vitjВрач и информационные технологииMedical Doctor and Information Technologies1811-01932413-5208Pirogov National Medical and Surgical Center10.25881/18110193_2026_1_6vitj-309Research ArticleОБЗОРЫREVIEWSПерспективы применения искусственного интеллекта для выявления Spina Bifida у плода, детей и взрослыхProspects for using artificial intelligence to detect Spina Bifida in fetuses, children, and adultshttps://orcid.org/0009-0001-3568-8138КазариноваВ. Е.KazarinovaV. E.

г. Москва

Moscow

KazarinovaVE@zdrav.mos.ruhttps://orcid.org/0000-0002-2307-725XЗинченкоВ. В.ZinchenkoV. V.

г. Москва

Moscow

https://orcid.org/0009-0006-7975-8974КовальчукА. Ю.KovalchukA. Y.

г. Москва

Moscow

KovalchukAY2@zdrav.mos.ruhttps://orcid.org/0000-0002-8235-9361АхмадЕ. С.AkhmadE. S.

г. Москва

Moscow

AkhmadES@zdrav.mos.ruhttps://orcid.org/0000-0002-0041-3281ПамоваА. П.PamovaA. P.

к.м.н.

г. Москва

PhD

Moscow

PamovaAP@zdrav.mos.ruhttps://orcid.org/0000-0001-7786-0349АрзамасовК. М.ArzamasovK. M.

д.м.н.

г. Москва

DSc

Moscow

ArzamasovKM@zdrav.mos.ruhttps://orcid.org/0000-0002-0245-4431ОмелянскаяО. В.OmelyanskayaO. V.

г. Москва

Moscow

npcmr@zdrav.mos.ruhttps://orcid.org/0000-0002-2990-7736ВладзимирскийА. В.VladzymyrskyyA. V.

д.м.н.

г. Москва

DSc

Moscow

npcmr@zdrav.mos.ruГБУЗ «НПКЦ ДиТ ДЗМ»РоссияMoscow Center for Diagnostics & TelemedicineRussian Federation20262903202601621Copyright © Казаринова В.Е., Зинченко В.В., Ковальчук А.Ю., Ахмад Е.С., Памова А.П., Арзамасов К.М., Омелянская О.В., Владзимирский А.В., 20262026Казаринова В.Е., Зинченко В.В., Ковальчук А.Ю., Ахмад Е.С., Памова А.П., Арзамасов К.М., Омелянская О.В., Владзимирский А.В.Kazarinova V.E., Zinchenko V.V., Kovalchuk A.Y., Akhmad E.S., Pamova A.P., Arzamasov K.M., Omelyanskaya O.V., Vladzymyrskyy A.V.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.vit-j.ru/jour/article/view/309Актуальность и цель

Актуальность и цель: Spina Bifida (SB) — это врожденный порок развития позвоночника, требующий своевременной диагностики. Цифровизация здравоохранения и стремительное развитие технологий искусственного интеллекта (ИИ) способствуют внедрению ИИ в различные области медицины, включая диагностику SB. Целью данного исследования является анализ потенциала применения ИИ для выявления признаков SB у плода, детей и взрослых, оценка существующих подходов, а также определение ключевых областей для дальнейших исследований и разработок.

Методы

Методы: в представленную обзорную работу включены оригинальные и обзорные публикации, материалы конференций с описанием применения алгоритмов ИИ к любым видам данных на любом из этапов исследования SB у плода, детей и взрослых. Поиск доступной литературы был произведен в базах данных: PubMed, Google Scholar и РИНЦ (eLibrary.ru).

Результаты

Результаты: было отобрано 17 публикаций о применении ИИ в диагностике SB, удовлетворяющих критериям поиска. Выделены следующие виды данных, которые анализируются с использованием алгоритмов ИИ: данные медицинской визуализации (магнитно-резонансной томографии, ультразвукового исследования, рентгенографии, видеоуродинамики), данные лабораторной диагностики, генетическая информация.

Обсуждение

Обсуждение: алгоритмы ИИ показали высокую эффективность в выявлении SB и ее осложнений на различных этапах диагностического процесса. Была продемонстрирована перспектива использования компьютерного зрения для выявления SB на изображениях различной модальности, алгоритмов машинного обучения в лабораторной диагностике и в генетических исследованиях.

Background and Objective

Background and Objective: Spina Bifida (SB) is a congenital spinal malformation requiring timely diagnosis. Digitalization of healthcare and rapid development of artificial intelligence technologies (AI) facilitate AI implementation in various fields of medicine, including SB diagnosis. The aim of this study is to analyze the potential of AI for detecting signs of Spina Bifida in fetuses, children, and adults, evaluate existing approaches, and identify key areas for further research and development.

Methods

Methods: this review includes original and review publications, conference materials describing the application of AI algorithms to any type of data at any stage of the diagnostic process of SB in fetuses, children, and adults. A search of available literature was conducted in the following databases: PubMed, Google Scholar, and the Russian Science Citation Index (eLibrary.ru).

Results

Results: seventeen publications on the use of AI in the diagnosis of SB were selected, which met the search criteria. Using AI algorithms, the following types of data were identified and analyzed: medical imaging data (magnetic resonance imaging, ultrasound, X-ray, video urodynamics), laboratory diagnostics data, and genetic information.

Discussion

Discussion: AI algorithms have shown high efficiency in detecting SB and its complications at various stages of the diagnostic process. The prospect of using computer vision to detect SB in images of various modalities as well as machine learning algorithms in laboratory diagnostics and genetic research was demonstrated.

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The authors declare that there are no conflicts of interest present.