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arXiv Machine Learning By Omer Burak Demirel, Kelly K. Horst, Alessio Perazzolo, Elisa Bruno, Kenan Kaya, Rongzhen Ouyang, Enas Ahmed, Jouke Smink, Spencer L. Waddle, Zainudeen Kallumpurath, Tzu Cheng Chao, Dinghui Wang, Steve G. Langer, Timothy L. Kline, Panagiotis Korfiatis, Jacinta Browne, Ivana Isgum, Tim Leiner

ORION-CMR: On-scanner Reporting with Integrated Foundation Model for End-to-End Cardiac MRI Analysis and Interpretation

Read the original on arXiv Machine Learning →

ORION‑CMR is a scanner‑native, end‑to‑end foundation model for cardiac MRI that performs sequence classification, ventricular function assessment, LGE detection, disease classification, and generates reports in about 90 seconds. Trained on 12.9 million images, it outperformed supervised baselines and a prior CMR foundation model, achieving state‑of‑the‑art LGE classification and scar segmentation. In a multi‑vendor clinical cohort, it reached an AUC of 0.96 for normal‑vs‑abnormal detection and 0.88 for multiclass disease classification, with generated reports agreeing 81.4% with expert interpretation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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