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人工智能在异常性肺纤维化:进步,挑战和未来方向
Moisés Selman1, Ivette Buendia-Roldan2, Annie Pardo3
1Instituto Nacional de Enfermedades Respiratorias "Ismael Cosío Villegas", Mexico City, México mselmanl@yahoo.com.mx.
The European respiratory journal
|December 11, 2025
概括
人工智能 (AI) 在改善异常性肺纤维化 (IPF) 的诊断和预后方面表现有前途. 人工智能模型可以增强早期检测和个性化治疗策略,尽管数据和验证方面的挑战仍然存在.
科学领域:
- 肺部医学 肺部医学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 异形性肺纤维化 (IPF) 是一种渐进的肺部疾病,具有具有挑战性的诊断和不可预测的进展.
- 目前IPF的诊断和预后方法存在局限性.
研究的目的:
- 审查人工智能 (AI) 在治疗异形性肺纤维化 (IPF) 中不断变化的作用.
- 探索AI在改善IPF诊断,预后和治疗策略方面的潜力.
- 确定AI整合在IPF临床实践中的挑战和未来方向.
主要方法:
- 审查人工智能的最新进展,包括应用于IPF的机器学习 (ML) 和深度学习 (DL) 模型.
- 在高分辨率计算机断层扫描 (HRCT) 中对人工智能应用的分析.
- 检查使用各种患者数据 (临床,成像,omics) 的预测模型.
- 对IPF的AI驱动药物发现进行讨论.
主要成果:
- 深度学习模型,特别是卷积神经网络,增强了HRCT分析,用于准确的IPF诊断.
- 预测性ML和DL模型显示了评估IPF进展和患者风险分层的潜力.
- 人工智能驱动的药物发现已经确定了IPF的潜在抗纤维菌疗法.
- 挑战包括数据异质性,模型可解释性,以及需要大规模验证.
结论:
- 人工智能具有巨大的潜力,可以通过早期诊断,个性化预后和向治疗来彻底改变IPF的管理.
- 克服数据标准化,模型透明度和验证方面的挑战对于临床整合至关重要.
- 未来的研究应该专注于开发在大型IPF队列中验证的强大,透明的AI模型.
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