis
Classification
The paper presents a
system that uses image
processing and artificial
neural network to
segment and classify
spinal x-ray images of
scoliosis patients based
on the degree of
curvature.
Artificial neural
network. Watershed
method
X-ray data The system achieves 99.74%
accuracy in segmentation and
curvature calculation. The system can
facilitate the diagnosis and treatment
of scoliosis patients.
Marcelo da Silva
Barreiro, Marcello
H. Nogueira-
Barbosa, Rangaraj
M. Rangayyan,
Rafael de Menezes
Reis1 , Lucas
Calabrez Pereyra1 ,
Paulo M. Azevedo-
Marques(2014)
semiautomatic
classification of
intervertebral disc
degeneration in
magnetic resonance
images of the spine
Develop a quantitative
method for computer-
aided diagnosis (CAD)
of intervertebral disc
degeneration according
to Pfirrmann’s scale, a
semiquantitative scale
with five degrees of
degeneration, in T2-
weighted magnetic
resonance images of the
lumbar spine.
The intervertebral
discs were assigned
Pfirrmann’s grades
based on independent
and blind
classification.
Image of 210 Discs. The study indicates the feasibility of
the proposed approach for
semiautomatic classification of disc
degeneration in T2-weighted MR
images.
The results clearly indicate that
dimensionality reduction has had a
positive effect on the classification
results.
The limitations of the study are the
relatively small dataset, nonuniform
distribution of cases among the levels
of disc degeneration, and the absence
of level V cases in the dataset used.

3. AUTHOR &YEAR TOPIC OBJECTIVE METHODOLOGY METRICS USED REMARKS
Pratik Shrestha,
Aachal Singh, Riya
Garg, Ishika Sarraf,
Mahesh T R, Sindhu
Madhuri G(2021)
Early Stage
Detection of
Scoliosis Using
Machine Learning
Algorithms
The paper presents a
five-stage method that
involves input image,
pre-processing, training
and testing,
classification, and
performance metrics.
And also compares the
accuracy and elapsed
time of linear regression
and SVM algorithms.
Linear regression and
SVM algorithm
X-ray Image SVM achieves better results with
85.67% accuracy and 15.438
seconds elapsed time.
The paper concludes that SVM is the
best approach for detecting scoliosis
automatically and provides benefits
such as less time, less cost, and early
stage predictive analysis.
Md. Shariful Islam,
Md. Asaduzzaman,
Mohammad Masudur
Rahman(2019)
Feature Selection and
Classification of
Spinal Abnormalities
to Detect Low Back
Pain Disorder using
Machine Learning
Approaches
The paper uses a setwise
evolutionary based
wrapper paradigm to
identify the most
influential features and
discard the less relevant
ones. It combines
multiple feature selection
algorithms such as LVQ,
RFE, and UST.
Algorithms such as
LVQ, random forest
and UST
310 patient data with
12 features
It finds that Random Forest classifier
achieves the best accuracy of 94%
with feature selection.
The paper also analyses the
contribution of each feature towards
the abnormality of the spine.

4. AUTHOR &YEAR TOPIC OBJECTIVE METHODOLOGY METRICS USED REMARKS
Jiabo He, Wei Liu, Yu
Wang , Xingjun Ma ,
Xian-Sheng Hua
SpineOne: A One-
Stage Detection
Framework for
Degenerative Discs
and Vertebrae
The paper introduces
three key techniques to
improve the detection
performance.
One-channel-per-
class(OCPC)
550 MRI The paper claims that the proposed
method is more efficient and accurate
than two-stage methods, and that the
novel techniques are generic and can
be applied to other medical diagnosis
tasks.
Zhenda Xu, Jiahao
Hu, Qiang Gao,
Donghua Hang,
Qihua Zhou, Song
Guo, Aiqian Gan
Development of
Deep Learning
Algorithms for
Automated Scoliosis
and Abnormal
Posture Screening
Using 2D Back
Image
The system aims to
overcome the limitations
of conventional
screening methods and
provide a cost-free, fast,
accurate, and radiation-
free solution.
Deep learning X-ray image age
between 6-24
The paper reports that the system
achieves an overall classification
accuracy of 88.1% on the test set and
can correctly detect mild scoliosis and
categorize postural abnormalities.

