This systematic review explores machine learning (ML) applications in surgical motion analysis using non-optical motion tracking systems (NOMTS), alone or with optical methods. It investigates objectives, experimental designs, model effectiveness, and future research directions. From 3632 records, 84 studies were included, with Artificial Neural Networks (38%) and Support Vector Machines (11%) being the most common ML models. Skill assessment was the primary objective (38%). NOMTS used included internal device kinematics (56%), electromagnetic (17%), inertial (15%), mechanical (11%), and electromyography (1%) sensors. Surgical settings were robotic (60%), laparoscopic (18%), open (16%), and others (6%). Procedures focused on bench-top tasks (67%), clinical models (17%), clinical simulations (9%), and non-clinical simulations (7%). Over 90% accuracy was achieved in 36% of studies. Literature shows NOMTS and ML can enhance surgical precision, assessment, and training. Future research should advance ML in surgical environments, ensure model interpretability and reproducibility, and use larger datasets for accurate evaluation.

Background: Microsurgical skill acquisition and development are complex processes, due to the often complex learning curve, limited training possibilities, and growing restrictions on working hours. Simulation-based training programmes, employing various models, have been proposed. Nevertheless, the extent to which these training programmes are supported by scientific evidence is unclear. The aim of this systematic review is to evaluate the extent and quality of the scientific evidence backing validated microsurgical training programmes. Methods: A systematic literature review was conducted, following a study protocol established a priori and in accordance with the PRISMA guidelines. The databases searched were the Web of Science Core Collection (Web of Knowledge), Medline (Ovid), Embase (Embase.com), and ERIC (Ovid). Studies were included if they described microsurgical training programmes and presented a form of validation of training effectiveness. Data extraction included the number of participants, training duration and frequency, validation type, assessment methods, outcomes, study limitations, and a detailed training regimen. The risk of bias and quality were assessed using the Medical Education Research Study Quality Instrument (MERSQI). Validity was assessed using an established validity framework (content, face, construct, and criterion encompassing both concurrent and predictive validity). The Level of Evidence (LoE) and Recommendation (LoR) were evaluated using the Oxford Centre for Evidence-Based Medicine (OCEBM). Results: A total of 25 studies met the inclusion criteria. Training programmes were classified into one-time intensive courses or longitudinal curricula. Face, content, and construct validity were the most commonly assessed aspects, while predictive validity was the least frequently assessed and not properly evaluated. Training models ranged from low-fidelity (silicone tubes, synthetic vessels) to high-fidelity (live animal models). The Global Rating Scale (GRS), the Structured Assessment of Microsurgery Skills (SAMS), and the Objective Structured Assessment of Technical Skills (OSATS) were the most frequently used objective assessment tools for evaluation methods within the programmes. The risk of bias MERSQI score was 12.96, ranging from 10.5 to 15.5, and LoE and LoR scores were moderated. Across the studies, 96% reported significant improvement in microsurgical skills among participants. However, most studies were limited by small sample sizes, heterogeneity in baseline skills, and a lack of long-term follow-up. Conclusions: While validated microsurgical training programmes improve skill acquisition, challenges remain in terms of standardisation and best cost-effective methods. Future research should prioritise evaluating predictive validity, creating standardised objective assessment tools, and focus on skill maintenance.

Microvascular anastomosis is a critical but technically demanding skill in neurosurgery, with a long learning curve and high potential for error. Intensive hands-on training, such as that provided by the European Association of Neurosurgical Societies (EANS), aims to improve surgical proficiency, but objective evaluations of such courses are limited. Does participation in the EANS Step I and Step II microvascular training courses lead to a significant reduction in technical errors during microvascular anastomosis? A pre- and post-intervention study design was used to assess error reduction in participants of the EANS microvascular courses. The 92 participants were evaluated on a chicken end-to-end arterial anastomosis on the first and last days of course by two independent, blinded assessors using the Anastomosis Lapse Index (ALI). In the Step I cohort, the overall median error count decreased by 32.5 % (from 20 to 13.5, P < 0.0001). In the Step II cohort, the reduction was 46.9 % (from 16 to 8.5, P < 0.0001). Back-wall stitches, a critical error, was significantly reduced in both cohorts (P < 0.0001). Participation in the Step I and Step II EANS courses significantly improved microsurgical performance, as evidenced by reduced technical errors in microvascular anastomosis. Future efforts should focus on refining evaluation tools and on integrating technological innovations such as motion tracking and machine learning for enhanced feedback and long-term skill assessment.

Background/Objectives: Multiple validated microsurgical assessment instruments exist that differ in design and theoretical approach. Product-based tools evaluate the quality of the completed anastomosis, while process-based tools assess technique during task execution. Whether these tools measure the same underlying construct and can be used interchangeably has not been directly examined. This study compared the behaviour of three validated instruments, ALI, MARS10, and SMaRT, when applied to identical performances. Methods: Forty-five participants from 26 institutions across 14 countries performed an eight-stitch end-to-end vascular anastomosis on a chicken leg model, independently evaluated by two trained graders using all three instruments. Interrater reliability was assessed using intraclass correlation coefficients (ICC(2,1)), rank-order agreement using Spearman’s rho and Kendall’s W, and score distributions were examined for floor and ceiling effects. Results: All three instruments demonstrated high interrater reliability (ICC: ALI 0.955, MARS10 0.939, SMaRT 0.960), though product-based tools showed wider confidence intervals. Systematic rater differences were observed across all instruments. Rank correlations were strongest between ALI and MARS10 ($\rho$ = 0.913), with moderate correlations between process-based and product-based tools ($\rho$ = 0.698–0.749). MARS10 showed a ceiling effect, SMaRT underutilised its upper range, and ALI had the greatest score variability. Conclusions: Although demonstrating high interrater reliability, the instruments differ in rater variability, discriminatory capacity, and the aspects of performance prioritised, embodying different definitions of surgical skill. Current tools are not interchangeable, and instrument selection carries potential consequences for training and research. Future research in technology-driven approaches could offer a path toward more objective and consistent evaluation that is less dependent on human interpretation.