Abstract: Objective  To construct a high-precision statistical shape model of the humerus and systematically describe its anatomical variation patterns. Methods  In this study, a statistical shape model of the humerus was constructed using the three-dimensional model data of 60 collected humerus cases. The principal component analysis method was adopted to reveal the main patterns of humerus anatomical variations and their contribution rates. Results  The results showed that the first five principal components (PC01-PC05) collectively explained 96. 6% of the total anatomical variations. Among them, PC01 and PC02 were the main components, contributing 66. 6% and 23. 5% of the variations respectively. PC01 mainly reflects the scaling effect of the overall size (length/width) of the humerus, while PC02 reveals the length variation characteristics independent of the overall scaling, which may reflect individualized differences. The subsequent principal components (PC03-PC05) depicted the local morphological characteristics and fine changes of the proximal and distal humerus. Conclusion  The statistical shape model constructed in this study provides a reliable digital basis for personalized prosthesis design, surgical planning and biomechanical simulation.

Key words: humerus, three-dimensional reconstruction, anatomical variation, morphological changes, statistical shape modeling, principal component analysis