Integrated model for COVID-19 diagnosis based on computed tomography artificial intelligence, and clinical features: a multicenter cohort study.
Ann Transl Med
; 10(3): 130, 2022 Feb.
Article
in English
| MEDLINE | ID: covidwho-1687683
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 73 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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Cohort study
/
Experimental Studies
/
Observational study
/
Prognostic study
Language:
English
Journal:
Ann Transl Med
Year:
2022
Document Type:
Article
Affiliation country:
Atm-21-5571
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