在炎症性肠道疾病中进行联合学习:保护隐私的人工智能的未来
Pierluigi Puca1, Loris Riccardo Lopetuso2, Lucrezia Laterza3
1IBD Unit, CEMAD, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy; Dipartimento di Medicina e Chirurgia Traslazionale, Università Cattolica del Sacro Cuore, Rome, Italy.
联合学习 (FL) 能够在不影响患者隐私的情况下对分散的医疗数据进行人工智能模型培训. 这种方法对改善诊断性能和推进诸如炎症性肠道疾病等领域的研究充满希望.
科学领域:
- 人工智能在医学中的应用
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
背景情况:
- 联合学习 (FL) 是一种保护隐私的人工智能技术,用于在分散的数据上训练模型.
- 医学研究面临着数据碎片化和隐私方面的挑战,阻碍了多中心研究.
- FL提供了一种解决方案,即允许在没有数据传输的情况下进行协作模型开发.
研究的目的:
- 解释联合学习的过程和运作.
- 审查在临床研究中FL的实施及其表现.
- 探索FL在炎症性肠病 (IBD) 研究中的潜力.
主要方法:
- 对临床研究中的联合学习现有文献的审查.
- 分析多中心研究,证明FL的诊断和预后性能.
- 聚焦讨论FL在炎症性肠道疾病中的应用.
主要成果:
- FL的诊断和预后性能与集中方法相比或优于集中方法 (AUC > 0.80).
- 在各种医疗领域,FL已经取得了成功.
- FL对IBD研究具有重大潜力,包括治疗反应预测和图像分析.
结论:
- 联合学习可以克服医学研究中的数据限制,特别是在IBD等疾病中.
- FL促进了跨机构的协作算法开发,同时保持了患者的保密性.
- 未来的方向包括解决FL的语义互操作性和隐私问题.
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