ArticleInternational journal of computer assisted radiology and surgery2020
Objective classification of psychomotor laparoscopic skills of surgeons based on three different approaches.
Article in International journal of computer assisted radiology and surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.
- Technical skill assessment in minimally invasive surgery using artificial intelligence: a systematic review.Surgical endoscopy · 2023Pooled it
- Developing and evaluating image-based metrics for open surgical suturing skill assessment.Global surgical education : journal of the Association for Surgical Education · 2026Article
- Evaluating the performance of a new ergonomic laparoscopic needle holder for intracorporeal suturing.PloS one · 2025Article
- Optimized automated detection of diabetic retinopathy severity: integrating improved multithresholding tunicate swarm algorithm and improved hybrid butterfly optimization.Health information science and systems · 2024Article
- Automated surgical skill assessment in endoscopic pituitary surgery using real-time instrument tracking on a high-fidelity bench-top phantom.Healthcare technology letters · 2024Article
- Impact of artificial intelligence on the training of general surgeons of the future: a scoping review of the advances and challenges.Acta cirurgica brasileira · 2024Article
- Objective psychomotor laparoscopic skills evaluation using a low-cost wearable device based on accelerometry: construct and concurrent validity study.Surgical endoscopy · 2023Article
- Semi-Supervised k-Star (SSS): A Machine Learning Method with a Novel Holo-Training Approach.Entropy (Basel, Switzerland) · 2023Article
- Computer-assisted approaches for measuring, segmenting, and analyzing functional upper extremity movement: a narrative review of the current state, limitations, and future directions.Frontiers in rehabilitation sciences · 2023Review
- A system for real-time multivariate feature combination of endoscopic mitral valve simulator training data.International journal of computer assisted radiology and surgery · 2022Article
- Objective evaluation of laparoscopic surgical skills in wet lab training based on motion analysis and machine learning.Langenbeck's archives of surgery · 2022Article
- Objective assessment of laparoscopic targeting skills using a Short-Time Power of Difference (STPOD) method.International journal of computer assisted radiology and surgery · 2022Article
- Ranking surgical skills using an attention-enhanced Siamese network with piecewise aggregated kinematic data.International journal of computer assisted radiology and surgery · 2022Article
- Automatic assessment of laparoscopic surgical skill competence based on motion metrics.PloS one · 2022Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe determination of surgeons' psychomotor skills in minimally invasive surgery techniques is one of the major concerns of the programs of surgical training in several hospitals. Therefore, it is important to assess and classify objectively the level of experience of surgeons and residents during their training process. The aim of this study was to investigate three classification methods for establishing automatically the level of surgical competence of the surgeons based on their psychomotor laparoscopic skills.
methodsA total of 43 participants, divided into an experienced surgeons group with ten experts (> 100 laparoscopic procedures performed) and non-experienced surgeons group with 24 residents and nine medical students (< 10 laparoscopic procedures performed), performed three tasks in the EndoViS training system. Motion data of the instruments were captured with a video-tracking system built into the EndoViS simulator and analyzed using 13 motion analysis parameters (MAPs). Radial basis function networks (RBFNets), K-star (K*), and random forest (RF) were used for classifying surgeons based on the MAPs' scores of all participants. The performance of the three classifiers was examined using hold-out and leave-one-out validation techniques.
resultsFor all three tasks, the K-star method was superior in terms of accuracy and AUC in both validation techniques. The mean accuracy of the classifiers was 93.33% for K-star, 87.58% for RBFNets, and 84.85% for RF in hold-out validation, and 91.47% for K-star, 89.92% for RBFNets, and 83.72% for RF in leave-one-out cross-validation.
conclusionsThe three proposed methods demonstrated high performance in the classification of laparoscopic surgeons, according to their level of psychomotor skills. Together with motion analysis and three laparoscopic tasks of the Fundamental Laparoscopic Surgery Program, these classifiers provide a means for objectively classifying surgical competence of the surgeons for existing laparoscopic box trainers.
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