This study evaluates the potential of integrating multimodal ultrasound diagnostics and artificial intelligence (AI) technologies into surgical practice. Data from 242 patients who under-went laparoscopic cholecystectomy between 2020 and 2025 were analyzed using the "MedSkan AI"platform. The study employed deep learning architectures, specifically Y0L0v3 and YOLOv8, for automated identification of biliary anatomical landmarks and real-time evaluation of the "Critical View of Safety" (CVS) criteria. The findings revealed a diagnostic accuracy exceeding 95%, with a 92.4%> correlation between AI-assisted identification and expert surgical assessment. The integra-tion of AI algorithms into ultrasound imaging significantly reduces operator dependence and mini-mizes the risk of iatrogenic bile duct injuries. The study concludes that AI-driven multimodal diag-nostics enhance surgical precision and patient safety in hepatobiliary surgery.
| Mualliflar | Таджибаев, Ш, Абдурашидов, Ф, Тоджиходжаев, Ш, Саминжонов, Х, Tojiboev, Sh, Abdurashidov, F, Tojikxo'jaev , Sh, Saminjonov, X, Tajibaev, Sh, Abdurashidov, F, Tojikhodjaev, Sh, Saminjonov, Kh |
|---|---|
| Jurnal | Тиббиёт ва спорт |
| Nashr sanasi | 2026-06-05 |
| Son | 1 |
| Betlar | 63-67 |
| Til | Rus |
холелитиаз, мультимодальное УЗИ, искусственный интеллект, POCUS, лапароскопическая холецистэктомия, нейронные сети, критерии CVS, cholelithiasis, multimodal ultrasound, artificial intelligence, POCUS, laparo-scopic cholecystectomy, neural networks, CVS criteria, xolelitiaz, multimodal ultratovush, sun’iy intellekt, POCUS, laparoskopik xo-lesistektomiya, neyron tarmoqlari, CVS mezonlari
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