ISMRM25 - AI: Diagnostic Models

ISMRM & ISMRT Annual Meeting & Exhibition • 10-15 May 2025 • Honolulu, Hawai'i

AI: Diagnostic Models

Oral

AI & Machine Learning

Tuesday, 13 May 2025

310 (Lili-u Theater)

08:15 - 10:15

Moderators: Hina Arif Tiwari & Sarah Eskreis-Winkler

Session Number: O-13

No CME/CE Credit

08:15		0373. Pathology-Guided AI System for Accurate Segmentation and Diagnosis of Cervical Spondylosis in MR T2 Images PDF Poster View Presentation Video Q. Zhang, X. Chen, L. Wu, K. Wang, J. Sun, H. Shen Shanghai Jiao Tong University, Shanghai, China Impact: This AI-based framework enhances precision and efficiency in cervical spondylosis diagnosis, reducing clinician workload and improving diagnostic accuracy, with PG-nnUNet supporting consistent, automated decision-making. Future efforts will focus on multimodal imaging for broader applicability.
08:27		0374. Multi-Stage Deep Learning Enables Accurate Detection of Ischemia in Myocardial Perfusion MRI with Order-of-magnitude Lower Contrast Dose View Presentation Video K. Youssef, L. Zamudio, B. Heydari, A. Howarth, B. Sharif Indiana University School of Medicine, Indianapolis, United States Impact: The MST deep learning method enables accurate ischemia detection in stress CMR with significantly reduced gadolinium doses, enhancing patient safety and reducing costs. This advancement could facilitate safer, more accessible stress CMR protocols in clinical practice.
08:39		0375. The deep topology of glioma PDF Poster View Presentation Video J. Ruffle, S. Mohinta, R. Gray, C. Foulon, S. Brandner, H. Hyare, P. Nachev UCL Queen Square Institute of Neurology, London, United Kingdom Impact: These works illustrate the benefit of computational modelling across clinical neuro-oncological imaging data in patient-personalised care, including diagnostic and outcome prediction, paving the way for future research and clinical translation.
08:51		0376. Vision Mamba for Liver Tumor Diagnosis in Multi-phase Magnetic Resonance Imaging View Presentation Video H. Kang, R. Jiang, J. Xu, Q. Shen, W. Chen The Chinese University of Hong Kong, Hong Kong, Hong Kong Impact: Our proposed deep learning method can diagnose most primary tumors with high accuracy. It has the potential to benefit treatment planning and improve patient outcomes.
09:03		0377. Tumor likelihood estimation on MRI prostate data by utilizing k-Space information View Presentation Video M. Rempe, F. Hörst, C. Seibold, B. Hadaschik, M. Schlimbach, J. Egger, K. Kröninger, F. Breuer, M. Blaimer, J. Kleesiek Institute for AI in medicine, Essen, Germany Impact: This study enables faster, reliable MRI-based prostate cancer predictions by utilizing k-Space raw data. It opens new possibilities for real-time diagnostics and broader applications of raw MRI data across clinical imaging.
09:15		0378. Discriminative Feature Learning for Lacune Detection in 2D T2-FLAIR Images using Supervised Contrastive Learning View Presentation Video S. H. Kim, C. H. Suh, M. W. Han, W. Jung, S. H. Lee VUNO Inc., Seoul, Korea, Republic of Impact: This work demonstrates an effective encoder training strategy for distinguishing small lesions like lacunes in cerebral small vessel disease through enhanced feature discrimination, potentially reducing both radiological interpretation time and inter-reader variability.
09:27		0379. Multi-Stage Deep Learning Architecture for Carotid Artery Segmentation and Stenosis Degree Evaluation: A Comparative Study with DSA Z. Zheng, X. Cao, Q. Yang, W. Liu, D. Geng Academy for Engineering and Technology, Fudan University, Shanghai, China Impact: This pipeline demonstrates high concordance with DSA and could significantly enhance cardiovascular risk assessment and atherosclerotic disease diagnosis in a non-invasive, radiation-free manner. Its clinical implementation may streamline diagnostic workflows and aid in the management of carotid artery disease.
09:39		0380. Deep learning-based computer-aided diagnostic system for lumbar degenerative diseases classification using MRI Y. Chen, Q. Huang, C. Zhang, J. Li, W. Huang, P. Luo, Q. Chen, R. Qi, Y. Wan, B. Huang, Z. Gao, X. Lin, S. Wu, X. Diao Shenzhen Traditional Chinese Medicine Hospital (The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine), Shenzhen, China Impact: Our study demonstrates the feasibility of using deep learning to classify multiple lumbar spine diseases with strong performance, highlighting the potential of our CAD system to reduce physician workload in clinical applications.
09:51		0381. Radio-pathomic maps of histo-morphometric features trained with whole mount prostate histology distinguish prostate cancer on MP-MRI View Presentation Video S. Duenweg, S. Bobholz, A. Lowman, A. Winiarz, B. Nath, B. Chao, S. Vincent-Sheldon, K. Bhatt, L. Chaudhary, K. Troy, K. Iczkowski, K. Jacobsohn, P. LaViolette Medical College of Wisconsin, Wauwatosa, United States Impact: This innovative approach uses radio-pathomic mapping for non-invasive prostate cancer detection, offering a quantitative alternative to PI-RADS scoring, enhanced cancer localization, and potentially improving diagnosis, grading, and treatment planning for prostate cancer patients.
10:03		0382. A Fully Automated Deep-Learning Model for Differentiating Diffuse Gliomas and Circumscribed Astrocytic Gliomas: A Multi-center Study View Presentation Video S. Li, Q. Yue West China Hospital of Sichuan university, Chengdu, China Impact: The integrated deep learning framework demonstrates robust performance in segmenting and differentiating diffuse gliomas and circumscribed astrocytic gliomas across multi-institutional datasets. Notably, the system significantly enhanced the preoperative diagnostic performance of radiologists across all experience levels.

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