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Digital Future of the Surgery: Brining the Innovation of Digital Technology into Operating Room

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The 12th Asia-Pacitic Congress of Endoscopic and Laparoscopic Surgery (ELSA)

Published in: Health & Medicine

Digital Future of the Surgery: Brining the Innovation of Digital Technology into Operating Room

  1. 1. Sungkyunkwan University Department of Human ICT Convergence Yoon Sup Choi, Ph.D. Digital Future of the Surgery : how to bring the innovation of digital technology into the operating room
  2. 2. The Convergence of IT, BT and Medicine
  3. 3. Inevitable Tsunami of Change
  4. 4. Digital Future of the Surgery • Wearable Devices • Augmented Reality • Artificial Intelligence • 3D Printings
  5. 5. Wearable Devices
  6. 6. n n- ng n es h- n ne ne ct d n- at s- or e, ts n a- gs d ch Nat Biotech 2015
  7. 7. Google Glass How-to: Getting Started
  8. 8. • Technically fancy device, but skepticisms on usability • Most functions can be achievable with smartphones • Necessary to find specific use case, which cannot be done with smartphone
  9. 9. Killer Application
  10. 10. #ifihadglass project In February 2013, 1,000 Glass Explorers were selected as a beta testers.
  11. 11. Jun 16. 2014 3 out of 5 Glass Certified Partners by Glass at Work develop applications in medicine/healthcare
  12. 12. 1.Ambulance • checking medical histories from EMR • sharing data / communication with ER
  13. 13. 2. ER • uploading EMR data with dictation / video recording • consulting with specialists with sharing video in real time
  14. 14. 3. Examination Room • uploading EMR data with dictation / video recording • improve patients-doctor relationships
  15. 15. 4. Operating Room
  16. 16. The Connected Surgeons with Glass
  17. 17. The Connected Surgeons with Glass
  18. 18. The Connected Surgeons with Glass
  19. 19. The Connected Surgeons with Glass
  20. 20. • While performing surgery, he used Google Glass to compare patient’s CT scan. • Google Glass doesn’t distract, like driving a car and glancing in the rearview mirror. Dr. Pierre Theodore, a cardiothoracic surgeon at UCSF Medical Center August 2013 “It was extraordinarily helpful.”
  21. 21. • Consult with a distant colleague using live video from the OR via Google Glass • Live streamed to the laptop of the medical school students Dr. Dr. Christopher Kaeding, Ohio State University Wexner Medical Center August 2013 US doctor performs first live Google Glass surgery 
  22. 22.  Ohio State University Wexner Medical Center의 Dr. Christopher Ceding • Consult with a distant colleague using live video from the OR via Google Glass • Live streamed to the laptop of the medical school students August 2013 US doctor performs first live Google Glass surgery 
  23. 23. UC Irvine School of Medicine first to integrate Google Glass into curriculum 2014. 4. UC Irvine School of Medicine is taking steps to become the first in the nation to integrate the wearable computer into its four-year curriculum – from first- and second-year anatomy courses and clinical skills training to third- and fourth-year hospital rotations.
  24. 24. Google Glass enters operating room at Stanford 2014. 7. Stanford University Medical Center’s Department of Cardiothoracic Surgery has started using Google Glass in its resident training program. While a resident is operating on a patient, surgeons can use the CrowdOptic software to watch the resident’s progress and send visual feedback to the resident on technique.
  25. 25. Augmented Reality
  26. 26. Augmented Reality Augmented Reality is a technology enriching the real world with digital information and media, such as 3D models and videos, overlaying in real-time the camera view of your smartphone, tablet, PC or connected glasses.
