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

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Ann Transl Med


. 2022 Feb;10(3):130.
doi: 10.21037/atm-21-5571.
Integrated model for COVID-19 diagnosis based on computed tomography artificial intelligence, and clinical features: a multicenter cohort study


Yuki Kataoka ^{# 1 2 3 4} , Yuya Kimura ^{# 5} , Tatsuyoshi Ikenoue ^{6 7} , Yoshinori Matsuoka ^{3 8} , Junichi Matsumoto ⁹ , Junji Kumasawa ^{6 10} , Kentaro Tochitatni ¹¹ , Hiraku Funakoshi ¹² , Tomohiro Hosoda ¹³ , Aiko Kugimiya ¹⁴ , Michinori Shirano ¹⁵ , Fumiko Hamabe ¹⁶ , Sachiyo Iwata ¹⁷ , Shingo Fukuma ⁶ , Japan COVID-19 AI team*



Affiliations

• PMID: 35284557
• PMCID: PMC8904977
• DOI: 10.21037/atm-21-5571

Abstract

Background: 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.
Methods: 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.
Results: 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.
Conclusions: 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.

Keywords: COVID-19; Light Gradient Boosting Machine (LightGBM); decision support tool; diagnosis; machine learning.