Department of Cardiology, Mersin University Medical Faculty, Mersin, Turkey.
Department of Radiology, Mersin University Medical Faculty, Mersin, Turkey.
Clin Cardiol. 2023 Dec;46(12):1562-1568. doi: 10.1002/clc.24105. Epub 2023 Aug 31.
Typical signs of heart failure (HF), like increased cardiothoracic ratio (CTR) and pleural effusion, can be seen on X-ray. Artificial Intelligence (AI) can help in the early and quicker diagnosis of HF.
The study's goal was to demonstrate that the AI interpretation of chest X-rays can assist the clinician in diagnosing HF.
Patients older than 45 years were included in the study. The study analyzed 10 100 deidentified outpatient chest X-rays by AI algorithm. The AI-generated report was later verified by an independent radiologist. Patients with CTR > 0.5 and pleural effusion were marked as potential HF. Flagged patients underwent confirmatory tests, and those labeled as negative also underwent further investigations to rule out HF.
Out of 10 100, the AI algorithm detected 183 (1.8%) patients with increased CTR and pleural effusion on chest X-rays. One hundred and six out of 183 underwent diagnostic tests. Eighty-two (77%) out of 106 were diagnosed with HF according to current guidelines. From the remaining 9917 patients, 106 patients were randomly selected. Nine (8%) out of them were diagnosed with HF. The positive predictive value of AI for diagnosing HF is 77%, and the negative predictive value is 91%. More than half (54.9%) of newly diagnosed patients had HF with preserved ejection fraction.
HF is a risky condition with nonspecific symptoms that are difficult to diagnose, especially in the early stages. Using AI assistance for X-ray interpretation can be helpful for early diagnosis of HF especially HF with preserved ejection fraction.
心力衰竭（HF）的典型症状，如心胸比（CTR）增加和胸腔积液，可在 X 光片上看到。人工智能（AI）可以帮助早期更快地诊断 HF。
本研究的目的是证明 AI 对 X 光胸片的解读可以帮助临床医生诊断 HF。
该研究纳入了年龄大于 45 岁的患者。该研究通过 AI 算法分析了 10100 张匿名门诊 X 光胸片。随后，由一名独立放射科医生对 AI 生成的报告进行验证。将 CTR>0.5 和胸腔积液的患者标记为潜在 HF。标记的患者接受了确认性检查，而标记为阴性的患者也接受了进一步的检查以排除 HF。
在 10100 例中，AI 算法在胸部 X 光片上检测到 183 例（1.8%） CTR 增加和胸腔积液的患者。其中 183 例中有 106 例接受了诊断性检查。根据现行指南，106 例中有 82 例（77%）被诊断为 HF。在其余的 9917 例患者中，随机选择了 106 例。其中 9 例（8%）被诊断为 HF。AI 对 HF 诊断的阳性预测值为 77%，阴性预测值为 91%。超过一半（54.9%）新诊断的患者为射血分数保留的 HF。
HF 是一种风险较大的疾病，其症状不特异，难以诊断，尤其是在早期阶段。使用 AI 辅助 X 光解读有助于早期诊断 HF，尤其是射血分数保留的 HF。
