Computer Vision in the Operating Room: Opportunities and Caveats

Effectiveness of computer vision techniques has been demonstrated through a number of applications, both within and outside healthcare. The operating room environment specifically is a setting with rich data sources compatible with computational approaches and high potential for direct patient benefit. The aim of this review is to summarize major topics in computer vision for surgical domains. The major capabilities of computer vision are described as an aid to surgical teams to improve performance and contribute to enhanced patient safety. Literature was identified through leading experts in the fields of surgery, computational analysis and modeling in medicine, and computer vision in healthcare. The literature supports the application of computer vision principles to surgery. Potential applications within surgery include operating room vigilance, endoscopic vigilance, and individual and team-wide behavioral analysis. To advance the field, we recommend collecting and publishing carefully annotated datasets. Doing so will enable the surgery community to collectively define well-specified common objectives for automated systems, spur academic research, mobilize industry, and provide benchmarks with which we can track progress. Leveraging computer vision approaches through interdisciplinary collaboration and advanced approaches to data acquisition, modeling, interpretation, and integration promises a powerful impact on patient safety, public health, and financial costs.

[1]  Masaru Ishii,et al.  Objective Assessment of Surgical Technical Skill and Competency in the Operating Room. , 2017, Annual review of biomedical engineering.

[2]  Gwénolé Quellec,et al.  Monitoring tool usage in surgery videos using boosted convolutional and recurrent neural networks , 2018, Medical Image Anal..

[3]  R. Hofmann-Wellenhof,et al.  Association Between Surgical Skin Markings in Dermoscopic Images and Diagnostic Performance of a Deep Learning Convolutional Neural Network for Melanoma Recognition. , 2019, JAMA dermatology.

[4]  L. L. Di Stasi,et al.  Gaze-based Technology as a Tool for Surgical Skills Assessment and Training in Urology. , 2017, Urology.

[5]  Stephanie Guerlain,et al.  Assessing team performance in the operating room: development and use of a "black-box" recorder and other tools for the intraoperative environment. , 2005, Journal of the American College of Surgeons.

[6]  Henricus J C M Sterenborg,et al.  Hyperspectral imaging for tissue classification, a way toward smart laparoscopic colorectal surgery , 2019, Journal of biomedical optics.

[7]  Didier Mutter,et al.  Weakly supervised convolutional LSTM approach for tool tracking in laparoscopic videos , 2018, International Journal of Computer Assisted Radiology and Surgery.

[8]  Aleix M. Martínez,et al.  EmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Andru Putra Twinanda,et al.  EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos , 2016, IEEE Transactions on Medical Imaging.

[10]  Wojciech Matusik,et al.  Gaze360: Physically Unconstrained Gaze Estimation in the Wild , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[11]  Carey E. Priebe,et al.  An integrative framework for sensor-based measurement of teamwork in healthcare , 2015, J. Am. Medical Informatics Assoc..

[12]  Philippe Ravaud,et al.  Mapping of Crowdsourcing in Health: Systematic Review , 2018, Journal of medical Internet research.

[13]  Andrew Zisserman,et al.  Detect to Track and Track to Detect , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[14]  Ara Darzi,et al.  Reducing the Burden of Surgical Harm: A Systematic Review of the Interventions Used to Reduce Adverse Events in Surgery , 2014, Annals of surgery.

[15]  Sukhan Lee,et al.  Computer-Aided Mechanical Assembly Planning , 1991 .

[16]  Didier Mutter,et al.  Formalizing video documentation of the Critical View of Safety in laparoscopic cholecystectomy: a step towards artificial intelligence assistance to improve surgical safety , 2019, Surgical Endoscopy.

[17]  T. Grantcharov,et al.  Relationship between intraoperative non‐technical performance and technical events in bariatric surgery , 2018, The British journal of surgery.

[18]  Alexandre Hostettler,et al.  New intraoperative imaging technologies: Innovating the surgeon’s eye toward surgical precision , 2018, Journal of surgical oncology.

[19]  N. Padoy,et al.  OR black box and surgical control tower: Recording and streaming data and analytics to improve surgical care. , 2021, Journal of visceral surgery.

[20]  Ming Yang,et al.  DeepFace: Closing the Gap to Human-Level Performance in Face Verification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Yaser Sheikh,et al.  OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[23]  Angela B Smith,et al.  How Implementation Science in Surgery is Done. , 2019, JAMA surgery.

[24]  M Pringle,et al.  Does awareness of being video recorded affect doctors' consultation behaviour? , 1990, The British journal of general practice : the journal of the Royal College of General Practitioners.

[25]  Marco A. Zenati,et al.  Cognitive Engineering to Improve Patient Safety and Outcomes in Cardiothoracic Surgery. , 2020, Seminars in thoracic and cardiovascular surgery.

[26]  Li Fei-Fei,et al.  Towards Vision-Based Smart Hospitals: A System for Tracking and Monitoring Hand Hygiene Compliance , 2017, MLHC.

