Sex Estimation in Terms of Inclination and Alsberg in Proximal Femur by using Machine Learning Algorithms
DOI:
https://doi.org/10.6000/1929-6029.2025.14.49Keywords:
Artificial neural networks, Upper femur angles, Sex assessment, Forensic anthropology, Femoral neck-shaft angle, Collo-diaphyseal angle.Abstract
The fact that the femur has a solid structure ensures that the integrity of the bone is preserved, making it favorable in the sex determination process. In this study, it was aimed to predict sex by using angular variables of femur from computed tomography (CT) images and machine learning algorithms. In this study, a total of 4 angular measurements, including the femoral inclination angle (FIA) and Alsberg angle (FAA) of the proximal femur on both sides, were evaluated on CT images of 88 female and 92 male adults. Logistic regression (LR) and classification and regression tree (CART) machine learning algorithms were used for sex diagnosis. 5-fold cross-validation method was used in the training and testing processes of the models. Model performances were evaluated with area under the ROC curve, Precision and Recall statistics. Of the 4 angle measurements evaluated, only the right side FIA mean was significantly higher in women (p=0.042). The sex diagnosis success of the LR model and the CART algorithm were found to be similar (p values 0.014 and 0.017, respectively). When the success criteria of each algorithm were examined, we saw that although sex estimation was significant, (Acc 0.61, Acc 0.60, respectively) they were not very successful. We found that the machine learning algorithms applied to the variables of proximal femur angle parameters gave low accuracy of sex and the effect of both models on sex estimation was similar.
References
Curate F, Coelho J, Gonçalves D, Coelho C, Ferreira MT, Navega D, Cunha E. A method for sex estimation using the proximal femur. Forensic Sci Int 2016; 266: 579.e1-579.e7. DOI: https://doi.org/10.1016/j.forsciint.2016.06.011
Wagner FV, Negrão JR, Campos J, Ward SR, Haghighi P, Trudell DJ, Resnick D. Capsular ligaments of the hip: anatomic, histologic, and positional study in cadaveric specimens with MR arthrography. Radiology 2012; 263(1): 189-98. DOI: https://doi.org/10.1148/radiol.12111320
Reynolds A. The fractured femur. Radiol Technol 2013; 84(3): 273-91; quiz pp. 292-4.
Boese CK, Dargel J, Oppermann J, Eysel P, Scheyerer MJ, Bredow J, Lechler P. The femoral neck-shaft angle on plain radiographs: a systematic review. Skeletal Radiol 2016; 45(1): 19-28. DOI: https://doi.org/10.1007/s00256-015-2236-z
Sertel Meyvaci S, Kaya Y. Investigation of the femoral inclination and Alsberg angles according to age and gender in adults. Anatomy 2020; 14(3). DOI: https://doi.org/10.2399/ana.20.827122
van den Heever DJ, Scheffer C, Erasmus P, Dillon E. Classification of gender and race in the distal femur using self organising maps. Knee 2012; 19(4): 488-492. DOI: https://doi.org/10.1016/j.knee.2011.06.009
Tuck SP, Pearce MS, Rawlings DJ, Birrell FN, Parker L, Francis RM. Differences in bone mineral density and geometry in men and women: the Newcastle Thousand Families Study at 50 years old. Br J Radiol 2005; 78: 493-498. DOI: https://doi.org/10.1259/bjr/42380498
Blankstein M, Schottel P, Brinker M, Krieg J. Limb Rotation in Lower Extremity Fractures. Instr Course Lect 2024; 73: 901-918.
Carballido-Gamio J, Nicolella DP. Computational anatomy in the study of bone structure. Curr Osteoporos Rep 2013; 11(3): 237-45. DOI: https://doi.org/10.1007/s11914-013-0148-1
Cao Y, Ma Y, Vieira DN, Guo Y, Wang Y, Deng K, Chen Y, Zhang J, Qin Z, Chen F, Huang P, Zhang J. A potential method for sex estimation of human skeletons using deep learning and three-dimensional surface scanning. Int J Legal Med 2021; 135(6): 2409-2421. DOI: https://doi.org/10.1007/s00414-021-02675-z
Deng Y, Wang L, Zhao C, Tang S, Cheng X, Deng HW, Zhou W. A deep learning-based approach to automatic proximal femur segmentation in quantitative CT images. Med Biol Eng Comput 2022; 60(5): 1417-1429. DOI: https://doi.org/10.1007/s11517-022-02529-9
Jang SJ, Alpaugh K, Kunze KN, Li TY, Mayman DJ, Vigdorchik JM, Jerabek SA, Gausden EB, Sculco PK. Deep-Learning Automation of Preoperative Radiographic Parameters Associated With Early Periprosthetic Femur Fracture After Total Hip Arthroplasty. J Arthroplasty 2024; 39(5): 1191-1198.e2. DOI: https://doi.org/10.1016/j.arth.2023.11.021
Homma Y, Zhuang X, Ohtsu H, Ishii S, Shirogane Y, Hayashi K, Watari T, Baba T, Ishijima M. Highly accurate acoustical prediction using support vector machine algorithm for post-operative subsidence after cementless total hip arthroplasty. Int Orthop 2023; 47(1): 187-192. DOI: https://doi.org/10.1007/s00264-022-05641-5
Spradley MK, Jantz RL. Sex estimation in forensic anthropology: skull versus postcranial elements. Journal of Forensic Sciences 2011; 56(2): 289-296. DOI: https://doi.org/10.1111/j.1556-4029.2010.01635.x
Maijanen H, Junno JA, Keisu A, Niinimäki J, Lehenkari P, Oura P. Sex estimation from knee breadth dimensions in a Finnish population. Leg Med (Tokyo) 2021; 51: 101873. DOI: https://doi.org/10.1016/j.legalmed.2021.101873
Knecht S, Santos F, Ardagna Y, Alunni V, Adalian P, Nogueira L. Sex estimation from long bones: a machine learning approach. Int J Legal Med 2023; 137(6): 1887-1895. DOI: https://doi.org/10.1007/s00414-023-03072-4
Li Y, Niu C, Wang J, Xu Y, Dai H, Xiong T, Yu D, Guo H, Liang W, Deng Z, Lv J, Zhang L. A fully automated sex estimation for proximal femur X-ray images through deep learning detection and classification. Leg Med (Tokyo) 2022; 57: 102056. DOI: https://doi.org/10.1016/j.legalmed.2022.102056
Attia MAH, Attia MH, Tarek Farghaly Y, Ahmed El-Sayed Abulnoor B, Curate F. Performance of the supervised learning algorithms in sex estimation of the proximal femur: A comparative study in contemporary Egyptian and Turkish samples. Sci Justice 2022; 62(3): 288-309. DOI: https://doi.org/10.1016/j.scijus.2022.03.003
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