GENIE-NF-AI: Identifying Neurofibromatosis Tumors using Liquid Neural Network (LTC) trained on AACR GENIE Datasets

• URL: http://arxiv.org/abs/2304.13429v1
• Date: Wed, 26 Apr 2023 10:28:59 GMT
• Title: GENIE-NF-AI: Identifying Neurofibromatosis Tumors using Liquid Neural Network (LTC) trained on AACR GENIE Datasets
• Authors: Michael Bidollahkhani, Ferhat Atasoy, Elnaz Abedini, Ali Davar, Omid Hamza, F{\i}rat Sefao\u{g}lu, Amin Jafari, Muhammed Nadir Yal\c{c}{\i}n, Hamdan Abdellatef
• Abstract summary: We propose an interpretable AI approach to diagnose patients with neurofibromatosis. Our proposed approach outperformed existing models with 99.86% accuracy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In recent years, the field of medicine has been increasingly adopting artificial intelligence (AI) technologies to provide faster and more accurate disease detection, prediction, and assessment. In this study, we propose an interpretable AI approach to diagnose patients with neurofibromatosis using blood tests and pathogenic variables. We evaluated the proposed method using a dataset from the AACR GENIE project and compared its performance with modern approaches. Our proposed approach outperformed existing models with 99.86% accuracy. We also conducted NF1 and interpretable AI tests to validate our approach. Our work provides an explainable approach model using logistic regression and explanatory stimulus as well as a black-box model. The explainable models help to explain the predictions of black-box models while the glass-box models provide information about the best-fit features. Overall, our study presents an interpretable AI approach for diagnosing patients with neurofibromatosis and demonstrates the potential of AI in the medical field.

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