[27]  J. Väyrynen,et al.  An improved image analysis method for cell counting lends credibility to the prognostic significance of T cells in colorectal cancer , 2012, Virchows Archiv.

[28]  A. Gruen Development and Status of Image Matching in Photogrammetry , 2012 .

[29]  George S. Avrunin,et al.  Dissecting Cardiac Surgery , 2019, Annals of surgery.

[30]  Todd McNutt,et al.  Machine learning and modeling: Data, validation, communication challenges , 2018, Medical physics.

[31]  Yisong Yue,et al.  Coordinated Multi-Agent Imitation Learning , 2017, ICML.

[32]  Gaurav Yengera,et al.  Less is More: Surgical Phase Recognition with Less Annotations through Self-Supervised Pre-training of CNN-LSTM Networks , 2018, ArXiv.

[33]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[34]  Lora Cavuoto,et al.  The Loud Surgeon Behind the Console: Understanding Team Activities During Robot-Assisted Surgery. , 2016, Journal of surgical education.

[35]  Li Fei-Fei,et al.  Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos , 2015, International Journal of Computer Vision.

[36]  D. Elbourne,et al.  Systematic review of the Hawthorne effect: New concepts are needed to study research participation effects☆ , 2014, Journal of clinical epidemiology.

[37]  Mica R. Endsley,et al.  Toward a Theory of Situation Awareness in Dynamic Systems , 1995, Hum. Factors.

[38]  Patrick J. Grother,et al.  Face Recognition Vendor Test (FRVT) part 2 :: identification , 2019 .

[39]  Chris T. Kiranoudis,et al.  Automated skin lesion assessment using mobile technologies and cloud platforms , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[40]  A Darzi,et al.  Comparison of gaze behaviour of trainee and experienced surgeons during laparoscopic gastric bypass , 2018, The British journal of surgery.

[41]  Andru Putra Twinanda,et al.  Single- and Multi-Task Architectures for Surgical Workflow Challenge at M2CAI 2016 , 2016, ArXiv.

[42]  Luke Oakden-Rayner,et al.  Exploring large scale public medical image datasets , 2019, Academic radiology.

[43]  Fei-Fei Li,et al.  Illuminating the dark spaces of healthcare with ambient intelligence , 2020, Nature.

[44]  Mathias Unberath,et al.  CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer-Assisted Interventions , 2019, Proceedings of the IEEE.

[45]  Teodor P. Grantcharov,et al.  Using Data to Enhance Performance and Improve Quality and Safety in Surgery. , 2017, JAMA surgery.

[46]  T. Grantcharov,et al.  Characterising ‘near miss’ events in complex laparoscopic surgery through video analysis , 2015, BMJ Quality & Safety.

[47]  Parisa Rashidi,et al.  Artificial Intelligence and Surgical Decision-Making. , 2019, JAMA surgery.

[48]  D. Mollura,et al.  Segmentation and Image Analysis of Abnormal Lungs at CT: Current Approaches, Challenges, and Future Trends. , 2015, Radiographics : a review publication of the Radiological Society of North America, Inc.

[49]  Frank Rudzicz,et al.  Explainable Artificial Intelligence for Safe Intraoperative Decision Support. , 2019, JAMA surgery.

[50]  Marco A. Zenati,et al.  Augmented Cognition in the Operating Room , 2020 .

[51]  Pietro Perona,et al.  Benchmarking and Error Diagnosis in Multi-instance Pose Estimation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[52]  D. Armellino,et al.  A Randomized Trial of the Efficacy of Hand Disinfection for Prevention of Rhinovirus Infection , 2012, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[53]  Jakob E. Bardram,et al.  A context-aware patient safety system for the operating room , 2008, UbiComp.

[54]  David S. Melnick,et al.  International evaluation of an AI system for breast cancer screening , 2020, Nature.

[55]  Debdoot Sheet,et al.  Multitask Learning of Temporal Connectionism in Convolutional Networks using a Joint Distribution Loss Function to Simultaneously Identify Tools and Phase in Surgical Videos , 2019, ArXiv.

[56]  Sebastian Bodenstedt,et al.  Unsupervised Temporal Video Segmentation as an Auxiliary Task for Predicting the Remaining Surgery Duration , 2019, OR/MLCN@MICCAI.

[57]  Peter Jüni,et al.  First-year Analysis of the Operating Room Black Box Study. , 2018, Annals of surgery.

[58]  D. Hashimoto,et al.  Surgical procedural map scoring for decision-making in laparoscopic cholecystectomy. , 2019, American journal of surgery.

[59]  D. Faller,et al.  Medical hyperspectral imaging to facilitate residual tumor identification during surgery , 2007, Cancer biology & therapy.

[60]  Wojciech Matusik,et al.  Video face replacement , 2011, ACM Trans. Graph..

[61]  Ying Jin,et al.  Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic study. , 2019, The Lancet. Oncology.

[62]  Gregory D. Hager,et al.  Assessment of Automated Identification of Phases in Videos of Cataract Surgery Using Machine Learning and Deep Learning Techniques , 2019, JAMA network open.

