Искусственный интеллект в анализе анатомии верхнечелюстной артерии: обоснование концептуального подхода

Верхнечелюстная артерия характеризуется значительной анатомической вариабельностью, что создаёт существенные трудности при планировании хирургических вмешательств в челюстно-лицевой области. Традиционные методы предоперационной визуализации требуют значительных временных затрат на интерпретацию данных и зависят от квалификации специалиста. Накопление больших массивов медицинских изображений в формате DICOM создаёт предпосылки для применения методов машинного обучения и глубоких нейронных сетей для автоматизации анализа сосудистых структур. Настоящая работа представляет концептуальное обоснование возможности применения технологий искусственного интеллекта для выявления анатомических вариаций верхнечелюстной артерии на основании анализа данных компьютерной и конусно-лучевой томографии. Проведён анализ современного состояния применения алгоритмов глубокого обучения в медицинской визуализации сосудистых структур головы и шеи, систематизированы известные анатомические вариации верхнечелюстной артерии и их клиническая значимость, сформулированы технические требования к архитектуре потенциальной системы автоматизированного анализа. Предлагаемый концептуальный подход включает использование сверточных нейронных сетей для семантической сегментации сосудистой сети, алгоритмов трёхмерной реконструкции для визуализации топографических взаимоотношений и системы классификации выявленных вариантов строения по степени хирургического риска. Обосновывается необходимость создания специализированной обучающей выборки аннотированных изображений верхнечелюстной артерии для обеспечения высокой точности распознавания. Обсуждаются потенциальные преимущества автоматизированного анализа, включая стандартизацию диагностических подходов, снижение времени предоперационного планирования и минимизацию интраоперационных осложнений, связанных с повреждением сосудов. Признаются существующие технические и организационные ограничения внедрения подобных систем, включая необходимость валидации на больших клинических когортах и интеграции в существующие медицинские информационные системы.

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