  27. 27. Extreme future of AR? http://gencept.com/sight-an-8-minute-augmented-reality-journey-video
  28. 28. http://www.ircad.fr/fr/recherche/visible-patient/ Visible Patient
  29. 29. 3D modeling and visualization of anatomical or pathological structures in the medical image
  30. 30. 3D modeling and visualization of anatomical or pathological structures in the medical image
  31. 31. VR Render: 3D image reconstruction guidance in surgery https://www.youtube.com/watch?v=JJtiBA24Snc
  32. 32. • Surgical planning • Training • Share information with patient / other practitioners • Intraoperative guidance
  33. 33. https://www.youtube.com/watch?v=xedzYSAT8S4 Augmented Reality: superimposing the preoperative 3D patient modeling onto the real intraoperative view of the patient • Identify the location of metastasized tumors in the organs
  34. 34. https://www.youtube.com/watch?v=xedzYSAT8S4 Augmented Reality: superimposing the preoperative 3D patient modeling onto the real intraoperative view of the patient
  35. 35. VIPAAR provides real-time, two-way, interactive video conferencing
  36. 36. VIPAAR: Remote Surgery Support UsingVIPAAR, a remote surgeon is able to put his or her hands into the surgical field and provide collaboration and assistance.
  37. 37. VIPAAR: Connecting Experts
  38. 38. VIPAAR: Connecting Experts
  39. 39. https://www.youtube.com/watch?v=aTOoBwfqBe0 Virtual surgery withVIPAAR and Google Glass
  40. 40. Artificial Intelligence
  41. 41. Jeopardy! IBM Watson defeated two human champions in Jepoardy! in 2011
  42. 42. IBM Watson Oncology
  43. 43. 600,000 pieces of medical evidence 2 million pages of text from 42 medical journals and clinical trials 69 guidelines, 61,540 clinical trials IBM Watson on Medicine Watson learned... + 1,500 lung cancer cases physician notes, lab results and clinical research + 14,700 hours of hands-on training
  44. 44. • Treatment plans suggestions with confidence level • Evidences behind the suggestions: articles, best practices, guidelines • Suggestion of eligible clinical trials
  45. 45. IBM Watson in Korea? 2015.7.9. SNUH
  46. 46. DeepFace: Closing the Gap to Human-Level Performance in FaceVerification Taigman,Y. et al. (2014). DeepFace: Closing the Gap to Human-Level Performance in FaceVerification, CVPR’14. Figure 2. Outline of the DeepFace architecture. A front-end of a single convolution-pooling-convolution filtering on the rectified input, followed by three locally-connected layers and two fully-connected layers. Colors illustrate feature maps produced at each layer. The net includes more than 120 million parameters, where more than 95% come from the local and fully connected layers. very few parameters. These layers merely expand the input into a set of simple local features. The subsequent layers (L4, L5 and L6) are instead lo- cally connected [13, 16], like a convolutional layer they ap- ply a filter bank, but every location in the feature map learns a different set of filters. Since different regions of an aligned image have different local statistics, the spatial stationarity The goal of training is to maximize the probability of the correct class (face id). We achieve this by minimiz- ing the cross-entropy loss for each training sample. If k is the index of the true label for a given input, the loss is: L = log pk. The loss is minimized over the parameters by computing the gradient of L w.r.t. the parameters and by updating the parameters using stochastic gradient de- Human: 95% vs. DeepFace in Facebook: 97.35% Recognition Accuracy for Labeled Faces in the Wild (LFW) dataset (13,233 images, 5,749 people)
  47. 47. FaceNet:A Unified Embedding for Face Recognition and Clustering Schroff, F. et al. (2015). FaceNet:A Unified Embedding for Face Recognition and Clustering Human: 95% vs. FaceNet of Google: 99.63% Recognition Accuracy for Labeled Faces in the Wild (LFW) dataset (13,233 images, 5,749 people) False accept False reject s. This shows all pairs of images that were on LFW. Only eight of the 13 errors shown the other four are mislabeled in LFW. on Youtube Faces DB ge similarity of all pairs of the first one our face detector detects in each video. False accept False reject Figure 6. LFW errors. This shows all pairs of images that were incorrectly classified on LFW. Only eight of the 13 errors shown here are actual errors the other four are mislabeled in LFW. 5.7. Performance on Youtube Faces DB We use the average similarity of all pairs of the first one hundred frames that our face detector detects in each video. This gives us a classification accuracy of 95.12%±0.39. Using the first one thousand frames results in 95.18%. Compared to [17] 91.4% who also evaluate one hundred frames per video we reduce the error rate by almost half. DeepId2+ [15] achieved 93.2% and our method reduces this error by 30%, comparable to our improvement on LFW. 5.8. Face Clustering Our compact embedding lends itself to be used in order to cluster a users personal photos into groups of people with the same identity. The constraints in assignment imposed by clustering faces, compared to the pure verification task, lead to truly amazing results. Figure 7 shows one cluster in a users personal photo collection, generated using agglom- erative clustering. It is a clear showcase of the incredible invariance to occlusion, lighting, pose and even age. Figure 7. Face Clustering. Shown is an exemplar cluster for one user. All these images in the users personal photo collection were clustered together. 