Integrated model for COVID-19 diagnosis based on computed tomography artificial intelligence, and clinical features: a multicenter cohort study

• Published in: Annals of translational medicine 2022-02, Vol.10 (3), p.130-130
• Main Authors: Kataoka, Yuki, Kimura, Yuya, Ikenoue, Tatsuyoshi, Matsuoka, Yoshinori, Matsumoto, Junichi, Kumasawa, Junji, Tochitatni, Kentaro, Funakoshi, Hiraku, Hosoda, Tomohiro, Kugimiya, Aiko, Shirano, Michinori, Hamabe, Fumiko, Iwata, Sachiyo, Fukuma, Shingo
• Format: Article
• Language: English
• Subjects: Original
• ISSN: 2305-5839; 2305-5839
• DOI: 10.21037/atm-21-5571

We developed and validated a machine learning diagnostic model for the novel coronavirus (COVID-19) disease, integrating artificial-intelligence-based computed tomography (CT) imaging and clinical features. We conducted a retrospective cohort study in 11 Japanese tertiary care facilities that treated COVID-19 patients. Participants were tested using both real-time reverse transcription polymerase chain reaction (RT-PCR) and chest CTs between January 1 and May 30, 2020. We chronologically split the dataset in each hospital into training and test sets, containing patients in a 7:3 ratio. A Light Gradient Boosting Machine model was used for the analysis. A total of 703 patients were included, and two models-the full model and the A-blood model-were developed for their diagnosis. The A-blood model included eight variables (the Ali-M3 confidence, along with seven clinical features of blood counts and biochemistry markers). The areas under the receiver-operator curve of both models [0.91, 95% confidence interval (CI): 0.86 to 0.95 for the full model and 0.90, 95% CI: 0.86 to 0.94 for the A-blood model] were better than that of the Ali-M3 confidence (0.78, 95% CI: 0.71 to 0.83) in the test set. The A-blood model, a COVID-19 diagnostic model developed in this study, combines machine-learning and CT evaluation with blood test data and performs better than the Ali-M3 framework existing for this purpose. This would significantly aid physicians in making a quicker diagnosis of COVID-19.