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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.18614 |
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| _version_ | 1866913671662272512 |
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| author | Chen, Xu Huang, Yuan Jessney, Benn Sangha, Jason Gu, Sophie Schönlieb, Carola-Bibiane Bennett, Martin Roberts, Michael |
| author_facet | Chen, Xu Huang, Yuan Jessney, Benn Sangha, Jason Gu, Sophie Schönlieb, Carola-Bibiane Bennett, Martin Roberts, Michael |
| contents | Artificial intelligence (AI) methodologies hold great promise for the rapid and accurate diagnosis of coronary artery disease (CAD) from intravascular optical coherent tomography (IVOCT) images. Numerous papers have been published describing AI-based models for different diagnostic tasks, yet it remains unclear which models have potential clinical utility and have been properly validated. This systematic review considered published literature between January 2015 and February 2023 describing AI-based diagnosis of CAD using IVOCT. Our search identified 5,576 studies, with 513 included after initial screening and 35 studies included in the final systematic review after quality screening. Our findings indicate that most of the identified models are not currently suitable for clinical use, primarily due to methodological flaws and underlying biases. To address these issues, we provide recommendations to improve model quality and research practices to enhance the development of clinically useful AI products. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_18614 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Review and Recommendations for using Artificial Intelligence in Intracoronary Optical Coherence Tomography Analysis Chen, Xu Huang, Yuan Jessney, Benn Sangha, Jason Gu, Sophie Schönlieb, Carola-Bibiane Bennett, Martin Roberts, Michael Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Artificial intelligence (AI) methodologies hold great promise for the rapid and accurate diagnosis of coronary artery disease (CAD) from intravascular optical coherent tomography (IVOCT) images. Numerous papers have been published describing AI-based models for different diagnostic tasks, yet it remains unclear which models have potential clinical utility and have been properly validated. This systematic review considered published literature between January 2015 and February 2023 describing AI-based diagnosis of CAD using IVOCT. Our search identified 5,576 studies, with 513 included after initial screening and 35 studies included in the final systematic review after quality screening. Our findings indicate that most of the identified models are not currently suitable for clinical use, primarily due to methodological flaws and underlying biases. To address these issues, we provide recommendations to improve model quality and research practices to enhance the development of clinically useful AI products. |
| title | Review and Recommendations for using Artificial Intelligence in Intracoronary Optical Coherence Tomography Analysis |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2501.18614 |