6. Summary We provide a method to directly learn an embedding into an Euclidean space for face verification. This sets it apart from other methods [15, 17] who use the CNN bottleneck layer, or require additional post-processing such as concate- nation of multiple models and PCA, as well as SVM clas- sification. Our end-to-end training both simplifies the setup and shows that directly optimizing a loss relevant to the task at hand improves performance. Another strength of our model is that it only requires False accept False reject Figure 6. LFW errors. This shows all pairs of images that were incorrectly classified on LFW. Only eight of the 13 errors shown here are actual errors the other four are mislabeled in LFW. 5.7. Performance on Youtube Faces DB We use the average similarity of all pairs of the first one hundred frames that our face detector detects in each video. This gives us a classification accuracy of 95.12%±0.39. Using the first one thousand frames results in 95.18%. Compared to [17] 91.4% who also evaluate one hundred frames per video we reduce the error rate by almost half. DeepId2+ [15] achieved 93.2% and our method reduces this error by 30%, comparable to our improvement on LFW. 5.8. Face Clustering Our compact embedding lends itself to be used in order to cluster a users personal photos into groups of people with the same identity. The constraints in assignment imposed by clustering faces, compared to the pure verification task, Figure 7. Face Clustering. Shown is an exemplar cluster for one user. All these images in the users personal photo collection were clustered together. 6. Summary We provide a method to directly learn an embedding into an Euclidean space for face verification. This sets it apart from other methods [15, 17] who use the CNN bottleneck layer, or require additional post-processing such as concate- nation of multiple models and PCA, as well as SVM clas-
  48. 48. Business Area Medical Image Analysis VUNOnet and our machine learning technology will help doctors and hospitals manage medical scans and images intelligently to make diagnosis faster and more accurately. Original Image Automatic Segmentation EmphysemaNormal ReticularOpacity Our system finds DILDs at the highest accuracy * DILDs: Diffuse Interstitial Lung Disease Digital Radiologist
  49. 49. Digital Radiologist Med Phys. 2013 May;40(5):051912. doi: 10.1118/1.4802214.
  50. 50. Constructing higher-level contextual/relational features: Relationships between epithelial nuclear neighbors Relationships between morphologically regular and irregular nuclei Relationships between epithelial and stromal objects Relationships between epithelial nuclei and cytoplasm Characteristics of stromal nuclei and stromal matrix Characteristics of epithelial nuclei and epithelial cytoplasm Building an epithelial/stromal classifier: Epithelial vs.stroma classifier Epithelial vs.stroma classifier B Basic image processing and feature construction: H&E image Image broken into superpixels Nuclei identified within each superpixel A Relationships of contiguous epithelial regions with underlying nuclear objects Learning an image-based model to predict survival Processed images from patients Processed images from patients C D onNovember17,2011stm.sciencemag.orgwnloadedfrom TMAs contain 0.6-mm-diameter cores (median of two cores per case) that represent only a small sample of the full tumor. We acquired data from two separate and independent cohorts: Nether- lands Cancer Institute (NKI; 248 patients) and Vancouver General Hospital (VGH; 328 patients). Unlike previous work in cancer morphom- etry (18–21), our image analysis pipeline was not limited to a predefined set of morphometric features selected by pathologists. Rather, C-Path measures an extensive, quantitative feature set from the breast cancer epithelium and the stro- ma (Fig. 1). Our image processing system first performed an automated, hierarchical scene seg- mentation that generated thousands of measure- ments, including both standard morphometric descriptors of image objects and higher-level contextual, relational, and global image features. The pipeline consisted of three stages (Fig. 1, A to C, and tables S8 and S9). First, we used a set of processing steps to separate the tissue from the background, partition the image into small regions of coherent appearance known as superpixels, find nuclei within the superpixels, and construct Constructing