Deep learning approach for sex determination using medial clavicle histomorphology

Sex determination from skeletal remains presents significant challenges, particularly when bones are damaged or incomplete. In such cases, histomorphological analysis of fragmented bone pieces becomes essential. The medial clavicle is recognized as a valuable anatomical marker in forensic and anthropological research due to its pronounced sex-related morphological variations. This study aimed to develop a deep learning-based method for sex determination using histological images of the medial clavicle in Thai population, and to evaluate its performance with both validation and blind test sets utilizing the GoogLeNet convolutional neural network architecture. A total of 100 pairs of clavicles were included, with 70 cases (35 males,35 females) assigned to the training group and 30 (15 males,15 females) to the test group. Histological images underwent pre-processing and were standardized in size before being input into the training model. Validation accuracy was assessed using MATLAB, while descriptive statistics for the test set were calculated with SPSS software. GoogLeNet demonstrated superior performance, achieving a validation accuracy of 96.43% and a blind test accuracy of 90%. These results highlight the potential of a deep learning approach using 2D histological images of the medial clavicle as a straightforward and effective tool for sex determination in forensic anthropology, offering a high degree of accuracy. This method paves the way for rapid, objective, and accessible sex identification, even from fragmented human remains, and demonstrates promise for broader applications in the forensic and anthropological sciences.