Browse through all publications from the Institute of Global Health Innovation, which our Patient Safety Research Collaboration is part of. This feed includes reports and research papers from our Centre. 

Citation

BibTex format

@article{Lam:2022:10.1038/s41746-022-00566-0,
author = {Lam, K and Chen, J and Wang, Z and Iqbal, F and Darzi, A and Lo, B and Purkayastha, S and Kinross, J},
doi = {10.1038/s41746-022-00566-0},
journal = {npj Digital Medicine},
title = {Machine learning for technical skill assessment in surgery: a systematic review},
url = {http://dx.doi.org/10.1038/s41746-022-00566-0},
volume = {5},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Accurate and objective performance assessment is essential for both trainees and certified surgeons. However, existing methods can be time consuming, labor intensive and subject to bias. Machine learning (ML) has the potential to provide rapid, automated and reproducible feedback without the need for expert reviewers. We aimed to systematically review the literature and determine the ML techniques used for technical surgical skill assessment and identify challenges and barriers in the field. A systematic literature search, in accordance with the PRISMA statement, was performed to identify studies detailing the use of ML for technical skill assessment in surgery. Of the 1896 studies that were retrieved, 66 studies were included. The most common ML methods used were Hidden Markov Models (HMM, 14/66), Support Vector Machines (SVM, 17/66) and Artificial Neural Networks (ANN, 17/66). 40/66 studies used kinematic data, 19/66 used video or image data, and 7/66 used both. Studies assessed performance of benchtop tasks (48/66), simulator tasks (10/66), and real-life surgery (8/66). Accuracy rates of over 80% were achieved, although tasks and participants varied between studies. Barriers to progress in the field included a focus on basic tasks, lack of standardization between studies, and lack of datasets. ML has the potential to produce accurate and objective surgical skill assessment through the use of methods including HMM, SVM, and ANN. Future ML-based assessment tools should move beyond the assessment ofbasic tasks and towards real-life surgery and provide interpretable feedback with clinical value for the surgeon.
AU  - Lam,K
AU  - Chen,J
AU  - Wang,Z
AU  - Iqbal,F
AU  - Darzi,A
AU  - Lo,B
AU  - Purkayastha,S
AU  - Kinross,J
DO  - 10.1038/s41746-022-00566-0
PY  - 2022///
SN  - 2398-6352
TI  - Machine learning for technical skill assessment in surgery: a systematic review
T2  - npj Digital Medicine
UR  - http://dx.doi.org/10.1038/s41746-022-00566-0
UR  - https://www.nature.com/articles/s41746-022-00566-0
UR  - http://hdl.handle.net/10044/1/94832
VL  - 5
ER  -
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