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DOI 10.3390/medicina62091705

Медицинская библиотека · Medicina (Kaunas, Lithuania) · 2026

Artificial Intelligence for Automated Recognition of Hepatocystic Anatomy During Laparoscopic Cholecystectomy: Current Evidence, Clinical Readiness, and Future Directions.

Cosma Catalin Dumitru, Molnar Dragos Calin, Botoncea Marian, Nicolescu Cosmin, Molnar Calin, Butiurca Vlad-Olimpiu

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Аннотация

Background and

Objectives

Bile duct injury remains a major safety concern during laparoscopic cholecystectomy, and reliable interpretation of hepatocystic anatomy is fundamental to safe dissection. Artificial intelligence (AI)-based computer vision may support anatomical recognition, critical view of safety (CVS) assessment, and intraoperative decision support. This narrative review synthesized the current evidence, clinical applications, readiness for implementation, and future requirements of anatomy-aware AI during laparoscopic cholecystectomy. Materials and

Methods

Five bibliographic databases were searched through 15 August 2026, supplemented by citation tracking and targeted searches. Studies were classified according to clinical task, dataset, reference standard, validation design, performance metrics, real-time capability, human factor assessment, and clinical readiness stage. The evidence base comprised 105 verified references: 50 primary AI reports and 55 contextual or methodological sources; 39 direct model development or evaluation reports were characterized in detail.

Results

Investigated applications included landmark detection, semantic segmentation, CVS assessment, safe and hazard zone mapping, multimodal analysis incorporating indocyanine green fluorescence, automated documentation, education, and real-time perceptual prompting. Among the 39 direct reports, 24 remained at the offline proof of concept or internal validation stage, eight achieved temporal, external, or multicenter validation, six demonstrated prospective operating-room feasibility, and one reached post-deployment surveillance. The latter evaluated a surgical-process outcome rather than patient morbidity. Generalizability was constrained by dataset overlap, heterogeneous reference standards and metrics, domain shift, and underrepresentation of difficult cholecystectomy. No included study demonstrated reduced bile duct injury or other patient-level benefit.

Conclusions

Despite progression to prospective feasibility and one post-deployment process surveillance report, no included study demonstrated a reduction in bile duct injury or another patient-level outcome. Current evidence supports adjunctive applications in documentation, video triage, education, coaching, and quality assurance, while surgeon-facing deployment requires further multicenter, human factor, and comparative-effectiveness evaluation.

Для цитирования: Cosma Catalin Dumitru, Molnar Dragos Calin, Botoncea Marian, Nicolescu Cosmin, Molnar Calin, Butiurca Vlad-Olimpiu. Artificial Intelligence for Automated Recognition of Hepatocystic Anatomy During Laparoscopic Cholecystectomy: Current Evidence, Clinical Readiness, and Future Directions.. Medicina (Kaunas, Lithuania). 2026. DOI: 10.3390/medicina62091705.

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