5. AUTHOR &YEAR TOPIC OBJECTIVE METHODOLOGY METRICS USED REMARKS
Audrey Ha, John
Vorhies, Andrew
Campion, Charles
Fang, Michael Fadell
II, Steve Dou,
Safwan Halabi,
David Larson, Emily
Wang, YongJin Lee,
Joanna Langner,
Japsimran Kaur, Bao
Do(2020)
Automatic Extraction
of Skeletal Maturity
from Whole Body
Pediatric Scoliosis X-
rays Using Regional
Proposal and
Compound Scaling
Convolutional Neural
Networks
In this paper, to detect
and classify multiple
skeletal maturity
indicators from scoliosis
x-rays, such as the
humeral head and the
modified Oxford Bone
Score regions.
CNN and Machine
learning
X-ray images In this paper the system achieved an
F1-score of 0.99 for regional detection,
an overall accuracy of 89% and an
intraclass correlation coefficient of
0.84 for staging models, and a
processing time of less than 45
seconds per study.
Anjany Sekuboyina
(2021)
VerSe: A Vertebrae
labelling and
segmentation
benchmark for multi-
detector CT images
A total of 25 algorithms
were benchmarked on
these datasets. In this
work, they present the
results of this evaluation
and further investigate
the performance
variation at the vertebra
level, scan level, and
different fields of view.
U-Net and Deep
learning
374 CT scan The authors summarise the main
contributions and findings of the VerSe
challenges, which are the largest spine
dataset to date, the evaluation and
benchmarking of 25 algorithms for
vertebral labelling and segmentation,
and the in-depth analysis of the
algorithms’ behaviour in terms of
spine region, fields of view, and
manual effort.

6. AUTHOR &YEAR TOPIC OBJECTIVE METHODOLOGY METRICS USED REMARKS
Shu Liao, Yiqiang
Zhan, Zhongxing
Dong, Ruyi Yan,
Liyan Gong, Xiang
Sean Zhou, Marcos
Salganicoff and Jun
Fei (2015)
Automatic Lumbar
Spondylolisthesis
Measurement in CT
Images
The paper uses a
hierarchical learning
approach to detect and
label each lumbar
vertebrae and inter-
vertebral disc in CT
images.
Hierarchical learning 258 CT Scan The paper evaluates the proposed
framework on 258 CT
spondylolisthesis patients, and shows
that the measurement derived by the
method is very similar to the manual
measurement by radiologists and
significantly increases the
measurement efficiency.
Fatih Varçın, Hasan
Erbay, Eyüp Çetin
Diagnosis of Lumbar
Spondylolisthesis via
Convolutional Neural
Networks
In this paper, solution to
the problem of diagnosis
of spondylolisthesis by
using two well known
artificial neural networks
AlexNet and GoogleLen.
CNN and transfer
learning
272 x-ray The paper evaluates the performance
of the models using metrics such as
accuracy, sensitivity, specificity, and
F1 score3. The results show that
GoogLeNet performs slightly better
than AlexNet on all metrics45. The
paper concludes that the proposed
method is an encouraging start for
diagnosing lumbar pathologies.

7. AUTHOR &YEAR TOPIC OBJECTIVE METHODOLOGY METRICS USED REMARKS
Deepika Saravagi ,
Shweta Agrawal,
Manisha
Saravagi,Jyotir Moy
Chatterjee ,and Mohit
Agarwal
Diagnosis of Lumbar
Spondylolisthesis
Using Optimized
Pretrained CNN
Models
CNN model, VGG16
model prepared.
Deep learning model 299 x-ray Data augmentation is used to increase
the sample size. VGG16 model has
achieved 98% accuracy rate.
Models may be used as a substitute for
manual radiological analysis and can
help clinicians to diagnose
spondylolisthesis from spine X-ray
data automatically, further study is
needed for grading spondylolisthesis
through X-ray images.
Syed furqan qadri,
linlin shen, mubashir
ahmad, salman qadri ,
syeda shamaila
zareen5 , and salabat
khan (2021)
OP-convNet: A Patch
Classification-Based
Framework for CT
Vertebrae
Segmentation
The paper proposes an
OP-convNet model that
divides the CT image
slices into equal-sized
square overlapping
patches and applies a
random under-sampling
function(RUS-Function)
for class balancing.
Deep learning and
CNN
CT scan The model outputs a binary label for
each patch, indicating whether it
belongs to the vertebrae or the
background. The paper also describes
the preprocessing, data augmentation,
and post-processing steps of the
proposed method.
OP-convNet has precision(PRE) of
90.1%,specificity (SPE) of 99.4%,
accuracy (ACC) of 98.8%, F-score of
90.1% in terms of the patch-based
classification accuracy, and BF-score
of 90.2%, sensitivity (SEN) of 90.3%,
Jaccard index (JAC) of 82.3%.