[63]  J. Fernandez-Miranda,et al.  Artificial Intelligence and the Future of Surgical Robotics. , 2019, Annals of surgery.

[64]  David J. Anderson,et al.  Computational Neuroethology: A Call to Action , 2019, Neuron.

[65]  Jia Deng,et al.  Stacked Hourglass Networks for Human Pose Estimation , 2016, ECCV.

[66]  Pietro Perona,et al.  Merging Pose Estimates Across Space and Time , 2013, BMVC.

[67]  Fabiana Rodrigues Leta,et al.  Applications of computer vision techniques in the agriculture and food industry: a review , 2012, European Food Research and Technology.

[68]  Thomas J. Fuchs,et al.  Clinical-grade computational pathology using weakly supervised deep learning on whole slide images , 2019, Nature Medicine.

[69]  N. Paragios,et al.  Video-Based Surveillance Systems: Computer Vision and Distributed Processing , 2001 .

[70]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[71]  Steve W. J. Kozlowski,et al.  Team Dynamics : Using “ Big Data ” to Advance the Science of Team Effectiveness , 2014 .

[72]  Daan Lips,et al.  Comparison of Systematic Video Documentation With Narrative Operative Report in Colorectal Cancer Surgery. , 2019, JAMA surgery.

[73]  Danail Stoyanov,et al.  EasyLabels: weak labels for scene segmentation in laparoscopic videos , 2019, International Journal of Computer Assisted Radiology and Surgery.

[74]  Shaogang Gong,et al.  Group and Crowd Behavior for Computer Vision , 2017 .

[75]  Margaret Mitchell,et al.  VQA: Visual Question Answering , 2015, International Journal of Computer Vision.

[76]  Brian D. Ziebart,et al.  Intent Prediction and Trajectory Forecasting via Predictive Inverse Linear-Quadratic Regulation , 2015, AAAI.

[77]  Andru Putra Twinanda,et al.  Data-driven spatio-temporal RGBD feature encoding for action recognition in operating rooms , 2015, International Journal of Computer Assisted Radiology and Surgery.

[78]  Eduardo Salas,et al.  Patient Safety in the Cardiac Operating Room: Human Factors and Teamwork A Scientific Statement From the American Heart Association , 2013, Circulation.

[79]  Taraprasad Das,et al.  Guidelines for Safe Surgery , 2018 .

[80]  Guillermo Sapiro,et al.  Computer vision and behavioral phenotyping: an autism case study , 2019, Current Opinion in Biomedical Engineering.

[81]  Joachim M. Buhmann,et al.  Computational Pathology: Challenges and Promises for Tissue Analysis , 2015, Comput. Medical Imaging Graph..

[82]  Li Fei-Fei,et al.  Bedside Computer Vision - Moving Artificial Intelligence from Driver Assistance to Patient Safety. , 2018, The New England journal of medicine.

[83]  Jonathan Krause,et al.  Tool Detection and Operative Skill Assessment in Surgical Videos Using Region-Based Convolutional Neural Networks , 2018, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV).

[84]  Andreas Holzinger,et al.  Legal, regulatory, and ethical frameworks for development of standards in artificial intelligence (AI) and autonomous robotic surgery , 2019, The international journal of medical robotics + computer assisted surgery : MRCAS.

[85]  Wei Luo,et al.  Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View , 2016, Journal of medical Internet research.

[86]  Shiliang Zhang,et al.  Pose-Driven Deep Convolutional Model for Person Re-identification , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[87]  Alexander Langerman,et al.  Video recording of the operating room--is anonymity possible? , 2015, The Journal of surgical research.

[88]  S. Guerlain,et al.  A systems approach to surgical safety , 2002, Surgical Endoscopy And Other Interventional Techniques.

[89]  Carsten Rother,et al.  Panoptic Segmentation , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[90]  Douglas A Wiegmann,et al.  A Statewide Surgical Coaching Program Provides Opportunity for Continuous Professional Development , 2017, Annals of surgery.

[91]  B. G. Batchelor,et al.  Machine vision for the food industry , 1993 .

[92]  David J. Anderson,et al.  Toward a Science of Computational Ethology , 2014, Neuron.

[93]  Pietro Perona,et al.  Learning recurrent representations for hierarchical behavior modeling , 2016, ICLR.

[94]  Atsushi Nara,et al.  Surgical Phase Recognition using Movement Data from Video Imagery and Location Sensor Data , 2017 .

[95]  N. Spinrad Google car takes the test , 2014, Nature.

[96]  Alex Sutherland,et al.  Wearing body cameras increases assaults against officers and does not reduce police use of force: Results from a global multi-site experiment , 2016 .

[97]  Nicolas Padoy,et al.  Machine and deep learning for workflow recognition during surgery , 2019, Minimally invasive therapy & allied technologies : MITAT : official journal of the Society for Minimally Invasive Therapy.

[98]  Hayato Itoh,et al.  Artificial Intelligence-Assisted Polyp Detection for Colonoscopy: Initial Experience. , 2018, Gastroenterology.