higher-level contextual/relational features: Relationships between epithelial nuclear neighbors Relationships between morphologically regular and irregular nuclei Relationships between epithelial and stromal objects Relationships between epithelial nuclei and cytoplasm Characteristics of stromal nuclei and stromal matrix Characteristics of epithelial nuclei and epithelial cytoplasm Epithelial vs.stroma classifier Epithelial vs.stroma classifier Relationships of contiguous epithelial regions with underlying nuclear objects Learning an image-based model to predict survival Processed images from patients alive at 5 years Processed images from patients deceased at 5 years L1-regularized logisticregression modelbuilding 5YS predictive model Unlabeled images Time P(survival) C D Identification of novel prognostically important morphologic features basic cellular morphologic properties (epithelial reg- ular nuclei = red; epithelial atypical nuclei = pale blue; epithelial cytoplasm = purple; stromal matrix = green; stromal round nuclei = dark green; stromal spindled nuclei = teal blue; unclassified regions = dark gray; spindled nuclei in unclassified regions = yellow; round nuclei in unclassified regions = gray; background = white). (Left panel) After the classification of each image object, a rich feature set is constructed. (D) Learning an image-based model to predict survival. Processed images from patients alive at 5 years after surgery and from patients deceased at 5 years after surgery were used to construct an image-based prog- nostic model. After construction of the model, it was applied to a test set of breast cancer images (not used in model building) to classify patients as high or low risk of death by 5 years. www.ScienceTranslationalMedicine.org 9 November 2011 Vol 3 Issue 108 108ra113 2 onNovember17,2011stm.sciencemag.orgDownloadedfrom Digital Pathologist Sci Transl Med. 2011 Nov 9;3(108):108ra113 A comprehensive analysis of automatically quantitated morphological features could identify characteristics of prognostic relevance and provide an accurate and reproducible means for assessing prognosis from microscopic image data.
  51. 51. Digital Pathologist Sci Transl Med. 2011 Nov 9;3(108):108ra113 Top stromal features associated with survival. primarily characterizing epithelial nuclear characteristics, such as size, color, and texture (21, 36). In contrast, after initial filtering of im- ages to ensure high-quality TMA images and training of the C-Path models using expert-derived image annotations (epithelium and stroma labels to build the epithelial-stromal classifier and survival time and survival status to build the prognostic model), our image analysis system is automated with no manual steps, which greatly in- creases its scalability. Additionally, in contrast to previous approaches, our system measures thousands of morphologic descriptors of diverse identification of prognostic features whose significance was not pre- viously recognized. Using our system, we built an image-based prognostic model on the NKI data set and showed that in this patient cohort the model was a strong predictor of survival and provided significant additional prognostic information to clinical, molecular, and pathological prog- nostic factors in a multivariate model. We also demonstrated that the image-based prognostic model, built using the NKI data set, is a strong prognostic factor on another, independent data set with very different SD of the ratio of the pixel intensity SD to the mean intensity for pixels within a ring of the center of epithelial nuclei A The sum of the number of unclassified objects SD of the maximum blue pixel value for atypical epithelial nuclei Maximum distance between atypical epithelial nuclei B C D Maximum value of the minimum green pixel intensity value in epithelial contiguous regions Minimum elliptic fit of epithelial contiguous regions SD of distance between epithelial cytoplasmic and nuclear objects Average border between epithelial cytoplasmic objects E F G H Fig. 5. Top epithelial features. The eight panels in the figure (A to H) each shows one of the top-ranking epithelial features from the bootstrap anal- ysis. Left panels, improved prognosis; right panels, worse prognosis. (A) SD of the (SD of intensity/mean intensity) for pixels within a ring of the center of epithelial nuclei. Left, relatively consistent nuclear intensity pattern (low score); right, great nuclear intensity diversity (high score). (B) Sum of the number of unclassified objects. Red, epithelial regions; green, stromal re- gions; no overlaid color, unclassified region. Left, few unclassified objects (low score); right, higher number of unclassified objects (high score). (C) SD of the maximum blue pixel value for atypical epithelial nuclei. Left, high score; right, low score. (D) Maximum distance between atypical epithe- lial nuclei. Left, high score; right, low score. (Insets) Red, atypical epithelial