TY  - JOUR
AU  - Tenda, Eric Daniel
AU  - Yunus, Reyhan Eddy
AU  - Zulkarnaen, Benny
AU  - Yugo, Muhammad Reynalzi
AU  - Pitoyo, Ceva Wicaksono
AU  - Asaf, Moses Mazmur
AU  - Islamiyati, Tiara Nur
AU  - Pujitresnani, Arierta
AU  - Setiadharma, Andry
AU  - Henrina, Joshua
AU  - Rumende, Cleopas Martin
AU  - Wulani, Vally
AU  - Harimurti, Kuntjoro
AU  - Lydia, Aida
AU  - Shatri, Hamzah
AU  - Soewondo, Pradana
AU  - Yusuf, Prasandhya Astagiri
PY  - 2024
DA  - 2024/3/7
TI  - Comparison of the Discrimination Performance of AI Scoring and the Brixia Score in Predicting COVID-19 Severity on Chest X-Ray Imaging: Diagnostic Accuracy Study
JO  - JMIR Form Res
SP  - e46817
VL  - 8
KW  - artificial intelligence
KW  - Brixia
KW  - chest x-ray
KW  - COVID-19
KW  - CAD4COVID
KW  - pneumonia
KW  - radiograph
KW  - artificial intelligence scoring system
KW  - AI scoring system
KW  - prediction
KW  - disease severity
AB  - Background: 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. Objective: 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. Methods: 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. Results: 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). Conclusions: 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. 
SN  - 2561-326X
UR  - https://formative.jmir.org/2024/1/e46817
UR  - https://doi.org/10.2196/46817
UR  - http://www.ncbi.nlm.nih.gov/pubmed/38451633
DO  - 10.2196/46817
ID  - info:doi/10.2196/46817
ER  -