The Journal of craniofacial surgery · 2026
Computer Vision for Preoperative Soft-Tissue Landmarking in Cleft Lip Repair: A Systematic Review.
Систематический обзорСтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
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Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Munns Callum, Bevacqua Frank, Najibi Tara, Chong David, Low Nelson
Аннотация
BACKGROUND: Cleft lip repair presents complex surgical challenges requiring precise preoperative planning. Emerging computer vision technologies powered by artificial intelligence (AI) may support more consistent and accurate surgical assessments. This systematic review defines the scope, methods, and performance of computer vision for preoperative soft-tissue landmarking in cleft lip repair.
METHODS: Articles were retrieved from Medline and Embase. Inclusion criteria encompassed studies using computer vision techniques for automated soft-tissue analysis before cleft. The selected studies were appraised based on their scope, design, and performance.
RESULTS: Six studies were eligible for inclusion. The included studies used convolutional neural networks (CNNs), generative adversarial networks (GANs), and 3D shape analysis to guide surgical marking, cleft severity scoring, and symmetry assessment. Most models demonstrated high accuracy and efficiency compared with manual methods, showing promise for computer vision as a virtual conduit of surgical expertise.
CONCLUSION: Computer vision offers strong potential to standardise and improve preoperative cleft lip planning. Future integration into surgical workflows must prioritise ethical design, validation, transparency, and equity of access.
METHODS: Articles were retrieved from Medline and Embase. Inclusion criteria encompassed studies using computer vision techniques for automated soft-tissue analysis before cleft. The selected studies were appraised based on their scope, design, and performance.
RESULTS: Six studies were eligible for inclusion. The included studies used convolutional neural networks (CNNs), generative adversarial networks (GANs), and 3D shape analysis to guide surgical marking, cleft severity scoring, and symmetry assessment. Most models demonstrated high accuracy and efficiency compared with manual methods, showing promise for computer vision as a virtual conduit of surgical expertise.
CONCLUSION: Computer vision offers strong potential to standardise and improve preoperative cleft lip planning. Future integration into surgical workflows must prioritise ethical design, validation, transparency, and equity of access.