Surgical endoscopy · 2025
Development and validation of the ENDOLAP artificial intelligence framework for inflammation severity classification in laparoscopic cholecystectomy: a cross-sectional study.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
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Mejía Norman A Rendón, De la Cruz Rey Said, Cervantes-Sánchez Carlos R, Figueroa Eduardo Cañedo, Alonso Graciela Ramírez
Аннотация
BACKGROUND: Laparoscopic cholecystectomy outcomes are significantly influenced by gallbladder inflammation severity, yet current intraoperative assessment remains subjective and lacks standardization. This study aimed to develop and validate an AI framework (ENDOLAP-IA) for real-time, objective severity classification to predict procedural difficulty and complications.
METHODS: A prospective cohort study (July 2023-February 2024) enrolled 53 elective cholecystectomy patients. A standardized 9-item checklist (ENDOLAP-IA tool) was validated for image quality via Delphi consensus and pilot testing. Intraoperative images (n = > 2000) were captured at three procedural stages using a 1080p laparoscope and annotated per Parkland severity grades. A YOLOv8 model was fine-tuned using transfer learning. Validation included fivefold cross-validation and external testing with 23 images assessed by blinded surgeons. Performance metrics (accuracy, precision, recall, F1-score, AUC-ROC) were evaluated against expert consensus.
RESULTS: The ENDOLAP tool achieved excellent content validity (CVI > 0.85) and inter-rater reliability (ICC = 0.82). The AI model demonstrated strong overall accuracy (87.3% ± 3.2%) and discriminative capability (AUC = 0.923, 95% CI [0.896-0.945]). Precision and recall were 84.7 and 86.8%, respectively. Performance varied by severity grade, with highest accuracy in Grades 1-2 (92.4-89.7%) and lower but clinically acceptable accuracy in severe Grades 4-5 (79.3-81.6%). External validation showed 82.6% agreement with surgeons and 91.3% sensitivity for severe inflammation. The AI eliminated interobserver variability seen in 17% of human assessments.
CONCLUSIONS: ENDOLAP-IA is the first AI framework to standardize intraoperative inflammatory severity classification in laparoscopic cholecystectomy. It achieves clinically reliable performance, enhances objectivity, and enables real-time decision support. Integration into surgical workflows may improve safety, training, and comparative outcome analyses.
METHODS: A prospective cohort study (July 2023-February 2024) enrolled 53 elective cholecystectomy patients. A standardized 9-item checklist (ENDOLAP-IA tool) was validated for image quality via Delphi consensus and pilot testing. Intraoperative images (n = > 2000) were captured at three procedural stages using a 1080p laparoscope and annotated per Parkland severity grades. A YOLOv8 model was fine-tuned using transfer learning. Validation included fivefold cross-validation and external testing with 23 images assessed by blinded surgeons. Performance metrics (accuracy, precision, recall, F1-score, AUC-ROC) were evaluated against expert consensus.
RESULTS: The ENDOLAP tool achieved excellent content validity (CVI > 0.85) and inter-rater reliability (ICC = 0.82). The AI model demonstrated strong overall accuracy (87.3% ± 3.2%) and discriminative capability (AUC = 0.923, 95% CI [0.896-0.945]). Precision and recall were 84.7 and 86.8%, respectively. Performance varied by severity grade, with highest accuracy in Grades 1-2 (92.4-89.7%) and lower but clinically acceptable accuracy in severe Grades 4-5 (79.3-81.6%). External validation showed 82.6% agreement with surgeons and 91.3% sensitivity for severe inflammation. The AI eliminated interobserver variability seen in 17% of human assessments.
CONCLUSIONS: ENDOLAP-IA is the first AI framework to standardize intraoperative inflammatory severity classification in laparoscopic cholecystectomy. It achieves clinically reliable performance, enhances objectivity, and enables real-time decision support. Integration into surgical workflows may improve safety, training, and comparative outcome analyses.