nuclei; black, typical epithelial nuclei. (E) Minimum elliptic fit of epithelial contiguous regions. Left, high score; right, low score. (F) SD of distance between epithelial cytoplasmic and nuclear objects. Left, high score; right, low score. (G) Average border between epithelial cytoplasmic objects. Left, high score; right, low score. (H) Maximum value of the minimum green pixel intensity value in epithelial contiguous regions. Left, low score indi- cating black pixels within epithelial region; right, higher score indicating presence of epithelial regions lacking black pixels. onNovember17,2011stm.sciencemag.orgDownloadedfrom and stromal matrix throughout the image, with thin cords of epithe- lial cells infiltrating through stroma across the image, so that each stromal matrix region borders a relatively constant proportion of ep- ithelial and stromal regions. The stromal feature with the second largest coefficient (Fig. 4B) was the sum of the minimum green in- tensity value of stromal-contiguous regions. This feature received a value of zero when stromal regions contained dark pixels (such as inflammatory nuclei). The feature received a positive value when stromal objects were devoid of dark pixels. This feature provided in- formation about the relationship between stromal cellular composi- tion and prognosis and suggested that the presence of inflammatory cells in the stroma is associated with poor prognosis, a finding con- sistent with previous observations (32). The third most significant stromal feature (Fig. 4C) was a measure of the relative border between spindled stromal nuclei to round stromal nuclei, with an increased rel- ative border of spindled stromal nuclei to round stromal nuclei asso- ciated with worse overall survival. Although the biological underpinning of this morphologic feature is currently not known, this analysis sug- gested that spatial relationships between different populations of stro- mal cell types are associated with breast cancer progression. Reproducibility of C-Path 5YS model predictions on samples with multiple TMA cores For the C-Path 5YS model (which was trained on the full NKI data set), we assessed the intrapatient agreement of model predictions when predictions were made separately on each image contributed by pa- tients in the VGH data set. For the 190 VGH patients who contributed two images with complete image data, the binary predictions (high or low risk) on the individual images agreed with each other for 69% (131 of 190) of the cases and agreed with the prediction on the aver- aged data for 84% (319 of 380) of the images. Using the continuous prediction score (which ranged from 0 to 100), the median of the ab- solute difference in prediction score among the patients with replicate images was 5%, and the Spearman correlation among replicates was 0.27 (P = 0.0002) (fig. S3). This degree of intrapatient agreement is only moderate, and these findings suggest significant intrapatient tumor heterogeneity, which is a cardinal feature of breast carcinomas (33–35). Qualitative visual inspection of images receiving discordant scores suggested that intrapatient variability in both the epithelial and the stromal components is likely to contribute to discordant scores for the individual images. These differences appeared to relate both to the proportions of the epithelium and stroma and to the appearance of the epithelium and stroma. Last, we sought to analyze whether sur- vival predictions were more accurate on the VGH cases that contributed multiple cores compared to the cases that contributed only a single core. This analysis showed that the C-Path 5YS model showed signif- icantly improved prognostic prediction accuracy on the VGH cases for which we had multiple images compared to the cases that con- tributed only a single image (Fig. 7). Together, these findings show a significant degree of intrapatient variability and indicate that increased tumor sampling is associated with improved model performance. DISCUSSION Heat map of stromal matrix objects mean abs.diff to neighbors H&E image separated into epithelial and stromal objects A B C Worse prognosis Improved prognosis Improved prognosis Improved prognosis Worse prognosis Worse prognosis Fig. 4. Top stromal features associated with survival. (A) Variability in ab- solute difference in intensity between stromal matrix regions and neigh- bors. Top panel, high score (24.1); bottom panel, low score (10.5). (Insets) Top panel, high score; bottom panel; low score. Right panels, stromal matrix objects colored blue (low), green (medium), or white (high) according to each object’s absolute difference in intensity to neighbors. (B) Presence R E S E A R C H A R T I C L E onNovember17,2011stm.sciencemag.orgDownloadedfrom Top epithelial features.The eight panels in the figure (A to H) each shows one of the top-ranking epithelial features from the bootstrap anal- ysis. Left panels, improved prognosis; right panels, worse prognosis.
