Machine Learning for Surgical Phase Recognition: A Systematic Review. Issue 4 (April 2021)

• Record Type: Journal Article
• Authors: Garrow, Carly R.; Kowalewski, Karl-Friedrich; Li, Linhong; Wagner, Martin; Schmidt, Mona W.; Engelhardt, Sandy; Hashimoto, Daniel A.; Kenngott, Hannes G.; Bodenstedt, Sebastian; Speidel, Stefanie; Müller-Stich, Beat P.; Nickel, Felix
• Is Part Of: Annals of surgery. Volume 273:Issue 4(2021)
• Publication Date: 2021-04
• Subjects: artificial intelligence -- digital health -- general surgery -- machine learning -- minimally invasive surgery -- robotic surgery -- surgical phase recognition -- workflow recognition; Surgery -- Periodicals; 617.005
• DOI: 10.1097/SLA.0000000000004425
• Languages: English
• ISSNs: 0003-4932

Abstract

Abstract : Objective: To provide an overview of ML models and data streams utilized for automated surgical phase recognition. Background: Phase recognition identifies different steps and phases of an operation. ML is an evolving technology that allows analysis and interpretation of huge data sets. Automation of phase recognition based on data inputs is essential for optimization of workflow, surgical training, intraoperative assistance, patient safety, and efficiency. Methods: A systematic review was performed according to the Cochrane recommendations and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. PubMed, Web of Science, IEEExplore, GoogleScholar, and CiteSeerX were searched. Literature describing phase recognition based on ML models and the capture of intraoperative signals during general surgery procedures was included. Results: A total of 2254 titles/abstracts were screened, and 35 full-texts were included. Most commonly used ML models were Hidden Markov Models and Artificial Neural Networks with a trend towards higher complexity over time. Most frequently used data types were feature learning from surgical videos and manual annotation of instrument use. Laparoscopic cholecystectomy was used most commonly, often achieving accuracy rates over 90%, though there was no consistent standardization of defined phases. Conclusions: ML for surgical phase recognition can be performed with high accuracy, depending on the model, data type, and complexity of surgery. Different intraoperative data inputs such as video and instrument type can successfully be used. Most ML models still require significant amounts of manual expert annotations for training. The ML models may drive surgical workflow towards standardization, efficiency, and objectiveness to improve patient outcome in the future. Registration PROSPERO: CRD42018108907 Abstract : Supplemental Digital Content is available in the text