Academic Journal
A transparent artificial intelligence framework to assess lung disease in pulmonary hypertension
| العنوان: | A transparent artificial intelligence framework to assess lung disease in pulmonary hypertension |
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| المؤلفون: | Michail Mamalakis, Krit Dwivedi, Michael Sharkey, Samer Alabed, David Kiely, Andrew J. Swift |
| المصدر: | Scientific Reports, Vol 13, Iss 1, Pp 1-17 (2023) |
| بيانات النشر: | Nature Portfolio, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Medicine LCC:Science |
| مصطلحات موضوعية: | Medicine, Science |
| الوصف: | Abstract Recent studies have recognized the importance of characterizing the extent of lung disease in pulmonary hypertension patients by using Computed Tomography. The trustworthiness of an artificial intelligence system is linked with the depth of the evaluation in functional, operational, usability, safety and validation dimensions. The safety and validation of an artificial tool is linked to the uncertainty estimation of the model’s prediction. On the other hand, the functionality, operation and usability can be achieved by explainable deep learning approaches which can verify the learning patterns and use of the network from a generalized point of view. We developed an artificial intelligence framework to map the 3D anatomical models of patients with lung disease in pulmonary hypertension. To verify the trustworthiness of the framework we studied the uncertainty estimation of the network’s prediction, and we explained the learning patterns of the network. Therefore, a new generalized technique combining local explainable and interpretable dimensionality reduction approaches (PCA-GradCam, PCA-Shape) was developed. Our open-source software framework was evaluated in unbiased validation datasets achieving accurate, robust and generalized results. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2045-2322 81879458 |
| Relation: | https://doaj.org/toc/2045-2322 |
| DOI: | 10.1038/s41598-023-30503-4 |
| URL الوصول: | https://doaj.org/article/7b81879458204bb3a1be2c29fde0e948 |
| رقم الانضمام: | edsdoj.7b81879458204bb3a1be2c29fde0e948 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 20452322 81879458 |
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| DOI: | 10.1038/s41598-023-30503-4 |