Surgical data science for next-generation interventions.
Lena Maier-Hein,Swaroop Vedula,Stefanie Speidel,Nassir Navab,Nassir Navab,Ron Kikinis,Ron Kikinis,Adrian Park,Matthias Eisenmann,Hubertus Feussner,Germain Forestier,Stamatia Giannarou,Makoto Hashizume,Darko Katic,Hannes Kenngott,Michael Kranzfelder,Anand Malpani,Keno März,Thomas Neumuth,Nicolas Padoy,Carla M. Pugh,Nicolai Schoch,Danail Stoyanov,Russell H. Taylor,Martin Wagner,Gregory D. Hager,Pierre Jannin,Pierre Jannin +27 more
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TLDR
Interventional healthcare will evolve from an artisanal craft based on the individual experiences, preferences and traditions of physicians into a discipline that relies on objective decision-making on the basis of large-scale data from heterogeneous sources.Abstract:
Interventional healthcare will evolve from an artisanal craft based on the individual experiences, preferences and traditions of physicians into a discipline that relies on objective decision-making on the basis of large-scale data from heterogeneous sources.read more
Citations
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Journal ArticleDOI
Artificial intelligence in surgery: A research team perspective.
Hossein Mohamadipanah,Calvin A. Perumalla,Su Yang,Brett Wise,LaDonna E. Kearse,Cassidi Goll,Anna Witt,James R. Korndorffer,Carla M. Pugh +8 more
Journal ArticleDOI
Quantification of Robotic Surgeries with Vision-Based Deep Learning
Dani Kiyasseh,Runzhuo Ma,Taseen F. Haque,Jessica H. Nguyen,Christian Wagner,Animashree Anandkumar,Andrew J. Hung +6 more
TL;DR: This work proposes a deep learning framework, entitled Roboformer, which operates exclusively on videos recorded during surgery to independently achieve multiple tasks: surgical phase recognition, gesture classification and skills assessment, and finds that this framework can generalize well to unseen videos, surgeons, medical centres, and surgical procedures.
Journal ArticleDOI
Comparative validation of machine learning algorithms for surgical workflow and skill analysis with the HeiChole benchmark
TL;DR: In the 2019 International Endoscopic Vision Challenge, a dataset with 33 laparoscopic cholecystectomy videos from three surgical centers with a total operation time of 22 h was created as discussed by the authors , where 12 research teams trained and submitted their machine learning algorithms for recognition of phase, action, instrument and/or skill assessment.
Journal ArticleDOI
Bounded Future MS-TCN++ for surgical gesture recognition
TL;DR: This study used an open surgery simulation data-set containing 96 videos of 24 participants that perform a suturing task on a variable tissue simulator to design an MS-TCN++-based algorithm that can utilize this performance-delay trade-off.
Book ChapterDOI
Robotic Automation for Surgery
Hossein Dehghani,Peter C.W. Kim +1 more
TL;DR: A new paradigm of surgical vision and intelligence driving automation and autonomy, initially at subtask and then task levels but eventually at systems level, is necessary and promises an enhanced adoption of the technology with measurable metrics on improved outcome, safety and accessibility.
References
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Gene Ontology: tool for the unification of biology
M Ashburner,Catherine A. Ball,Judith A. Blake,David Botstein,Heather Butler,J. M. Cherry,Allan Peter Davis,Kara Dolinski,Selina S. Dwight,J.T. Eppig,Midori A. Harris,David P. Hill,Laurie Issel-Tarver,Andrew Kasarskis,Suzanna E. Lewis,John C. Matese,Joel E. Richardson,M. Ringwald,Gerald M. Rubin,Gavin Sherlock +19 more
TL;DR: The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing.
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ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky,Jia Deng,Hao Su,Jonathan Krause,Sanjeev Satheesh,Sean Ma,Zhiheng Huang,Andrej Karpathy,Aditya Khosla,Michael S. Bernstein,Alexander C. Berg,Li Fei-Fei +11 more
TL;DR: The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) as mentioned in this paper is a benchmark in object category classification and detection on hundreds of object categories and millions of images, which has been run annually from 2010 to present, attracting participation from more than fifty institutions.
Journal ArticleDOI
Machine learning: Trends, perspectives, and prospects
TL;DR: The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing.
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Deep Learning in Medical Image Analysis
TL;DR: This review covers computer-assisted analysis of images in the field of medical imaging and introduces the fundamentals of deep learning methods and their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on.
Journal ArticleDOI
An estimation of the global volume of surgery: a modelling strategy based on available data
Thomas G. Weiser,Thomas G. Weiser,Scott E. Regenbogen,Katherine D. Thompson,Alex B. Haynes,Stuart R. Lipsitz,William R. Berry,Atul A. Gawande,Atul A. Gawande +8 more
TL;DR: In view of the high death and complication rates of major surgical procedures, surgical safety should now be a substantial global public-health concern.