Sidaty El Hadramy

Learning from the unseen: synthetic data for scoliosis AI segmentation.

PURPOSE Severe scoliosis remains challenging for 3D vertebral segmentation because annotated pathological computed tomography (CT) data are scarce. METHODS We synthesize scoliosis-like CT volumes by jointly warping public healthy spine scans and labels and then train nnU-Net CNN and PRIMUS models under matched comput...

Generalizing Soft Tissue Deformation and Force Prediction Across Material Stiffness and Geometry

Accurate soft tissue simulation is essential for surgical training, pre-operative planning, and haptic feedback systems. While learning-based surrogate models trained on data using the finite element method (FEM) offer a promising path to real-time inference, their reliability depends on well-calibrated constitutive mo...

Age-Residualized Physiological Biomarkers and Gradient-Boosted Trees for Predicting Cognitive Impairment from Polysomnography

SA-Profile: Automated Sulcus Angle Profiling from Super-Resolution MRI

An automatic framework for continuous SA profiling from super-resolved MR volumes is proposed, extending conventional SA assessment to a continuous profile-based description of trochlear morphology without additional imaging, while remaining conceptually linked to current clinical assessment.