Surgical endoscopy · 2026
Artificial intelligence in abdominal wall hernia surgery: clinical applications and translational readiness.
СтатьяОбзорНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
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Bademkıran Ender, Bademkıran Sidar
Аннотация
BACKGROUND: Abdominal wall hernia repair is amongst the highest-volume operations in general surgery, yet recurrence, mesh-related morbidity, and surgeon-dependent variability remain unsolved. Artificial intelligence (AI) is increasingly applied across the perioperative continuum of hernia care.
METHODS: We performed a narrative synthesis of PubMed/MEDLINE, Web of Science, and Scopus from database inception to 31 December 2025, with a supplementary verification search on 15 January 2026 and eligibility based on the earliest online (Online-First/Epub) date. Studies were selected thematically rather than systematically for a clinically oriented synthesis. Two reviewers with clinical and data-science backgrounds screened independently; full-text agreement was substantial (Cohen κ = 0.82). Reporting followed the Scale for the Assessment of Narrative Review Articles (SANRA; six items scored 0-2, maximum 12; author self-assessment 10/12).
KEY FINDINGS: AI applications cluster into four domains: preoperative risk stratification and CT-based body composition analysis; intraoperative computer vision for phase recognition and critical structure detection; postoperative outcome prediction and digital follow-up; and registry-based big data analytics. Preoperative machine learning risk modeling and CT-based sarcopenia quantification are closest to clinical translation, although the evidence remains predominantly retrospective and internally validated; real-time intraoperative landmark detection, augmented reality, and federated learning remain at earlier translational stages.
CONCLUSIONS: Hernia surgery is well suited for AI integration because of its standardized anatomy, repeatable workflows, and growing registry and video repositories. The distinct contribution of this review is a criteria-based clinical readiness framework and a guideline-anchored translational roadmap, rather than a re-cataloguing of existing studies. Clinical adoption now depends on prospective external validation, demonstration of patient-level outcome benefit, adherence to TRIPOD+AI, CONSORT-AI, and SPIRIT-AI standards, and clarification of regulatory pathways for AI as a software medical device.
METHODS: We performed a narrative synthesis of PubMed/MEDLINE, Web of Science, and Scopus from database inception to 31 December 2025, with a supplementary verification search on 15 January 2026 and eligibility based on the earliest online (Online-First/Epub) date. Studies were selected thematically rather than systematically for a clinically oriented synthesis. Two reviewers with clinical and data-science backgrounds screened independently; full-text agreement was substantial (Cohen κ = 0.82). Reporting followed the Scale for the Assessment of Narrative Review Articles (SANRA; six items scored 0-2, maximum 12; author self-assessment 10/12).
KEY FINDINGS: AI applications cluster into four domains: preoperative risk stratification and CT-based body composition analysis; intraoperative computer vision for phase recognition and critical structure detection; postoperative outcome prediction and digital follow-up; and registry-based big data analytics. Preoperative machine learning risk modeling and CT-based sarcopenia quantification are closest to clinical translation, although the evidence remains predominantly retrospective and internally validated; real-time intraoperative landmark detection, augmented reality, and federated learning remain at earlier translational stages.
CONCLUSIONS: Hernia surgery is well suited for AI integration because of its standardized anatomy, repeatable workflows, and growing registry and video repositories. The distinct contribution of this review is a criteria-based clinical readiness framework and a guideline-anchored translational roadmap, rather than a re-cataloguing of existing studies. Clinical adoption now depends on prospective external validation, demonstration of patient-level outcome benefit, adherence to TRIPOD+AI, CONSORT-AI, and SPIRIT-AI standards, and clarification of regulatory pathways for AI as a software medical device.