arXiv:2606. 05357v1 Announce Type: new Abstract: Purpose: To develop an interpretable and trustworthy AI framework that combines deep learning based MRI Osteoarthritis Knee Score (MOAKS) prediction with interpretable statistical modeling to study structure-pain relationships at scale using data from the Osteoarthritis Initiative (OAI).
arXiv:2604. 23435v2 Announce Type: replace-cross Abstract: Grading knee osteoarthritis (KOA) on plain radiographs is poorly reproducible across readers.
arXiv:2608. 15719v1 Announce Type: new Abstract: Osteoarthritis (OA) is a progressive chronic joint disease resulting in a breakdown of articular cartilage and bone when damaged joint tissues are not able to normally repair themselves.
arXiv:2609.10125v1 Announce Type: new Abstract: Trochlear dysplasia (TD) is an abnormality of the femoral trochlea associated with anterior knee pain and patellar instability. The sulcus angle (SA) i...
arXiv:2608.18012v2 Announce Type: replace Abstract: One of the most common reasons for anterior cruciate ligament (ACL) reconstruction failure is femoral tunnel malpositioning (ACL footprint center a...
arXiv:2606. 15250v1 Announce Type: cross Abstract: Radiographic assessment of lower-limb alignment (LLA) is important for predicting joint health and surgical outcomes in total knee arthroplasty.
arXiv:2604. 27967v2 Announce Type: replace Abstract: Background: We introduce StructGP, a continuous-time multi-task Gaussian process that couples process convolutions with differentiable structure learning to uncover a sparse, ordered directed acyclic graph (DAG) of inter-variable dependencies while preserving principled uncertainty.
arXiv:2609.31461v1 Announce Type: new Abstract: Background: Large volumes of unlabeled knee MRI scans are available across repositories but remain insufficiently leveraged. We developed KneePreM, a k...
arXiv:2609.26105v1 Announce Type: cross Abstract: Prediction of pathological complete response before neoadjuvant chemotherapy may facilitate more tailored therapeutic planning for breast cancer pati...
arXiv:2606. 17115v1 Announce Type: cross Abstract: Foundation models (FMs) have emerged as powerful representation extractors for medical data, yet their generalizability to datasets under distribution shift remains underexplored.
arXiv:2607. 15394v1 Announce Type: new Abstract: Black-box models limit the adoption of artificial intelligence in medicine due to their lack of interpretability and reproducibility.
arXiv:2608. 07368v1 Announce Type: cross Abstract: Computed tomography (CT) remains the reference for 3D osseous morphometry in femoroacetabular impingement (FAI) but requires ionizing radiation and manual measurement.
This study introduces a self‑supervised, physics‑guided deep‑learning framework that converts standard clinical T1‑, T2‑, and FLAIR MRIs into quantitative T1, T2, and proton‑density maps. Trained on 4,121 scan sessions from four different 3 T scanners over six years, the method produces maps whose white‑ and gray‑matter values fall within literature ranges and shows minimal variation across scanner hardware and acquisition protocols (coefficients of variation ≤ 1.1 %). Voxel‑wise reproducibility is high, with Pearson and concordance correlation coefficients above 0.82 for T1 and T2 and mean relative differences below 6 % for T2.