The Use of Statistical Shape and Appearance Modelling for Predicting Osteoporotic Fracture Risk.

purpose of reviewOsteoporotic fractures remain a major cause of morbidity and mortality worldwide. Current clinical assessment metrics (e.g., bone mineral density) are limited in their ability to identify fracture risk and bone strength. Statistical Shape and Appearance Modelling (SSAM) offers a method to quantify anatomical geometry and density patterns. This review examines advancements in SSAM for osteoporosis research. RECENT

findingsRecent literature demonstrates that SSAM can capture detailed bone geometry and internal density distribution. Increasingly, these models are combined with computational analytics, including finite element analysis and machine learning, to assess the mechanical and structural behavior of bone. SSAM provides a robust quantitative framework for bone research. Notably, SSAM is used to reconstruct 3D subjects from clinical 2D images for biomechanical evaluation. Although clinical adoption remains limited by generalizability, the advancement of deep learning and complex SSAM pipelines supports its potential for osteoporosis screening and fracture risk prediction.