| Autor: |
Eric Daniel Tenda, Reyhan Eddy Yunus, Benny Zulkarnaen, Muhammad Reynalzi Yugo, Ceva Wicaksono Pitoyo, Moses Mazmur Asaf, Tiara Nur Islamiyati, Arierta Pujitresnani, Andry Setiadharma, Joshua Henrina, Cleopas Martin Rumende, Vally Wulani, Kuntjoro Harimurti, Aida Lydia, Hamzah Shatri, Pradana Soewondo, Prasandhya Astagiri Yusuf |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
JMIR Formative Research, Vol 8, p e46817 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2561-326X |
| DOI: |
10.2196/46817 |
| Popis: |
BackgroundThe 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. ObjectiveThe study aims to compare the performance of human radiologist Brixia scores versus 2 AI scoring systems in predicting the severity of COVID-19 pneumonia. MethodsWe 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. ResultsThe 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). ConclusionsThe 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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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