Deep neural networks for the assessment of surgical skills: A systematic review

Yanik, Erim; Intes, Xavier; Krüger, Uwe; Yan, Pingkun; Diller, David; Voorst, Brian Van; Makled, Basiel; Norfleet, Jack; De, Suvranu

doi:10.1177/15485129211034586

Cited by 29 publications

(27 citation statements)

References 56 publications

(89 reference statements)

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“…Simulation-based learning (SBL) in surgical education has been found as an effective way of education [1,2]. With the rapid development of IT (info-communication technologies) and other engineering disciplines, innovative tools and methods are introduced in surgical education, including virtual and augmented reality [3], 3D printing [4], and artificial intelligence (AI) [5]. Despite their proven advantages and effectiveness, it must be mentioned that some high-end simulators are not widely available in low-or middleincome countries due to their high costs [6].…”

Section: Introductionmentioning

confidence: 99%

Distance Education for Basic Surgical Skills Using Homemade Tools—DIY Methods for Emergency Situations

et al. 2022

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The aim of our research was to establish a reproducible curriculum that offers the possibility to gain basic surgical skills (knot tying, suturing, laparoscopy basics) through distance education in emergency situations by using tools available in the household. Forty-six volunteering third- and fourth-year medical students were involved in the study. The distance education system was set up using homemade or easily obtainable tools (an empty can, shoe box, sponge, etc.) to teach surgical knotting, suturing, and basic laparoscopic skills. The reachable learning objectives were contrasted with the original course plan. Feedback from the students has been collected. The students’ results were compared to the regular course of the previous years. Seventy-nine percent of the original learning objectives could be reached completely, and 15% partially. The necessary tools were available for 82% of the students. The students evaluated the course for 4.26 in general and 4.86 considering the circumstances (on a 5-level-scale). The homemade trainers were assessed over four as an acceptable substitution. Students’ exam results decreased only by 7% compared to the previous two years. Basic surgical skills can be educated with acceptable efficiency and student satisfaction using distance teaching and homemade tools. This is the first study where not only the simulators but the surgical instruments were replaced with household tools and evaluated by a reproducible curriculum.

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Section: Introductionmentioning

confidence: 99%

Distance Education for Basic Surgical Skills Using Homemade Tools—DIY Methods for Emergency Situations

et al. 2022

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“…Leave-one-super-trial-out is another available rigorous CV technique. 94 Many of the reviewed papers carry out a 10-fold CV, whereas a few execute LOSO CV. 48 , 49 The most common metrics used for evaluation are accuracy, specificity, and sensitivity.…”

Section: Deep Learning Methodologymentioning

confidence: 99%

Deep learning in fNIRS: a review

Eastmond

Subedi

et al. 2022

Neurophoton.

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Significance: Optical neuroimaging has become a well-established clinical and research tool to monitor cortical activations in the human brain. It is notable that outcomes of functional nearinfrared spectroscopy (fNIRS) studies depend heavily on the data processing pipeline and classification model employed. Recently, deep learning (DL) methodologies have demonstrated fast and accurate performances in data processing and classification tasks across many biomedical fields.Aim: We aim to review the emerging DL applications in fNIRS studies.Approach: We first introduce some of the commonly used DL techniques. Then, the review summarizes current DL work in some of the most active areas of this field, including braincomputer interface, neuro-impairment diagnosis, and neuroscience discovery.Results: Of the 63 papers considered in this review, 32 report a comparative study of DL techniques to traditional machine learning techniques where 26 have been shown outperforming the latter in terms of the classification accuracy. In addition, eight studies also utilize DL to reduce the amount of preprocessing typically done with fNIRS data or increase the amount of data via data augmentation. Conclusions:The application of DL techniques to fNIRS studies has shown to mitigate many of the hurdles present in fNIRS studies such as lengthy data preprocessing or small sample sizes while achieving comparable or improved classification accuracy.

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“…Thus, there is a lack of objective high-fidelity tools to evaluate surgical skills in hospital environments. Previously researchers have estimated surgical skills using i)kinematic data and convolutional neural network [ 9 ], ii) kinematic data as putative markers and deep neural networks [ 10 ], iii) virtual reality spinal task and machine learning algorithms (support vector machines, k-nearest neighbors, least discriminant analysis, naïve bayes and decision tree) [ 11 ], iv) image processing and deep neural network during robotic surgery [ 12 – 14 ], v) kinematic data from da Vinci robot and global rating score and machine learning (kNN, logistic regression, SVM) [ 15 ]. Recently deep learning-based haptic guidance systems have been used for surgical skill development [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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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Deep neural networks for the assessment of surgical skills: A systematic review

Cited by 29 publications

References 56 publications

Distance Education for Basic Surgical Skills Using Homemade Tools—DIY Methods for Emergency Situations

Distance Education for Basic Surgical Skills Using Homemade Tools—DIY Methods for Emergency Situations

Deep learning in fNIRS: a review

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

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