Artificial intelligence-based model for predicting pulmonary arterial hypertension on chest x-ray images

Pulmonary arterial hypertension is a serious medical condition. However, the condition is often misdiagnosed or a rather long delay occurs from symptom onset to diagnosis, associated with decreased 5-year survival. In this study, we developed and tested a deep-learning algorithm to detect pulmonary...

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Bibliographic Details
Published in:	BMC pulmonary medicine Vol. 24; no. 1; p. 101
Main Authors:	Imai, Shun, Sakao, Seiichiro, Nagata, Jun, Naito, Akira, Sekine, Ayumi, Sugiura, Toshihiko, Shigeta, Ayako, Nishiyama, Akira, Yokota, Hajime, Shimizu, Norihiro, Sugawara, Takeshi, Nomi, Toshiaki, Honda, Seiwa, Ogaki, Keisuke, Tanabe, Nobuhiro, Baba, Takayuki, Suzuki, Takuji
Format:	Journal Article
Language:	English
Published:	England BioMed Central Ltd 27-02-2024 BioMed Central BMC
Subjects:	Algorithms Artificial intelligence Care and treatment Catheters Chest Chest X-ray Datasets Deep learning Diagnosis Family physicians Health aspects Hospitals Hypertension Learning Males Patients Pneumonia Pulmonary arterial hypertension Pulmonary hypertension Stochasticity Teaching methods Tuberculosis Japan Deep learning Pulmonary arterial hypertension Chest X-ray Artificial intelligence
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Summary:	Pulmonary arterial hypertension is a serious medical condition. However, the condition is often misdiagnosed or a rather long delay occurs from symptom onset to diagnosis, associated with decreased 5-year survival. In this study, we developed and tested a deep-learning algorithm to detect pulmonary arterial hypertension using chest X-ray (CXR) images. From the image archive of Chiba University Hospital, 259 CXR images from 145 patients with pulmonary arterial hypertension and 260 CXR images from 260 control patients were identified; of which 418 were used for training and 101 were used for testing. Using the testing dataset for each image, the algorithm outputted a numerical value from 0 to 1 (the probability of the pulmonary arterial hypertension score). The training process employed a binary cross-entropy loss function with stochastic gradient descent optimization (learning rate parameter, α = 0.01). In addition, using the same testing dataset, the algorithm's ability to identify pulmonary arterial hypertension was compared with that of experienced doctors. The area under the curve (AUC) of the receiver operating characteristic curve for the detection ability of the algorithm was 0.988. Using an AUC threshold of 0.69, the sensitivity and specificity of the algorithm were 0.933 and 0.982, respectively. The AUC of the algorithm's detection ability was superior to that of the doctors. The CXR image-derived deep-learning algorithm had superior pulmonary arterial hypertension detection capability compared with that of experienced doctors.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1471-2466 1471-2466
DOI:	10.1186/s12890-024-02891-4