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Evaluation of surgical skill using machine learning with optimal wearable sensor locations

Evaluation of surgical skills during minimally invasive surgeries is needed when recruiting new surgeons. Although surgeons’ differentiation by skill level is highly complex, performance in specific clinical tasks such as pegboard transfer and knot tying could be determined using wearable EMG and ac...

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Autores principales: Soangra, Rahul, Sivakumar, R., Anirudh, E. R., Reddy Y., Sai Viswanth, John, Emmanuel B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9165861/
https://www.ncbi.nlm.nih.gov/pubmed/35657912
http://dx.doi.org/10.1371/journal.pone.0267936
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author Soangra, Rahul
Sivakumar, R.
Anirudh, E. R.
Reddy Y., Sai Viswanth
John, Emmanuel B.
author_facet Soangra, Rahul
Sivakumar, R.
Anirudh, E. R.
Reddy Y., Sai Viswanth
John, Emmanuel B.
author_sort Soangra, Rahul
collection PubMed
description Evaluation of surgical skills during minimally invasive surgeries is needed when recruiting new surgeons. Although surgeons’ differentiation by skill level is highly complex, performance in specific clinical tasks such as pegboard transfer and knot tying could be determined using wearable EMG and accelerometer sensors. A wireless wearable platform has made it feasible to collect movement and muscle activation signals for quick skill evaluation during surgical tasks. However, it is challenging since the placement of multiple wireless wearable sensors may interfere with their performance in the assessment. This study utilizes machine learning techniques to identify optimal muscles and features critical for accurate skill evaluation. This study enrolled a total of twenty-six surgeons of different skill levels: novice (n = 11), intermediaries (n = 12), and experts (n = 3). Twelve wireless wearable sensors consisting of surface EMGs and accelerometers were placed bilaterally on bicep brachii, tricep brachii, anterior deltoid, flexor carpi ulnaris (FCU), extensor carpi ulnaris (ECU), and thenar eminence (TE) muscles to assess muscle activations and movement variability profiles. We found features related to movement complexity such as approximate entropy, sample entropy, and multiscale entropy played a critical role in skill level identification. We found that skill level was classified with highest accuracy by i) ECU for Random Forest Classifier (RFC), ii) deltoid for Support Vector Machines (SVM) and iii) biceps for Naïve Bayes Classifier with classification accuracies 61%, 57% and 47%. We found RFC classifier performed best with highest classification accuracy when muscles are combined i) ECU and deltoid (58%), ii) ECU and biceps (53%), and iii) ECU, biceps and deltoid (52%). Our findings suggest that quick surgical skill evaluation is possible using wearables sensors, and features from ECU, deltoid, and biceps muscles contribute an important role in surgical skill evaluation.
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spelling pubmed-91658612022-06-05 Evaluation of surgical skill using machine learning with optimal wearable sensor locations Soangra, Rahul Sivakumar, R. Anirudh, E. R. Reddy Y., Sai Viswanth John, Emmanuel B. PLoS One Research Article Evaluation of surgical skills during minimally invasive surgeries is needed when recruiting new surgeons. Although surgeons’ differentiation by skill level is highly complex, performance in specific clinical tasks such as pegboard transfer and knot tying could be determined using wearable EMG and accelerometer sensors. A wireless wearable platform has made it feasible to collect movement and muscle activation signals for quick skill evaluation during surgical tasks. However, it is challenging since the placement of multiple wireless wearable sensors may interfere with their performance in the assessment. This study utilizes machine learning techniques to identify optimal muscles and features critical for accurate skill evaluation. This study enrolled a total of twenty-six surgeons of different skill levels: novice (n = 11), intermediaries (n = 12), and experts (n = 3). Twelve wireless wearable sensors consisting of surface EMGs and accelerometers were placed bilaterally on bicep brachii, tricep brachii, anterior deltoid, flexor carpi ulnaris (FCU), extensor carpi ulnaris (ECU), and thenar eminence (TE) muscles to assess muscle activations and movement variability profiles. We found features related to movement complexity such as approximate entropy, sample entropy, and multiscale entropy played a critical role in skill level identification. We found that skill level was classified with highest accuracy by i) ECU for Random Forest Classifier (RFC), ii) deltoid for Support Vector Machines (SVM) and iii) biceps for Naïve Bayes Classifier with classification accuracies 61%, 57% and 47%. We found RFC classifier performed best with highest classification accuracy when muscles are combined i) ECU and deltoid (58%), ii) ECU and biceps (53%), and iii) ECU, biceps and deltoid (52%). Our findings suggest that quick surgical skill evaluation is possible using wearables sensors, and features from ECU, deltoid, and biceps muscles contribute an important role in surgical skill evaluation. Public Library of Science 2022-06-03 /pmc/articles/PMC9165861/ /pubmed/35657912 http://dx.doi.org/10.1371/journal.pone.0267936 Text en © 2022 Soangra et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Soangra, Rahul
Sivakumar, R.
Anirudh, E. R.
Reddy Y., Sai Viswanth
John, Emmanuel B.
Evaluation of surgical skill using machine learning with optimal wearable sensor locations
title Evaluation of surgical skill using machine learning with optimal wearable sensor locations
title_full Evaluation of surgical skill using machine learning with optimal wearable sensor locations
title_fullStr Evaluation of surgical skill using machine learning with optimal wearable sensor locations
title_full_unstemmed Evaluation of surgical skill using machine learning with optimal wearable sensor locations
title_short Evaluation of surgical skill using machine learning with optimal wearable sensor locations
title_sort evaluation of surgical skill using machine learning with optimal wearable sensor locations
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9165861/
https://www.ncbi.nlm.nih.gov/pubmed/35657912
http://dx.doi.org/10.1371/journal.pone.0267936
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