Work

Artificial Intelligence-Based Computer Vision Feedback to Improve Clinical Performance in Surgical Technology Education.

Year: 2026

Type: article

Abstract: Introduction: Advancements in artificial intelligence (AI) and computer vision offer new opportunities to improve clinical skills training. This study developed a real-time, AI-based computer vision system for detecting surgical instruments and evaluated its association with clinical performance among surgical technology students. Methods: A quasi-experimental pre-test/post-test study with a control group was conducted among 48 eligible surgical technology students at Iran University of Medical Sciences during the 2025-2026 academic year. Participants who met the predefined inclusion criteria were enrolled using census sampling. Following ethics approval (IR.IUMS.REC.1403.928), the participants were allocated to an intervention group (n=31), which received immediate AI-generated visual feedback after each instrument arrangement task, and a control group (n=17), which received routine instructor-led training without AI feedback. A YOLOv8-based deep learning model was trained using transfer learning on annotated images of surgical table setups. Clinical performance was assessed using a validated observational checklist (CVI=0.90; Cronbach's α=0.87). Data were analyzed using analysis of covariance (ANCOVA), the Wilcoxon signed-rank test, Spearman's correlation, and the intraclass correlation coefficient (ICC) in SPSS version 27. All 48 participants completed the study (attrition rate=0%). Results: =0.302). Excellent agreement was observed between AI-generated assessments and expert human ratings (ICC=0.984). Conclusions: Real-time, AI-based computer vision feedback was associated with improved surgical instrument arrangement performance among surgical technology students while providing an objective and reliable assessment of practical skills. The proposed system represents a promising proof-of-concept educational tool for simulation-based training and competency assessment and may complement conventional instructional approaches in surgical technology education.

Source: PubMed Central

DOI: 10.30476/jamp.2026.111362.2415

Authors: Kiarash Kamboozia, Sedigheh Hannani, Nazanin Sarraf Shahri, Zeynab Barzegar, Saeid Zarif Ghasemian

Institution: Iran University of Medical Sciences

Language: English

Topic: Surgical Simulation and Training

Subfield: Surgery

Field: Medicine

Domain: Health Sciences

SDGs: Quality education, Good health and well-being

Open Access status: green

Funder: Iran University of Medical Sciences