Development and external validation of a deep learning-based computed tomography classification system for COVID-19.

ABSTRACT:

Background

We aimed to develop and externally validate a novel machine learning model that can classify CT image findings as positive or negative for SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR).

Methods

We used 2,928 images from a wide variety of case-control type data sources for the development and internal validation of the machine learning model. A total of 633 COVID-19 cases and 2,295 non-COVID-19 cases were included in the study. We randomly divided cases into training and tuning sets at a ratio of 8:2. For external validation, we used 893 images from 740 consecutive patients at 11 acute care hospitals suspected of having COVID-19 at the time of diagnosis. The dataset included 343 COVID-19 patients. The reference standard was RT-PCR.

Results<

SUBMITTER: Kataoka Y 

PROVIDER: S-EPMC10760489 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature