Organizations: School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Belfast, UK · Radiology Department, Affiliated Nantong Clinical College of Nantong University, Nantong First People’s Hospital, School of Clinical Medicine, Nantong University, Nantong, Jiangsu, China · School of Artificial Intelligence and Computer Science, Nantong University, Nantong, China · Faculty of Data Science, City University of Macau, Macau, China · Department of Chemistry, University of Oxford, Oxford, UK · Orthopedics Department, Nantong First People’s Hospital, Southeast University, Nantong, Jiangsu, China · for Ocean Engineering, Tsinghua SIGS, Shenzhen, China · 7Institution
Clinical diagnosis of meniscus injuries requires radiologists to integrate volumetric MRI evidence with patient context (e.g., sex, age, BMI) and to produce structured diagnostic reports. Existing knee MRI benchmarks are typically unimodal and rely on coarse labels, limiting their ability to evaluate holistic clinical reasoning. We introduce MeniOmni, a structured multimodal benchmark for meniscus injury assessment, consisting of 746 multi-center MRI studies with tri-planar volumetric inputs, Clinical Priors, and expert-annotated clinical text. MeniOmni supports two tasks: (1) fine-grained Stoller severity grading and (2) diagnostic report generation. We further propose risk-aware ordinal evaluation and a semantic consistency metric (Meni-Score) to better reflect clinical relevance. Baseline experiments show that incorporating Clinical Priors improves grading performance and reduces severe errors, highlighting the value of multimodal context for safer assessment. Code and data are available at https://github.com/ShuruiXu/MeniOmni.