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

• Published in: PloS one Vol. 17; no. 6; p. e0267936
• Main Authors: Soangra, Rahul, Sivakumar, R., Anirudh, E. R., Reddy Y., Sai Viswanth, John, Emmanuel B.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 03.06.2022; Public Library of Science (PLoS)
• Subjects: Accelerometers; Accuracy; Algorithms; Bayes Theorem; Bayesian analysis; Biology and Life Sciences; Biosensors; Classification; Classifiers; Clinical competence; Computer and Information Sciences; Discriminant analysis; Electromyography; Engineering and Technology; Entropy; Evaluation; Feature selection; Kinematics; Laparoscopy; Learning algorithms; Machine Learning; Medical students; Medicine and Health Sciences; Minimally invasive surgery; Muscle contraction; Muscle function; Muscle, Skeletal - physiology; Muscles; Neural networks; People and Places; Physical Sciences; Research and Analysis Methods; Robotics; Sensors; Skills; Support vector machines; Surgeons; Sutures; Task complexity; Urology; Wearable Electronic Devices; Wearable technology; Workloads; United States
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0267936

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.