Article intro - ML for Surgical Phase Recognition
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."
The article also presents an "Overview of the phase recognition process. Annotated training data is used to train the machine learning algorithm, and the algorithm creates a model based on the data. Once the model is developed, it can be used to recognize the phases of unlabeled test data. This phase recognition may act as a cornerstone for a variety of tasks in the future. ANN indicates artificial neural network; DTW, dynamic time warping; HMM, hidden Markov model; RF, random forest; SVM, support vector machine."
Source: Annals of Surgery
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