AbdCTBench: Learning Clinical Biomarker Representations from Abdominal Surface Geometry

Body composition analysis through CT and MRI imaging provides critical insights for cardio-metabolic health assessment but remains limited by accessibility barriers including radiation exposure, high costs, and infrastructure requirements. We present AbdCTBench, a large-scale dataset containing 23,506 CT-derived abdominal surface meshes from 18,719 patients, paired with 87 comorbidity labels, 31 specific diagnosis codes, and 16 CT-derived biomarkers. Our key insight is that external surface geometry is predictive of internal tissue composition, enabling accessible health screening through consumer devices. We establish comprehensive benchmarks across seven computer vision architectures (ResNet-18/34/50, DenseNet-121, EfficientNet-B0, ViT-Small, Swin Transformer-Base), demonstrating that models can learn robust surface-to-biomarker representations directly from 2D mesh projections. Our best-performing models achieve clinically relevant accuracy: age prediction with MAE 6.22 years (R²=0.757), mortality prediction with AUROC 0.839, and diabetes (with chronic complications) detection with AUROC 0.801. Notably, smaller architectures consistently matched or surpassed larger models, while medical-domain pre-training (RadImageNet) and self-supervised pre-training (DINOv2) showed competitive but not superior performance. AbdCTBench represents the largest publicly available dataset bridging external body geometry with internal clinical measurements, enabling future research in accessible medical AI. We plan to release the dataset, evaluation protocols, and baseline models to accelerate research in representation learning for medical applications, immediately following the review period.