  52. 52. GaussSurgical: Estimation of Blood Loss in Surgery with iPad Camera
  53. 53. 1. Surgical Sponge (Pixel App) FDA 510 (k) Clearance in 2012
  54. 54. 2. Suction Container (Triton App) FDA 510 (k) Clearance in March 2015
  55. 55. 3D Printers
  56. 56. Replicator
  57. 57. • 3D object is constructed by adding material in layers (usually sprayed) • Materials: rubber, plastics, paper, polyurethane, metals, and even cells 3D printers: Replicators in the real world
  58. 58. ‘Liberator’, the 3D Printed Gun
  59. 59. $25, 3-D printed handgun
  60. 60. Winsun: 3D Printed House
  61. 61. Winsun: 3D Printed House
  62. 62. 3D Printed Hearing Aid
  63. 63. http://www.telegraph.co.uk/technology/news/9066721/3D-printer-builds-new-jaw-bone-for-transplant.html • The artificial jaw was made from titanium powder, heated and built-up in layers in a 3D printer to create a working lower jaw which was then finished with a bioceramic coating. • The implant was fitted in an operation in the Netherlands in June 2011. 3D Printed Jaw
  64. 64. 3D Printed Jaw
  65. 65. Tracheobronchomalacia (TBM)
  66. 66. Bioresorbable Airway Splint Created with a Three-Dimensional Printer
  67. 67. Bioresorbable Airway Splint Created with a Three-Dimensional Printer N Engl J Med 2013; 368:2043-2045 • A custom-designed and custom-fabricated resorbable airway splint, which was manufactured from polycaprolactone with the use of a 3D printer • Our bellowed topology design provides resistance against collapse while simultaneously allowing flexion, extension, and expansion with growth.
  68. 68. N Engl J Med 2013; 368:2043-2045 One year after surgery, imaging and endoscopy showed a patent left mainstem bronchus
  69. 69. Morrison RJ et al. Sci Transl Med. 2015 Fig. 1. Computational image- based design of 3D-printed tracheo- bronchialsplints.(A)Stereolithography (.STL) representation (top) and virtual rendering (bottom) of the tracheo- bronchial splint demonstrating the bounded design parameters of the device. We used a fixed open angle of 90° to allow placement of the de- vice over the airway. Inner diameter, length, wall thickness, and number and spacing of suture holes were adjusted according to patient anato- my (Table 1) and can be adjusted on the submillimeter scale. Bellow height and periodicity (ribbing) can be adjusted to allow additional flexion of the device in the z axis. (B) Mecha- nismofactionofthetracheobronchial splint intreatingtracheobronchialcol- lapse in TBM. Solid arrows denote positive intrathoracic pressure gener- ated on expiration. Hollow arrow de- notes vector of tracheobronchial collapse. Dashed arrow denotes vector of opening wedge displace- ment of the tracheobronchial splint with airway growth. (C) Digital Imag- ingandCommunicationsinMedicine (DICOM) images of the patient’s CT scan were used to generate a 3D model of the patient’s airway via seg- mentation in Mimics. A centerline was fit within the affected segment of the airway, and measurements of airway hydraulic diameter (DH) and length were used as design param- eters to generate the device design. (D) Design parameters were input into MATLAB to generate an output as a series of 2D. TIFF image slices using Fourier series representation. Light and gray areas indicate struc- tural components; dark areas are voids. The top image demonstrates a device bellow, and the bottom image demonstrates suture holes in- corporated into the device design. The .TIFF images were imported into Mimics to generate an. STL of the final splint design. (E) Virtual assessment of fit of tracheobronchial splint over segmented primary airway model for all patients. (F) Final 3D-printed PCL tracheobronchial splint used to treat the left bronchus of patient 2. The splint incorporated a 90° spiral to the open angle of the device to accommodate concurrent use of a right bronchial splint and growth of the right bronchus. R E S E A R C H A R T I C L E Mitigation of tracheobronchomalacia with 3D-printed personalized medical devices in pediatric patients
