Tenda Eric Daniel, Yunus Reyhan Eddy, Zulkarnaen Benny, Yugo Muhammad Reynalzi, Pitoyo Ceva Wicaksono, Asaf Moses Mazmur, Islamiyati Tiara Nur, Pujitresnani Arierta, Setiadharma Andry, Henrina Joshua, Rumende Cleopas Martin, Wulani Vally, Harimurti Kuntjoro, Lydia Aida, Shatri Hamzah, Soewondo Pradana, Yusuf Prasandhya Astagiri
Department of Internal Medicine, Pulmonology and Critical Care Division, Faculty of Medicine Universitas Indonesia, RSUPN Dr. Cipto Mangunkusumo, Universitas Indonesia, Jakarta, Indonesia.
Department of Radiology, RSUPN Dr. Cipto Mangunkusumo, Universitas Indonesia, Jakarta, Indonesia.
JMIR Form Res. 2024 Mar 7;8:e46817. doi: 10.2196/46817.
The artificial intelligence (AI) analysis of chest x-rays can increase the precision of binary COVID-19 diagnosis. However, it is unknown if AI-based chest x-rays can predict who will develop severe COVID-19, especially in low- and middle-income countries.
The study aims to compare the performance of human radiologist Brixia scores versus 2 AI scoring systems in predicting the severity of COVID-19 pneumonia.
We performed a cross-sectional study of 300 patients suspected with and with confirmed COVID-19 infection in Jakarta, Indonesia. A total of 2 AI scores were generated using CAD4COVID x-ray software.
The AI probability score had slightly lower discrimination (area under the curve [AUC] 0.787, 95% CI 0.722-0.852). The AI score for the affected lung area (AUC 0.857, 95% CI 0.809-0.905) was almost as good as the human Brixia score (AUC 0.863, 95% CI 0.818-0.908).
The AI score for the affected lung area and the human radiologist Brixia score had similar and good discrimination performance in predicting COVID-19 severity. Our study demonstrated that using AI-based diagnostic tools is possible, even in low-resource settings. However, before it is widely adopted in daily practice, more studies with a larger scale and that are prospective in nature are needed to confirm our findings.
胸部X光片的人工智能（AI）分析可提高COVID-19二元诊断的准确性。然而，基于AI的胸部X光片能否预测谁会发展为重症COVID-19尚不清楚，尤其是在低收入和中等收入国家。
本研究旨在比较人类放射科医生的布里夏评分与两种AI评分系统在预测COVID-19肺炎严重程度方面的表现。
我们对印度尼西亚雅加达300例疑似或确诊COVID-19感染的患者进行了横断面研究。使用CAD4COVID x光软件生成了2种AI评分。
AI概率评分的辨别力略低（曲线下面积[AUC]为0.787，95%CI为0.722-0.852）。受影响肺区的AI评分（AUC为0.857，95%CI为0.809-0.905）与人类布里夏评分（AUC为0.863，95%CI为0.818-0.908）几乎一样好。
受影响肺区的AI评分和人类放射科医生的布里夏评分在预测COVID-19严重程度方面具有相似且良好的辨别性能。我们的研究表明，即使在资源匮乏的环境中，使用基于AI的诊断工具也是可行的。然而，在其被广泛应用于日常实践之前，需要更多大规模的前瞻性研究来证实我们的发现。
