Comp2Comp：用于计算机断层扫描图像分析的FDA批准人工智能算法开源软件
Comp2Comp: Open-Source Software with FDA-Cleared Artificial Intelligence Algorithms for Computed Tomography Image Analysis
AI总结：
Comp2Comp开源软件通过FDA批准的人工智能算法，实现了腹主动脉量化和骨密度估计的高精度临床应用。
摘要：
人工智能允许从已获得的放射学图像中自动提取影像生物标记物。这种机会性成像范式在不增加额外成像成本或患者辐射暴露的情况下增强了医学影像的价值。然而，许多开源图像分析解决方案缺乏严谨的验证，而商业解决方案缺乏透明度，导致在部署时出现意外故障。在这里，我们报告了两种首批完全开源、获得FDA 510(k)批准的深度学习流程的开发和验证，以解决这两个挑战：腹主动脉量化（AAQ）和骨矿物质密度（BMD）估计均在Comp2Comp软件包中提供，用于机会性分析计算机断层扫描扫描。AAQ对腹主动脉进行分割以评估动脉瘤大小；BMD对椎体进行分割以估计网状骨密度和骨质疏松风险。AAQ得出的最大腹主动脉直径与放射科医生的地面真实测量在258例患者扫描中进行了比较，这些扫描来自四个外部机构，富含腹主动脉瘤。BMD的二元分类（低骨密度 vs. 正常骨密度）与同时进行的DXA扫描地面真实值进行了比较，这些扫描来自四个外部机构的371例患者扫描。AAQ的总体平均绝对误差为1.57毫米（95%置信区间1.38-1.80毫米）。BMD的灵敏度为81.0%（95%置信区间74.0-86.8%），特异度为78.4%（95%置信区间72.3-83.7%）。Comp2Comp AAQ和BMD展示了足够的准确性，适用于临床使用。开源这些算法提高了通常不透明的FDA批准流程的透明度，使医院能够在繁琐的临床试点之前测试这些算法，并为研究人员提供了最佳方法。
英文摘要：
Artificial intelligence allows automatic extraction of imaging biomarkers from already-acquired radiologic images. This paradigm of opportunistic imaging adds value to medical imaging without additional imaging costs or patient radiation exposure. However, many open-source image analysis solutions lack rigorous validation while commercial solutions lack transparency, leading to unexpected failures when deployed. Here, we report development and validation for two of the first fully open-sourced, FDA-510(k)-cleared deep learning pipelines to mitigate both challenges: Abdominal Aortic Quantification (AAQ) and Bone Mineral Density (BMD) estimation are both offered within the Comp2Comp package for opportunistic analysis of computed tomography scans. AAQ segments the abdominal aorta to assess aneurysm size; BMD segments vertebral bodies to estimate trabecular bone density and osteoporosis risk. AAQ-derived maximal aortic diameters were compared against radiologist ground-truth measurements on 258 patient scans enriched for abdominal aortic aneurysms from four external institutions. BMD binary classifications (low vs. normal bone density) were compared against concurrent DXA scan ground truths obtained on 371 patient scans from four external institutions. AAQ had an overall mean absolute error of 1.57 mm (95% CI 1.38-1.80 mm). BMD had a sensitivity of 81.0% (95% CI 74.0-86.8%) and specificity of 78.4% (95% CI 72.3-83.7%). Comp2Comp AAQ and BMD demonstrated sufficient accuracy for clinical use. Open-sourcing these algorithms improves transparency of typically opaque FDA clearance processes, allows hospitals to test the algorithms before cumbersome clinical pilots, and provides researchers with best-in-class methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
