Recalibration of deep learning models for abnormality detection in smartphone-captured chest radiograph

Abstract Image-based teleconsultation using smartphones has become increasingly popular. In parallel, deep learning algorithms have been developed to detect radiological findings in chest X-rays (CXRs). However, the feasibility of using smartphones to automate this process has yet to be evaluated. T...

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Autores principales:	Po-Chih Kuo, Cheng Che Tsai, Diego M. López, Alexandros Karargyris, Tom J. Pollard, Alistair E. W. Johnson, Leo Anthony Celi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/69292020cb6c4ee48dd996efb58bb8fb
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spelling	oai:doaj.org-article:69292020cb6c4ee48dd996efb58bb8fb2021-12-02T14:21:51ZRecalibration of deep learning models for abnormality detection in smartphone-captured chest radiograph10.1038/s41746-021-00393-92398-6352https://doaj.org/article/69292020cb6c4ee48dd996efb58bb8fb2021-02-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00393-9https://doaj.org/toc/2398-6352Abstract Image-based teleconsultation using smartphones has become increasingly popular. In parallel, deep learning algorithms have been developed to detect radiological findings in chest X-rays (CXRs). However, the feasibility of using smartphones to automate this process has yet to be evaluated. This study developed a recalibration method to build deep learning models to detect radiological findings on CXR photographs. Two publicly available databases (MIMIC-CXR and CheXpert) were used to build the models, and four derivative datasets containing 6453 CXR photographs were collected to evaluate model performance. After recalibration, the model achieved areas under the receiver operating characteristic curve of 0.80 (95% confidence interval: 0.78–0.82), 0.88 (0.86–0.90), 0.81 (0.79–0.84), 0.79 (0.77–0.81), 0.84 (0.80–0.88), and 0.90 (0.88–0.92), respectively, for detecting cardiomegaly, edema, consolidation, atelectasis, pneumothorax, and pleural effusion. The recalibration strategy, respectively, recovered 84.9%, 83.5%, 53.2%, 57.8%, 69.9%, and 83.0% of performance losses of the uncalibrated model. We conclude that the recalibration method can transfer models from digital CXRs to CXR photographs, which is expected to help physicians’ clinical works.Po-Chih KuoCheng Che TsaiDiego M. LópezAlexandros KarargyrisTom J. PollardAlistair E. W. JohnsonLeo Anthony CeliNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Po-Chih Kuo Cheng Che Tsai Diego M. López Alexandros Karargyris Tom J. Pollard Alistair E. W. Johnson Leo Anthony Celi Recalibration of deep learning models for abnormality detection in smartphone-captured chest radiograph
description	Abstract Image-based teleconsultation using smartphones has become increasingly popular. In parallel, deep learning algorithms have been developed to detect radiological findings in chest X-rays (CXRs). However, the feasibility of using smartphones to automate this process has yet to be evaluated. This study developed a recalibration method to build deep learning models to detect radiological findings on CXR photographs. Two publicly available databases (MIMIC-CXR and CheXpert) were used to build the models, and four derivative datasets containing 6453 CXR photographs were collected to evaluate model performance. After recalibration, the model achieved areas under the receiver operating characteristic curve of 0.80 (95% confidence interval: 0.78–0.82), 0.88 (0.86–0.90), 0.81 (0.79–0.84), 0.79 (0.77–0.81), 0.84 (0.80–0.88), and 0.90 (0.88–0.92), respectively, for detecting cardiomegaly, edema, consolidation, atelectasis, pneumothorax, and pleural effusion. The recalibration strategy, respectively, recovered 84.9%, 83.5%, 53.2%, 57.8%, 69.9%, and 83.0% of performance losses of the uncalibrated model. We conclude that the recalibration method can transfer models from digital CXRs to CXR photographs, which is expected to help physicians’ clinical works.
format	article
author	Po-Chih Kuo Cheng Che Tsai Diego M. López Alexandros Karargyris Tom J. Pollard Alistair E. W. Johnson Leo Anthony Celi
author_facet	Po-Chih Kuo Cheng Che Tsai Diego M. López Alexandros Karargyris Tom J. Pollard Alistair E. W. Johnson Leo Anthony Celi
author_sort	Po-Chih Kuo
title	Recalibration of deep learning models for abnormality detection in smartphone-captured chest radiograph
title_short	Recalibration of deep learning models for abnormality detection in smartphone-captured chest radiograph
title_full	Recalibration of deep learning models for abnormality detection in smartphone-captured chest radiograph
title_fullStr	Recalibration of deep learning models for abnormality detection in smartphone-captured chest radiograph
title_full_unstemmed	Recalibration of deep learning models for abnormality detection in smartphone-captured chest radiograph
title_sort	recalibration of deep learning models for abnormality detection in smartphone-captured chest radiograph
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/69292020cb6c4ee48dd996efb58bb8fb
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Recalibration of deep learning models for abnormality detection in smartphone-captured chest radiograph

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