可解释的AI建模术后手术部位感染风险在自主乳房重建中的AI建模
Diwakar Phuyal1, Berk Ozmen1, Ibrahim Berber2
1Department of Plastic Surgery, Cleveland Clinic, Cleveland, OH, USA.
Journal of plastic, reconstructive & aesthetic surgery : JPRAS
|January 29, 2026
概括
开发了一种机器学习模型,用于预测经过深下腹膜穿孔器 (DIEP) 板乳房重建后的手术部位感染 (SSI). 确定的主要预测因素包括BMI,体重和手术时间,有助于风险评估.
科学领域:
- 整形外科 整形外科 整形外科
- 外科手术的结果
- 人工智能在医学中的应用
背景情况:
- 手术部位感染 (SSI) 是自主乳房重建后的严重并发症,特别是DIEP门手术.
- 现有的模型难以准确识别患有SSI高风险的患者.
- 这项研究解决了在DIEP手术中改善SSI预测的需求.
研究的目的:
- 开发和验证一种机器学习模型,用于预测DIEP片乳房重建后的30天SSI风险.
- 确定有助于SSI发展的关键预测因素.
- 利用可解释的人工智能来理解SSI风险因素.
主要方法:
- 来自ACS NSQIP数据库 (2016-2022) 的13312个DIEP重建的分析.
- 开发一个XGBoost分类器,使用人口统计,并发病,操作和实验室数据.
- 使用准确度,回忆度,精度,F1分数和AUC评估模型性能; SHAP用于解释性.
主要成果:
- 整体SSI率为8.14%,达到8.14%.
- XGBoost模型在30天的SSI预测中实现了74.6%的准确性和0.63 AUC.
- 重要的SSI预测因素包括手术年份,体重指数升高,体重增加,手术时间延长和住院时间长.
结论:
- 可解释性人工智能可用于识别大型数据集中的SSI风险模式.
- 该模型为回顾性SSI风险分析和假设生成提供了一个框架.
- 这项工作为未来的前性和手术前的SSI预测模型奠定了基础.
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