Harvey E. Beardmore Division of Pediatric Surgery, The Montreal Children's Hospital, McGill University Health Centre, Montreal, Quebec, Canada.
Harvey E. Beardmore Division of Pediatric Surgery, The Montreal Children's Hospital, McGill University Health Centre, Montreal, Quebec, Canada.
J Pediatr Surg. 2024 May;59(5):774-782. doi: 10.1016/j.jpedsurg.2024.01.044. Epub 2024 Feb 13.
Artificial intelligence (AI) has been recently shown to improve clinical workflows and outcomes - yet its potential in pediatric surgery remains largely unexplored. This systematic review details the use of AI in pediatric surgery.
Nine medical databases were searched from inception until January 2023, identifying articles focused on AI in pediatric surgery. Two authors reviewed full texts of eligible articles. Studies were included if they were original investigations on the development, validation, or clinical application of AI models for pediatric health conditions primarily managed surgically. Studies were excluded if they were not peer-reviewed, were review articles, editorials, commentaries, or case reports, did not focus on pediatric surgical conditions, or did not employ at least one AI model. Extracted data included study characteristics, clinical specialty, AI method and algorithm type, AI model (algorithm) role and performance metrics, key results, interpretability, validation, and risk of bias using PROBAST and QUADAS-2.
Authors screened 8178 articles and included 112. Half of the studies (50%) reported predictive models (for adverse events [25%], surgical outcomes [16%] and survival [9%]), followed by diagnostic (29%) and decision support models (21%). Neural networks (44%) and ensemble learners (36%) were the most commonly used AI methods across application domains. The main pediatric surgical subspecialties represented across all models were general surgery (31%) and neurosurgery (25%). Forty-four percent of models were interpretable, and 6% were both interpretable and externally validated. Forty percent of models had a high risk of bias, and concerns over applicability were identified in 7%.
While AI has wide potential clinical applications in pediatric surgery, very few published AI algorithms were externally validated, interpretable, and unbiased. Future research needs to focus on developing AI models which are prospectively validated and ultimately integrated into clinical workflows.
2A.
人工智能（AI）最近被证明可以改善临床工作流程和结果，但它在小儿外科学中的潜力在很大程度上仍未得到探索。本系统评价详细介绍了 AI 在小儿外科学中的应用。
从创建到 2023 年 1 月，在 9 个医学数据库中进行了搜索，以确定专注于小儿外科学 AI 的文章。两位作者审查了合格文章的全文。如果研究是关于 AI 模型在主要通过手术治疗的小儿健康状况的开发、验证或临床应用的原始研究，则将其纳入研究。如果研究不是同行评审的、是评论文章、社论、评论或病例报告、不关注小儿外科状况、或未使用至少一种 AI 模型，则将其排除在外。提取的数据包括研究特征、临床专业、AI 方法和算法类型、AI 模型（算法）角色和性能指标、主要结果、可解释性、验证和使用 PROBAST 和 QUADAS-2 的偏倚风险。
作者筛选了 8178 篇文章，纳入了 112 篇。一半的研究（50%）报告了预测模型（用于不良事件[25%]、手术结果[16%]和生存[9%]），其次是诊断（29%）和决策支持模型（21%）。神经网络（44%）和集成学习器（36%）是跨应用领域最常用的 AI 方法。所有模型中代表的主要小儿外科亚专科是普通外科（31%）和神经外科（25%）。44%的模型是可解释的，6%的模型既具有可解释性又具有外部验证性。40%的模型存在高偏倚风险，7%的模型存在适用性问题。
虽然 AI 在小儿外科学中有广泛的潜在临床应用，但很少有发表的 AI 算法经过外部验证、可解释和无偏倚。未来的研究需要专注于开发前瞻性验证和最终整合到临床工作流程中的 AI 模型。
2A。
