CMANet: Cross-Modal Attention Network for 3-D Knee MRI and Report-Guided Osteoarthritis Assessment

Abstract Objective Knee osteoarthritis (OA) is a leading cause of disability worldwide, with early identification of structural changes critical for improving patient outcomes. While magnetic resonance imaging (MRI) provides rich spatial detail, its interpretation remains challenging due to complex anatomy, subtle lesion presentation, and limited voxel-level annotations. Meanwhile, radiology reports encode semantic and diagnostic insights that are typically underutilized in imaging AI pipelines. In this work, we introduce CMANet, a Cross-Modal Attention Network that integrates 3D knee MRI volumes with their corresponding free-text radiology reports for joint OA severity classification and lesion segmentation. CMANet introduces four key innovations: (1) an asymmetric cross-modal attention mechanism that enables bidirectional information flow between image and text, (2) a weakly supervised anatomical alignment module linking report phrases to MRI regions, (3) a multi-task prediction head for simultaneous OA grading and voxel-level lesion detection, and (4) interpretable attention pathways for tracing predictions to report language and anatomical structures. Evaluated on a dataset of 642 patients with paired MRI and radiology reports, CMANet achieved significant improvements over unimodal baselines—boosting KL-grade classification AUC from 0.769 to 0.871 (Δ =0.102, p=0.004) and increasing Dice scores for cartilage and BML lesion segmentation. The model also demonstrated generalizability in predicting 2-year OA progression (AUC=0.804) and achieved improved alignment between anatomical regions and textual descriptions. These results highlight the potential of multimodal learning to enhance diagnostic accuracy, spatial localization, and explainability in musculoskeletal imaging. Competing Interest Statement The authors have declared no competing interest. Funding Statement This study did not receive any funding Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics Committee of Kuwait Ministry of Health gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Footnotes Email: oghaz{at}exac.com Email: Hosseini.f{at}sbmu.ac.ir Email: mojdeh_gholamishali{at}student.uml.edu Email: dr.altaweel{at}gmail.com Email: nhaouchine{at}bwh.harvard.edu Email: Frikhtegarnezami{at}bwh.harvard.edu Author affiliations updated, Figures and tables revised Data Availability All data produced in the present study are available upon reasonable request to the authors