通过智能手机传输的胸部X射线的AI驱动诊断的挑战:COVID-19中的一个案例研究
Mariamma Antony1, Siva Teja Kakileti2, Rachit Shah3
1Indian Institute of Science, Bangalore, India. mariammaa@iisc.ac.in.
Scientific reports
|October 23, 2023
概括
通过智能手机发送的胸部X射线 (CXR) 分析的AI模型由于图像压缩而难以准确和可靠. 多任务学习 (MTL) 显著提高了这些AI诊断工具的可预测性和可解释性.
科学领域:
- 医学成像分析 医学成像分析
- 医疗保健中的人工智能
- 数字健康数字健康
背景情况:
- COVID-19大流行突出了医疗保健提供方面的挑战,特别是快速胸部X射线 (CXR) 诊断的需要.
- 放射科医生减少和智能手机使用增加,为人工智能驱动的诊断工具提供了机会,这些工具使用通过智能手机传输的CXR.
- 人工智能模型性能和智能手机传输CXR的解释性方面的挑战仍未得到充分研究.
研究的目的:
- 研究智能手机图像传输和压缩对人工智能驱动的CXR诊断模型的可预测性和可解释性的影响.
- 评估多任务学习 (MTL) 在缓解这些挑战方面的有效性.
- 引入和发布WaCXR数据集,用于研究这些具体问题.
主要方法:
- 在使用原始与压缩CXR图像 (例如通过WhatsApp) 的深度学习模型中评估了预测不稳定性和可解释性恶化.
- 量化图像压缩速率,发现高达95%的显著降低.
- 实施和评估多任务学习 (MTL) 模型,以提高可靠性和可解释性.
主要成果:
- 现有的深度学习模型显示了压缩CXR的预测不稳定性和减少可解释性,通常依赖于虚假的相关性.
- 与基线模型相比,MTL模型的稳定性提高了40%,可解释性提高了35%.
- 创建并发布了包含6562个原始/压缩CXR对的WaCXR数据集.
结论:
- 智能手机介导的CXR传输显著影响AI诊断工具的性能,影响由于图像压缩而导致的可预测性和可解释性.
- 多任务学习 (MTL) 提供了一个强大的解决方案,提高了医疗图像分析人工智能模型的准确性和可解释性.
- 开源的WaCXR数据集和实现将促进对资源有限的环境中可靠的AI诊断的进一步研究.
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