  70. 70. DISCUSSION We report successful implantation of 3D-printed, patient-specific bio- resorbable airway splints for treatment of severe TBM. The personalized splints conformed to the patients’ individual geometries and expanded compression) (20). Thus, we defined our maximum compressive allow- ance as less than 50% deformation under a 20-N load. However, a sim- ilar degree of bending compliance was too low for the splint to be effective at maintaining airway patency. We expected that under a 20-N load, the splint should allow greater than 20% displacement in bending to accommodate flexion of the airway but less than 50% displacement (greater than which may interrupt airflow). Fig. 2. Pre- and postoperative imaging of patients. Black arrrows in all figures denote location of the malcic segment of the airway. White arrows designate the location/presence of the tracheo- bronchial splint. Asterisk denotes focal degradation of splint. All CT images are coronal minimum intensity projection (MinIP) reformatted images of the lung and airway on expiration. All MRI images are axial proton density turbospin echo MRI images of the chest. (A) Preoperative (top) and 1-month postoperative (upper middle) CT images of patient 1. Postoperative MRI (lower middle) demonstrated presence of splint around left bronchus in patient 1 at 12 months and focal fragmentation of splint due to degradation at 39 months (bottom). (B) Preoperative (top) and 1-month postoperative (upper mid- dle) CT images of patient 2. Postoperative MRI (lower middle) demonstrated presence of splints around the left and right bronchi in patient 2 at 1 month. Note that the patient had bilateral mainstem bronchomalacia and received a tracheobronchial splint on both the left and right mainstem bronchus. (C) Preoperative (top) and 1-month postoperative (bottom) CT images of patient 3. www.ScienceTranslationalMedicine.org 29 April 2015 Vol 7 Issue 285 285ra64 5 Morrison RJ et al. Sci Transl Med. 2015 Mitigation of tracheobronchomalacia with 3D-printed personalized medical devices in pediatric patients
  71. 71. pressure (table S2). Patient airway image–based computational design coupled with 3D printing allowed rapid production of these devices. The regulatory approval process and evaluation of patient candidacy needed 7 days. All devices were completed within this time frame. Design and MATERIALS AND METHODS Study design Our hypothesis was that an external splint could be designed to obtain Fig. 4. Mean airway caliber over time. Patient airway DH was measured over time after implantation of the 3D-printed bioresorbable material. Solid lines denote bronchi that received the tracheobronchial splint. Dashed lines are normal, contralateral bronchi for patients 1 and 3. All caliber measurements were made on expiratory-phase CT imaging using the centerline function of each isolated bronchus in Mimics. The centerline function measures DH every 0.1 to 1.0 mm along the entire segment of the isolated model. Measurements are represented as averages of all measurements along the length of the isolated affected bronchus model ± SD. Pre-op, preoperative. R E S E A R C H A R T I C L E Morrison RJ et al. Sci Transl Med. 2015 Mitigation of tracheobronchomalacia with 3D-printed personalized medical devices in pediatric patients
  72. 72. 3D Printed Skull • A 22-year-old female from the Netherlands • A chronic bone disorder, which has increased the thickness of her skull from 1.5 to 5cm causing reduced eyesight and severe headaches. • Top section of skull was removed and replaced with a 3D printed implant. March 2014
  73. 73. 3D Printed Skull • Since the operation, the patient has gained her sight back entirely, is symptom-free and back to work. March 2014
  74. 74. by prof. Hyung Jin Choi (SNU) 3D printers for the anatomy education You cannot physically touch the 3D simulated models
  75. 75. by prof. Hyung Jin Choi (SNU) 3D printers for the anatomy education
  76. 76. by prof. Hyung Jin Choi (SNU) 3D printers for anatomy education
  77. 77. Digital Future of the Surgery • Wearable Devices • Augmented Reality • Artificial Intelligence • 3D Printings
  78. 78. Feedback/Questions • Email: yoonsup.choi@gmail.com • Blog: http://www.yoonsupchoi.com • Facebook: Yoon Sup Choi

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