Prediction of COVID-19 with Statistical Data on Chest Radiography using Artificial Intelligence - Kaewlek, Titipong; Sakaekhum, Waritsara; Promton, Warisa; Tharama, Areeya; Chusin, Thunyarat; Yabsantia, Sumalee; Udee, Nuntawat (Asian Medical Journal and Alternative Medicine, 2024)
Introduction: COVID-19 is rapidly spreading around the world and has a high mortality rate. Artificial intelligence (AI) technology is a method that can be used to diagnose the presence of COVID-19 via chest radiographic apparatus. AI can be found to provide accurate results and increased diagnostic efficiency. Objectives: To evaluate the efficacy of artificial intelligence for COVID-19 diagnosis using statistical data from radiographic chest images. Methods: The research population sample consisted of 10,000 normal heathy individuals and 10,000 COVID-19 chest radiographs of patients were used for training (70.0%), validating (20.0%), and testing (10.0%). The images were segmented into the left and right lung regions by using the U-net architecture and then statistical data was calculated, including integrated density, mean, standard deviation, skewness, and kurtosis. Three artificial intelligence methods (support vector machine, K-mean clustering, and restricted Boltzmann machine) were compared the models’ predictions. The performance of three methods were analyzed for accuracy, sensitivity, specificity, precision, and F1-score. Results: The accuracy of the support vector machine, K-mean clustering, and restricted Boltzmann machine were 70.5%, 62.5%, and 63.2%, respectively. The trend of the sensitivity, specificity, precision, and F1-score were similar in terms of accuracy, sensitivity, specificity, precision, and F1-score of the support vector machine, which were 64.2%, 73.5%, 68.2%, and 68.5%, respectively. Conclusions: The most successful technique for diagnosing COVID-19 from chest radiographs was the support vector machine. It outperformed the restricted Boltzmann machine, which was followed by K-mean clustering