Dataset Information

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

In external validation, the sensitivity and specificity of the model were 0.869 and 0.432, at the low-level cutoff, 0.724 and 0.721, at the high-level cutoff. Area under the receiver operating characteristic was 0.76.

Conclusions

Our machine learning model exhibited a high sensitivity in external validation datasets and may assist physicians to rule out COVID-19 diagnosis in a timely manner at emergency departments. Further studies are warranted to improve model specificity.

SUBMITTER: Kataoka Y

PROVIDER: S-EPMC10760489 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

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

Kataoka Yuki Y Baba Tomohisa T Ikenoue Tatsuyoshi T Matsuoka Yoshinori Y Matsumoto Junichi J Kumasawa Junji J Tochitani Kentaro K Funakoshi Hiraku H Hosoda Tomohiro T Kugimiya Aiko A Shirano Michinori M Hamabe Fumiko F Iwata Sachiyo S Kitamura Yoshiro Y Goto Tsubasa T Hamaguchi Shingo S Haraguchi Takafumi T Yamamoto Shungo S Sumikawa Hiromitsu H Nishida Koji K Nishida Haruka H Ariyoshi Koichi K Sugiura Hiroaki H Nakagawa Hidenori H Asaoka Tomohiro T Yoshida Naofumi N Oda Rentaro R Koyama Takashi T Iwai Yui Y Miyashita Yoshihiro Y Okazaki Koya K Tanizawa Kiminobu K Handa Tomohiro T Kido Shoji S Fukuma Shingo S Tomiyama Noriyuki N Hirai Toyohiro T Ogura Takashi T

Annals of clinical epidemiology 20220708 4

<h4>Background</h4>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).<h4>Methods</h4>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 c ...[more]

PMID: 38505255

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Project description:BackgroundAccurate diagnosis of pneumonia is vital for effective disease management and mortality reduction, but it can be easily confused with other conditions on chest computed tomography (CT) due to an overlap in imaging features. We aimed to develop and validate a deep learning (DL) model based on chest CT for accurate classification of viral pneumonia (VP), bacterial pneumonia (BP), fungal pneumonia (FP), pulmonary tuberculosis (PTB), and no pneumonia (NP) conditions.MethodsIn total, 1,776 cases from five hospitals in different regions were retrospectively collected from September 2019 to June 2023. All cases were enrolled according to inclusion and exclusion criteria, and ultimately 1,611 cases were used to develop the DL model with 5-fold cross-validation, with 165 cases being used as the external test set. Five radiologists blindly reviewed the images from the internal and external test sets first without and then with DL model assistance. Precision, recall, F1-score, weighted F1-average, and area under the curve (AUC) were used to evaluate the model performance.ResultsThe F1-scores of the DL model on the internal and external test sets were, respectively, 0.947 [95% confidence interval (CI): 0.936-0.958] and 0.933 (95% CI: 0.916-0.950) for VP, 0.511 (95% CI: 0.487-0.536) and 0.591 (95% CI: 0.557-0.624) for BP, 0.842 (95% CI: 0.824-0.860) and 0.848 (95% CI: 0.824-0.873) for FP, 0.843 (95% CI: 0.826-0.861) and 0.795 (95% CI: 0.767-0.822) for PTB, and 0.975 (95% CI: 0.968-0.983) and 0.976 (95% CI: 0.965-0.986) for NP, with a weighted F1-average of 0.883 (95% CI: 0.867-0.898) and 0.846 (95% CI: 0.822-0.871), respectively. The model performed well and showed comparable performance in both the internal and external test sets. The F1-score of the DL model was higher than that of radiologists, and with DL model assistance, radiologists achieved a higher F1-score. On the external test set, the F1-score of the DL model (F1-score 0.848; 95% CI: 0.824-0.873) was higher than that of the radiologists (F1-score 0.541; 95% CI: 0.507-0.575) as was its precision for the other three pneumonia conditions (all P values <0.001). With DL model assistance, the F1-score for FP (F1-score 0.541; 95% CI: 0.507-0.575) was higher than that achieved without assistance (F1-score 0.778; 95% CI: 0.750-0.807) as was its precision for the other three pneumonia conditions (all P values <0.001).ConclusionsThe DL approach can effectively classify pneumonia and can help improve radiologists' performance, supporting the full integration of DL results into the routine workflow of clinicians.

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Dataset Information

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

Background

Methods

Results

Conclusions

Publications

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

Similar Datasets

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