Machine learning methods for sex estimation of sub-adults using cranial computed tomography images

This research aimed to compare the classification accuracy of three machine learning (ML) methods (random forest (RF), support vector machines (SVM), linear discriminant analysis (LDA)) for sex estimation of sub-adults using cranial computed tomography (CCT) images. A total of 521 CCT scans from sub-adult Malaysians aged 0 to 20 were analysed using Mimics software (Materialise Mimics Ver. 21). Plane-to-plane (PTP) protocol was used for measuring 14 chosen craniometric parameters. A trio of machine learning algorithms RF, SVM, and LDA with GridSearchCV was used to produce classification models for sex estimation. In addition, performance was measured in the form of accuracy, precision, recall, and F1-score, among others. RF produced testing accuracy of 73%, with the best hyperparameters of max_depth = 6, max_samples = 40, and n_estimators = 45. SVM obtained an accuracy of 67% with the best hyperparameters: learning rate (C) = 10, gamma = 0.01, and kernel = radial basis function (RBF). LDA obtained the lowest accuracy of 65% with shrinkage of 0.02. Among the tested ML methods, RF showed the highest testing accuracy in comparison to SVM and LDA. This is the first AI-based classification model that can be used for estimating sex in sub-adults using CCT scans.

Computed tomography · Estimation · Radiology · Sociology · Tomography · Artificial Intelligence · Computer Science · Demographic Trends and Gender Preferences · Demography · Engineering · Forensic Anthropology and Bioarchaeology Studies · Medicine · Psychology