دورية أكاديمية
Artificial intelligence-based model for predicting pulmonary arterial hypertension on chest x-ray images
| العنوان: | Artificial intelligence-based model for predicting pulmonary arterial hypertension on chest x-ray images |
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| المؤلفون: | Shun Imai, Seiichiro Sakao, Jun Nagata, Akira Naito, Ayumi Sekine, Toshihiko Sugiura, Ayako Shigeta, Akira Nishiyama, Hajime Yokota, Norihiro Shimizu, Takeshi Sugawara, Toshiaki Nomi, Seiwa Honda, Keisuke Ogaki, Nobuhiro Tanabe, Takayuki Baba, Takuji Suzuki |
| المصدر: | BMC Pulmonary Medicine, Vol 24, Iss 1, Pp 1-7 (2024) |
| بيانات النشر: | BMC, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Diseases of the respiratory system |
| مصطلحات موضوعية: | Pulmonary arterial hypertension, Artificial intelligence, Deep learning, Chest X-ray, Diseases of the respiratory system, RC705-779 |
| الوصف: | Abstract Background 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. Methods 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. Results 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. Conclusion The CXR image-derived deep-learning algorithm had superior pulmonary arterial hypertension detection capability compared with that of experienced doctors. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1471-2466 01019554 |
| العلاقة: | https://doaj.org/toc/1471-2466Test |
| DOI: | 10.1186/s12890-024-02891-4 |
| الوصول الحر: | https://doaj.org/article/b3fd010195544205a3065e7685395f29Test |
| رقم الانضمام: | edsdoj.b3fd010195544205a3065e7685395f29 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 14712466 01019554 |
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| DOI: | 10.1186/s12890-024-02891-4 |