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克服数据障碍:转移学习用于一般外科90天死亡率预测 - 一个回顾性的多中心发展和比较研究

Axel Winter¹, Bjarne Pfitzner², Robin P van de Water²

¹Department of Surgery, Charité - Universitätsmedizin Berlin, Campus Charité Mitte and Campus Virchow-Klinikum, Berlin, Germany.

International journal of surgery (London, England)

|November 4, 2025

概括

转移学习 (TL) 显著改善了人工智能 (AI) 模型的预测手术死亡率的性能,特别是在数据稀缺的领域. 这种人工智能方法提高了一般外科手术中的风险分层.

科学领域:

手术瘤学手术瘤学
医疗人工智能的人工智能
机器学习在医疗保健中的应用

背景情况:

术前风险分层对于优化外科手术结果和一般手术中的患者决策至关重要.
数据稀缺性对开发用于外科应用的高维人工智能 (AI) 模型构成重大挑战.
转移学习 (TL) 提供了一个解决方案,通过使知识从预先训练的模型转移到新的,数据有限的外科领域.

研究的目的:

评估转移学习 (TL) 在增强人工智能 (AI) 模型预测一般外科90天死亡率的性能方面的有效性.
将TL模型与传统机器学习 (ML) 模型和已建立的风险评分进行基准比较.

主要方法:

一项多中心研究包括14922名接受高级整体手术的患者.
大规模的源模型被训练在85个手术前参数的死亡率预测.
在食道,肝脏,胰腺和结直肠手术中应用了器官特定的微调.
与标准ML模型和使用AUROC,AUPRC和F1得分的传统风险得分进行TL模型比较.

主要成果:

转移学习 (TL) 显著改善了精确回忆曲线 (AUPRC) 下的区域,食道手术的38%,肝脏的14%,胰腺手术的8%.
患者年龄和查尔森并发症指数 (CCI) 在TL模型中始终是重量较高的特征.
使用TL开发的所有神经网络 (NN) 在死亡率预测方面都超过了ASA物理状态和CCI.

结论:

机器学习模型的表现优于传统的手术前死亡率预测方法.
转移学习有效地解决了手术AI中的数据限制,大大提高了模型性能.
TL提出了一种有前途的策略,用于克服AI开发手术中的数据限制.

关键词:

人工智能是一种人工智能.整体外科的一般手术.机器学习是机器学习.在手术前的风险预测和预测.转移学习转移学习

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