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    Age estimation using machine learning algorithms with parameters obtained from X-ray images of the calcaneus
    (Wolters Kluwer Medknow Publications, 2024) Ciftci, R.; Secgin, Y.; Oner, Z.; Toy, S.; Öner, Serkan
    Background:Determination of bone age is a critical issue for forensics, surgery, and basic sciences.Aim:This study aims to estimate age with high accuracy and precision using Machine Learning (ML) algorithms with parameters obtained from calcaneus x-ray images of healthy individuals.Method:The study was carried out by retrospectively examining the foot X-ray images of 341 people aged 18-65 years. Maximum width of the calcaneus (MW), body width (BW), maximum length (MAXL), minimum length (MINL), facies articularis cuboidea height (FACH), maximum height (MAXH), and tuber calcanei width (TKW) parameters were measured from the images. The measurements were then grouped as 20-45 years of age, 46-64 years of age, 65 and older, and age estimation was made by using these at the input of ML models.Results:As a result of the ML input of the measurements obtained, a 0.85 Accuracy (Acc) rate was obtained with the Extra Tree Classifier algorithm. The accuracy rate of other algorithms was found to vary between 0.78 and 0.82. The contribution of parameters to the overall result was evaluated by using the shapley additive explanations (SHAP) analyzer of Random Forest algorithm and the MAXH parameter was found to have the highest contribution in age estimation.Conclusions:As a result of our study, calcaneus bone was found to have high accuracy and precision in age estimations.
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    Sex prediction with morphometric measurements of first and fifth metatarsal and phalanx obtained from X-ray images by using machine learning algorithms
    (Via Medica, 2023) Senol, D.; Bodur, F.; Secgin, Y.; Bakici, R. S.; Sahin, N. E.; Toy, S.; Oner, S.
    Background: The aim of this study is to predict sex with machine learning (ML) algorithms by making morphometric measurements on radiological images of the first and fifth metatarsal and phalanx bones.Materials and methods: In this study, radiologic images of 263 individuals (135 female, 128 male) between the ages of 27 and 60 were analysed retrospectively. The images in digital imaging and communications in medicine (DICOM) format were transferred to personal workstation Radiant DICOM Viewer programme. Length and width measurements of the first and fifth metatarsal and foot phalanx bones were performed on the transferred images. In addition, the ratios of the total length of the first proximal and distal phalanx and length of the first metatarsal and total length of fifth proximal, middle, and distal phalanx and maximum length of fifth metatarsal were calculated.Results: As a result of machine learning algorithms, highest accuracy, specificity, sensitivity, and Matthews correlation coefficient values were found as 0.85, 0.86, 0.85, and 0.71, respectively with decision tree algorithm. It was found that accu racy rates of other algorithms varied between 0.74 and 0.83. Conclusions: As a result of our study, it was found that sex estimation was made with high accuracy rate by using machine learning algorithms on X-ray images of the first and fifth metatarsal and foot phalanx. We think that in cases when pelvis, cranium and long bones are harmed and examination is difficult, bones of the first and fifth metatarsal and foot phalanx can be used for sex estimation. (Folia Morphol 2023; 82, 3: 704-711)