用深度学习从腹部CT中自动发现漏诊疾病，提升精准医疗水平
Automated detection of underdiagnosed medical conditions via opportunistic imaging
- 利用深度学习分析住院患者CT影像，挖掘潜在疾病特征
- 仅0.5%~30.7%的相应病例被正确编码，显示大量漏诊
- 适合临床医生与数据科学家关注医学影像自动化诊断
腹部计算机断层扫描（CT）在临床中频繁使用。机会性CT通过重用常规CT图像提取诊断信息，成为发现如肌少症、脂肪肝和腹水等漏诊疾病的新工具。本研究采用深度学习方法提升诊断准确性和临床记录质量。分析2,674例住院患者CT扫描，比较由机会性CT识别出的影像表型与放射科报告及ICD编码之间的差异。结果显示，仅0.5%、3.2%和30.7%的肌少症、脂肪肝和腹水病例在报告或编码中被记录。研究证明机会性CT可显著提升诊断精度和风险调整模型的准确性，推动精准医疗发展。
原文摘要 · Abstract (English)
Abdominal computed tomography (CT) scans are frequently performed in clinical settings. Opportunistic CT involves repurposing routine CT images to extract diagnostic information and is an emerging tool for detecting underdiagnosed conditions such as sarcopenia, hepatic steatosis, and ascites. This study utilizes deep learning methods to promote accurate diagnosis and clinical documentation. We analyze 2,674 inpatient CT scans to identify discrepancies between imaging phenotypes (characteristics derived from opportunistic CT scans) and their corresponding documentation in radiology reports and ICD coding. Through our analysis, we find that only 0.5%, 3.2%, and 30.7% of scans diagnosed with sarcopenia, hepatic steatosis, and ascites (respectively) through either opportunistic imaging or radiology reports were ICD-coded. Our findings demonstrate opportunistic CT's potential to enhance diagnostic precision and accuracy of risk adjustment models, offering advancements in precision medicine.
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