8. AUTHOR
&YEAR
TOPIC OBJECTIVE METHODOLOG
Y
METRICS USED REMARKS
SK. Hasane
Ahammad,
V.Rajesh, Md.zia
Ur Rahman
(2020)
A Hybrid CNN
Based
Segmentation And
Boosting Classifier
For Real Time
Sensor Spinal Cord
injury Data
The proposed model is
a novel CNN-deep
segmentation based
boosting classifier that
uses a real-time
wearable sensor to
capture the SCI data
and performs
segmentation and
classification using a
hybrid CNN-SVM and
CNN-RF approach.
Deep Learning,
Embedded sensor,
random Forest,
SVM
Spinal Cord Injury
Data
This method deep learning
framework optimizes nearly 10%
improvement on the classification
rate and segmentation quality
compared to other models.
Experimental results proved that the
present model has better
performance than the existing spinal
cord injury detection models in
terms of true positive rate;
TP=0.9859, Accuracy=0.9894, and
Error rate=0.019 are concerned.
.
Ala s. al-kafri , Sud
sudirman , Abir
hussain , dhiya al-
jumeily , friska
natalia , hira
meidia , nunik
afriliana , wasfi al-
rashdan ,
mohammad
bashtawi, and
mohammed al-
jumaily (2018)
Boundary
Delineation of MRI
Images for Lumbar
Spinal Stenosis
Detection Through
Semantic
Segmentation Using
Deep Neural
Networks
In this paper, lumbar
spinal stenosis
detection through
semantic segmentation
and delineation of
magnetic resonance
imaging (MRI) scans
of the lumbar spine
using deep learning.
Deep learning. MRI scan with
symptomatic back
pain patients data
set used.
The model’s performance is within
the range of manual labelling
performance .
The ground-truth dataset has an
excellent inter-rater agreement score.
The mean accuracy in segmentation
is consistently lower in the
unregistered class.

9. AUTHOR &YEAR TOPIC OBJECTIVE METHODOLOGY METRICS USED REMARKS
Malaika Mushtaq ,
Muhammad Usman
Akram , Norah Saleh
Alghamdi , Joddat
Fatima ,and Rao Farhat
Masood (2022)
Localization and
Edge-Based
Segmentation of
Lumbar Spine
Vertebrae to
Identify the
Deformities Using
Deep Learning
Models
In this paper, the
localization and
segmentation of
the lumbar spine,
which aid in the
analysis of
lumbar spine
abnormalities.
Deep learning; Sensors localization; lumbar
lordortic angle;
lumbosacral angle;
lumbar spine; edge-
based segmentation
They have high computational
complexity.
This work can be extended to diagnose
cervical, thoracic spine, and pelvic
region deformities. Other may be used
to investigate and develop a fully
automated machine learning toolkit for
spinal deformities to prevent invasive
surgery methods.
Dong-sik Chaea , Thong
Phi Nguyenb, Sung-Jun
Parkc , Kyung-Yil Kang
d, Chanhee Wonb ,
Jonghun Yoone, (2020)
Decentralized
convolutional
neural network for
evaluating spinal
deformity with
spinopelvic
parameters
This paper
presents an
automated
method for
precisely
measuring
spinopelvic
parameters using
a decentralized
convolutional
neural network as
an efficient
replacement for
current manual
process.
Artificial intelligent,
Orthopaedic,
Convolutional neural
network
According to key
points obtained,
parameters
representing the
spinal deformity are
calculated, which
consistency with
manual measurement
was validated by 40
test cases.
The improvement in accuracy caused
by an increase in number of orders was
also verified by comparing the results
in cases of 3 and 4 orders, which
suggests directions for application in
multiple fields requiring precise
measurement using a limited dataset.
They have also an serror detection.

