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

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.

Informations de copyright

2022 Annals of Translational Medicine. All rights reserved.

Déclaration de conflit d'intérêts

Conflicts of Interest: All authors have completed the ICMJE unified disclosure form (available at https://atm.amegroups.com/article/view/10.21037/atm-21-5571/coif). Yuki Kataoka serves as an unpaid editorial board member of Annals of Translational Medicine from July 2020 to June 2022. Yuki Kataoka, Yuya Kimura, TI, YM, JM, JK, KT, HF, TH, AK, FH, SI, SF report that this study was partially supported by Kyoto University managing fund for English editing; the article processing fee was supported by the Scientific Research Works Peer Support Group (SRWS-PSG); M3 Inc. and Clinical Porter provided free Ali-M3 analysis and data storage. TI also reports that the analysis of the CT by Ali-M3 was carried out by Nobori on behalf of M3. M3 and Nobori did not know the patients’ data including the result of RT-PCR. JM also reports that receiving a lecture fee from M3 Inc. The other author has no conflicts of interest to declare.