Latest AI and machine learning research in orthopedics for healthcare professionals.
Objective. To develop an interpretable multimodal machine-learning model for risk stratification of the rapid pain progression phenotype in knee osteoarthritis and to evaluate its performance in the independent PROCOAC cohort. Methods. An elastic-net logistic regression model was trained using Osteoarthritis Initiative (OAI) data. Rapid pain progression was defined over overlapping 24-month window...
Adversarial attacks against large vision-language models (LVLMs) serve as an effective means of assessing their robustness in cross-modal semantic understanding. Existing studies mainly focus on corrupting visual inputs to induce predefined erroneous responses in general vision-language tasks, whereas corresponding investigations in remote sensing fields remain largely underexplored. Compared with...
Although Multimodal Large Language Models have achieved strong performance across a wide range of vision-language tasks, they still suffer from halluc...
Open-world video anomaly detection (OWVAD) is expected to detect events that match a user-specified definition of abnormality. This requirement is str...
The accurate diagnosis of spinal pathologies depends heavily on radiological interpretation, yet automated systems are hindered by the lack of diverse...
Active sampling for accelerated MRI must distribute a tight sampling budget across spatial frequencies that carry very different kinds of information....
Robust out-of-the-box performance is essential for the clinical deployment of deep learning models in medical imaging. An important but underexplored ...
Remote sensing image editing aims to modify remote sensing images according to natural language instructions while preserving geographic rules and sen...
Incorrect disposal can contaminate campus recycling streams, and a bin-mounted camera could provide feedback as an item is discarded. We evaluated whe...
Downstream use of genomic foundation models follows one of three conventions: aggregating representations across all layers (Pearce et al., 2026), def...
Magnetic resonance imaging comes in various modality contrasts that provide complementary anatomical and pathological information. Complete multimodal...
We investigate whether a pretrained generative image-editing model can provide a common interface for numerical simulation. Physical inputs and soluti...
Neural simulation-based inference enables parameter estimation for complex models, but typically requires the user to specify a simulator encoding a f...
Quantitative joint angles are rarely available in routine care because the tools are slow, costly, or confined to a laboratory. We show that clinical ...
Longitudinal tumor measurements, dropout information, and genetic covariates provide complementary information about treatment response, but integrati...
Human cognition does not separate understanding and generation. A teacher at a whiteboard speaks and draws $\textit{together}$, each modality reshapes...
Self-supervision is a powerful technique for learning visual representations from unlabeled data. Existing techniques primarily adopt a two-stage appr...
Accurate geometric calibration is essential for fluoroscopy-guided spinal imaging, digitally reconstructed radiograph (DRR) generation, and 2D--3D ver...
The rapid expansion of solar photovoltaic (PV) systems has increased the need for reliable and scalable fault classification, as manual inspection is ...
Purpose The application of machine learning (ML) to osteoporosis prediction has expanded rapidly, yet no comprehensive meta-analysis has synthesized t...