10. AUTHOR &YEAR TOPIC OBJECTIVE METHODOLO
GY
METRICS
USED
REMARKS
SK. HASANE
AHAMMAD, V.
RAJESH, AND
MD. ZIA UR
RAHMAN
(2019)
Fast and Accurate
Feature Extraction-
Based Segmentation
Framework for
Spinal Cord Injury
Severity
Classification
In this paper a
novel segment-
based classification
model which
determine the
extent of the
damage and
forecast the illness
patterns on the
excessively
segmented regions
and features.
Machine learning,
spinal cord image,
support vector
machine,
segmentation.
MRI image. In the future work, a novel multi-
level segmentation-based
classification approach will be
implemented on the gender wise
spinal cord images to improve the
error rate and accuracy.
Also, the noises in the T1-weighted
and T2-weighted regions are
optimized in order to improve the
classification accuracy in the older
age SCI images.
Faisal rehman ,
Syed irtiza ali shah ,
Naveed riaz , and
Syed omer gilani
(2019)
A Robust Scheme of
Vertebrae
Segmentation for
Medical Diagnosis
In this paper, they
proposed a novel
and efficient
framework to
address the subject
problem by
integrating a
parametric level set
approach in deep
convolutional
neural networks.
Deep Neural
network
MRI image and
CT scan image
Segmentation performance degrades
with substantial topological shape
variability.
In future, they can extend this work
to multimodality images or datasets
from different scanners in order to
build a robust system.
And also developed a simultaneous
segmentation scheme that can
perform cervical, thoracic and
lumbar vertebrae segmentation over
a single platform.

11. AUTHOR &YEAR TOPIC OBJECTIVE METHODOLOGY METRICS USED REMARKS
Jiajun Zhanga,b , Ka-
yee Cheuka,b , Leilei
Xub,c , Yujia Wanga,b ,
Zhenhua Fengb,c , Tony
Sitd , Ka-lo Chenga,b ,
Evguenia
Nepotchatykhe , Tsz-
ping Lama,b , Zhen
Liub,c , Alec L.H.
Hunga,b , Zezhang
Zhub,c , Alain
Moreaue,f,g , Jack C.Y.
Chenga,b, Yong Qiub,c,
Wayne Y.W.
Leea,b,(2020)
A validated
composite model
to predict risk of
curve progression
in adolescent
idiopathic
scoliosis
In this paper, create a
composite model for
prediction, patients
with AIS were
tracked for a
minimum of six
years.
Scoliosis Adolescent
Clinical study
In this paper, a two
phase study with an
exploration group of
120 Adolescent
idiopathic
scoliosis(AIS) and a
validation cohort.
In this paper, they provide very less
information for clinical decision making.
Ben Glocker, Darko
Zikic, Ender
Konukoglu, David R.
Haynor, and Antonio
Criminisi
Vertebrae
Localization in
Pathological Spine
CT via Dense
Classification
from Sparse
Annotations
In this paper they
proposed a robust
localization and
identification
algorithm which
builds upon
supervised
classification forests
and avoids an
explicit parametric
model of appearance.
Supervised
classification forests
CT scans image, MRI
image
In this paper, they improve the centroid
estimation.

12. AUTHOR &YEAR TOPIC OBJECTIVE METHODOLOGY METRICS USED REMARKS
Ben Glocker,J. Feulner,
Antonio Criminisi1, D.R.
Haynor, and E. Konukoglu
Automatic
Localization and
Identification of
Vertebrae in
Arbitrary Field-of-
View CT Scan
In this paper they have
presented a new method
for automatic
localization and
identification of
vertebrae in arbitrary
field-of-view CT scans.
Regression forests and
probabilistic graphical
model
CT scan In this paper, they will
be increase the amount
of training data, in
particular, for the
cervical region
Bizhan Aarabi,Chen
Chixiang, J. Marc
Simard,Timothy
Chryssikos, Jesse A.
Stokum, Charles A. Sansur,
Kenneth M. Crandall,Joshua
Olexa,Jeffrey Oliver,
Melissa R. Meister,Gregory
Cannarsa, Ashish Sharma,
Cara Lomangino, Maureen
Scarboro,Abdul-Kareem
Ahmed, Nathan
Han,Riccardo Serra, Phelan
Shea, Carla Aresco, and
Gary T. Schwartzbauer,
Proposal of a
Management
Algorithm to Predict
the Need for
Expansion
Duraplasty in
American Spinal
Injury Association
Impairment Scale
Grades A–C
Traumatic Cervical
Spinal Cord Injury
Patients
In this paper to identify
patient for expansion
duraplasty, based on the
absence of cerebrospinal
fluid (CSF) interface
around the spinal cord
on magnetic resonance
imaging (MRI), in the
setting of otherwise
adequate bony
decompression.
Decompression,
duraplasty,
neuroprotection
MRI scan In this paper, they have
implement the CNN for such
a TSCI data set.