| Autor: |
Kosuke Kita, Takahito Fujimori, Yuki Suzuki, Yuya Kanie, Shota Takenaka, Takashi Kaito, Takuyu Taki, Yuichiro Ukon, Masayuki Furuya, Hirokazu Saiwai, Nozomu Nakajima, Tsuyoshi Sugiura, Hiroyuki Ishiguro, Takashi Kamatani, Hiroyuki Tsukazaki, Yusuke Sakai, Haruna Takami, Daisuke Tateiwa, Kunihiko Hashimoto, Tomohiro Wataya, Daiki Nishigaki, Junya Sato, Masaki Hoshiyama, Noriyuki Tomiyama, Seiji Okada, Shoji Kido |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
iScience, Vol 26, Iss 10, Pp 107900- (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2589-0042 |
| DOI: |
10.1016/j.isci.2023.107900 |
| Popis: |
Summary: We proposed a bimodal artificial intelligence that integrates patient information with images to diagnose spinal cord tumors. Our model combines TabNet, a state-of-the-art deep learning model for tabular data for patient information, and a convolutional neural network for images. As training data, we collected 259 spinal tumor patients (158 for schwannoma and 101 for meningioma). We compared the performance of the image-only unimodal model, table-only unimodal model, bimodal model using a gradient-boosting decision tree, and bimodal model using TabNet. Our proposed bimodal model using TabNet performed best (area under the receiver-operating characteristic curve [AUROC]: 0.91) in the training data and significantly outperformed the physicians' performance. In the external validation using 62 cases from the other two facilities, our bimodal model showed an AUROC of 0.92, proving the robustness of the model. The bimodal analysis using TabNet was effective for differentiating spinal tumors. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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