An artificial intelligence system for predicting mortality in COVID-19 patients using chest X-rays: a retrospective study
Abstract
Background
Early prediction of disease severity in COVID-19 patients is essential. Chest X-ray (CXR) is a faster, widely available, and less expensive imaging modality that may be useful in predicting mortality in COVID-19 patients. Artificial Intelligence (AI) may help expedite CXR reading times, and improve mortality prediction. We sought to develop and assess an artificial intelligence system that used chest X-rays and clinical parameters to predict mortality in COVID-19 patients.
Methods
A retrospective study was conducted in Ruby Hall Clinic, Pune, India. The study included patients who had a positive real-time reverse transcriptase-polymerase chain reaction (RT-PCR) test for COVID-19 and at least one available chest X-ray at the time of their initial presentation or admission. Features from CXR images and clinical parameters were used to train the Random Forest model.
Results
Clinical data from a total of 201 patients was assessed retrospectively. The average age of the cohort was 51.4±14.8 years, with 29.4% of the patients being over the age of 60. The model, which used CXRs and clinical parameters as inputs, had a sensitivity of 0.83 [95% CI: 0.7, 0.95] and a specificity of 0.7 [95% CI: 0.64, 0.77]. The area under the curve (AUC) on receiver operating characteristics (ROC) was increased from 0.74 [95% CI: 0.67, 0.8] to 0.79 [95% CI: 0.72, 0.85] when the model included features of CXRs in addition to clinical parameters.
Conclusion
An Artificial Intelligence (AI) model based on CXRs and clinical parameters demonstrated high sensitivity and can be used as a rapid and reliable tool for COVID-19 mortality prediction.
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