Журнал «Медицина неотложных состояний» Том 20, №6, 2024

Останні досягнення в галузі штучного інтелекту спричинили сплеск застосування комп’ютерного зору (КЗ) в аналізі хірургічного відео. Хірургічні ускладнення часто виникають через помилки в судженні та прийнятті рішень. При лапароскопічній холецистектомії для запобігання ушкодженню жовчних проток зазвичай рекомендується досягнення критичного погляду на безпеку. Однак показники ушкодження жовчних проток залишаються стабільними, імовірно, через непослідовне застосування або погане розуміння критичного погляду на безпеку. Досягнення в галузі штучного інтелекту зробили можливим навчання алгоритмів, які ідентифікують анатомію та інтерпретують хірургічне поле. Методи КЗ на основі штучного інтелекту можуть використовувати дані хірургічного відео для розробки автоматизованих інструментів підтримки прийняття рішень у реальному часі і систем навчання хірургів. Ефективність застосування КЗ при хірургічних процедурах все ще перебуває на стадії ранньої оцінки. В огляді подано найпоширеніші алгоритми глибокого навчання при КЗ і детально описано їх використання в чотирьох прикладних сценах, включно з розпізнаванням хірургічних фаз, анатомії, інструментів і дій при лапароскопічній холецистектомії. У базах даних MEDLINE, Scopus, IEEE Xplore було проведено пошук публікацій до 2024 року. Під час пошуку використовувалися ключові слова «лапароскопічна холецистектомія», «штучний інтелект». Описане на сьогодні застосування КЗ при лапароскопічній холецистектомії є обмеженим. Більшість поточних досліджень зосереджені на ідентифікації робочого процесу й анатомічної структури, у той час як ідентифікація інструментів і хірургічних дій все ще чекає на подальші прориви. Майбутні дослідження щодо використання КЗ при лапароскопічній холецистектомії повинні бути зосереджені на застосуванні в більшій кількості сценаріїв, таких як оцінка навичок хірурга і розробка більш ефективних моделей.

Recent advances in artificial intelligence (AI) have sparked a surge in the application of computer vision (CV) in surgical video analysis. Surgical complications often occur due to lapses in judgment and decision-making. In laparoscopic cholecystectomy, achievement of the critical view of safety is commonly advocated to prevent bile duct injuries. However, bile duct injuries rates remain stable, probably due to inconsistent application or a poor understanding of critical view of safety. Advances in AI have made it possible to train algorithms that identify anatomy and interpret the surgical field. AI-based CV techniques may leverage surgical video data to develop real-time automated decision support tools and surgeon training systems. The effectiveness of CV application in surgical procedures is still under early evaluation. The review considers the commonly used deep learning algorithms in CV and describes their usage in detail in four application scenes, including phase recognition, anatomy detection, instrument detection and action recognition in laparoscopic cholecystectomy. The MedLine, Scopus, and IEEE Xplore databases were searched for publications up to 2024. The keywords used in the search were “laparoscopic cholecystectomy”, “artificial intelligence”. The currently described applications of CV in laparoscopic cholecystectomy are limited. Most current research focus on the identification of workflow and anatomical structure, while the identification of instruments and surgical actions is still awaiting further breakthroughs. Future research on the use of CV in laparoscopic cholecystectomy should focus on application in more scenarios, such as surgeon skill assessment and the development of more efficient models.

лапароскопічна холецистектомія; штучний інтелект; комп’ютерний зір; критичний погляд на безпеку; огляд

laparoscopic cholecystectomy; artificial intelligence; computer vision; critical view of safety; review

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