AI-assisted 3D Morphology Mapping for Musculoskeletal Anatomy

Thesis Abstract

The study addresses a critical gap in musculoskeletal science where conventional imaging and manual morphometrics fail to capture the complex 3D morphology of bones, joints, and soft-tissue interfaces at scale and with high precision. AI-assisted 3D Morphology Mapping for Musculoskeletal Anatomy aims to develop an integrated ICT-driven framework that automatically reconstructs, analyzes, and interprets three-dimensional morphology from multimodal data to enhance diagnostic accuracy, surgical planning, and biomechanical modeling. Specific objectives are to (1) design a scalable pipeline for automated 3D reconstruction of skeletal and soft-tissue structures from CT, MRI, and ultrasound datasets; (2) develop deep learning models for precise segmentation, landmark localization, and surface registration across diverse populations; (3) implement a geometry-aware representation learning approach to quantify morphological variations and biomechanical properties; (4) validate the mapping framework against gold-standard cadaver measurements and clinically verified imaging metrics; and (5) evaluate the clinical utility of the atlas-like morphology maps in radiology reporting and preoperative planning. The research adopts a mixed-methods design combining quantitative model development with qualitative clinical validation. The population comprises 1,200 de-identified biomechanically diverse imaging studies sourced from two tertiary hospitals, including 600 CT scans of limbs, 400 MRIs of the knee and hip, and 200 ultrasound-led morphometric datasets. A stratified random sampling approach ensures representation across age, sex, body mass index, and pathology status (osteoarthritis, osteoporosis, and trauma cases). Data collection instruments include standardized imaging protocols, a curated morphological reference atlas, and expert-annotated ground-truth segments and landmarks. The analytical framework integrates computer vision and advanced statistics convolutional neural networks (CNNs) for segmentation, Graph Neural Networks (GNNs) for landmark-based surface graphs, and variational autoencoders (VAEs) for latent morphology representations; surface registration employs Thin-Plate Spline and iterative closest point (ICP) algorithms; statistical analyses involve multivariate regression, Bland-Altman agreement, and intra-class correlation coefficients (ICC) to assess reliability, with regression analyses and ANOVA used to examine morphological differences across demographic groups. Theoretical grounding draws on the Geometric Morphometrics framework and the Theory of Planned Behavior in clinical adoption, enabling a principled interpretation of shape variation and user acceptance of AI-driven outputs. A two-stage validation procedure includes (i) quantitative accuracy assessment against expert-derived meshes and cadaveric measurements, reporting dice similarity coefficients (>0.85 for major bones) and landmark localization errors under 2 mm on average, and (ii) a blinded clinical validation study with radiologists and surgeons evaluating decision-support impact on 3D planning and measurement reproducibility. Expected findings include high-precision 3D reconstructions across modalities, robust cross-population generalizability of landmark localization with minimal bias, and demonstrable improvement in diagnostic confidence and planning efficiency when using the morphology maps. The study anticipates uncovering novel patterns of morphological covariation linked to age and pathology, quantified through latent space analyses and biomechanical simulations that enhance predictive modeling of joint degeneration and fracture risk. Contributions to knowledge encompass (i) a reusable, multimodal AI-driven morphometrics pipeline that democratizes access to quantitative anatomy at the 3D level, (ii) a validated 3D morphology atlas integrating bone and soft-tissue interfaces for clinical and educational use, and (iii) empirical evidence on the clinical utility of automated morphology maps in improving radiological reporting consistency and preoperative planning outcomes. The conclusion posits that AI-assisted 3D morphology mapping offers superior accuracy, reproducibility, and practical utility over traditional morphometrics, advocating for its integration into routine musculoskeletal assessment and surgical workflows. Recommendations include expanding the dataset to include pediatric populations, refining real-time processing capabilities for intraoperative use, and developing user-centric visualization interfaces to maximize adoption by clinicians.