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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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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. |
format | Online Article Text |
id | pubmed-9165861 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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