Quantification, model characterization, and challenges in automatic perivascular space candidate discrimination

Jordan D. Fuhrman; Madison Luther; Ali Mansour; Laura Dennis; Fernando D. Goldenberg; Maryellen L. Giger; Juan Piantino

doi:10.1117/12.3006493

3 April 2024 Quantification, model characterization, and challenges in automatic perivascular space candidate discrimination

Jordan D. Fuhrman, Madison Luther, Ali Mansour, Laura Dennis, Fernando D. Goldenberg, Maryellen L. Giger, Juan Piantino

Author Affiliations +

Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 129272G (2024) https://doi.org/10.1117/12.3006493
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

In recent years, there has been significant interest in evaluating perivascular spaces (PVS) due to their potential to characterize multiple neurological conditions. In this study, we demonstrated the potential to improve PVS evaluation at scale by introducing an AI algorithm to review identified PVS candidates and remove false positives on T2-weighted MRI. For this task, we were able to achieve an AUC of 0.93 +/- 0.02 while identifying optimal model characteristics and exploring areas of future improvement and investigation, thus demonstrating the potential for AI to replace human review in PVS quantification at scale.

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Jordan D. Fuhrman, Madison Luther, Ali Mansour, Laura Dennis, Fernando D. Goldenberg, Maryellen L. Giger, and Juan Piantino "Quantification, model characterization, and challenges in automatic perivascular space candidate discrimination", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 129272G (3 April 2024); https://doi.org/10.1117/12.3006493

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