AI-driven predictive models for determining age and sex from cranial measurements

The ability to accurately determine the age and sex of individuals from cranial measurements has significant implications in forensic anthropology and bioarchaeology. Leveraging artificial intelligence (AI) advancements, we have developed predictive models that utilize cranial measurements to estimate these demographic variables. Additionally, these models have been adapted to predict cephalometric measurements from nonradiological data, enhancing their utility in clinical settings. This study utilized a dataset comprising cranial measurements from diverse populations. Advanced AI algorithms, including Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Deep Neural Networks (DNN), were employed to develop predictive models. The dataset was split into training and validation sets, and cross-validation techniques were applied to ensure model robustness and prevent overfitting. Feature selection and hyperparameter tuning were conducted to optimize model performance. The predictive models demonstrated high accuracy in estimating sex, with the SVM and ANN models achieving accuracies between 86% and 91%. The DNN model further improved accuracy, reaching over 93%. For age prediction, models treated age as a continuous and categorical variable, with the constant model achieving a mean absolute error (MAE) of less than 2 years and the categorical model achieving an accuracy of approximately 85%. The cephalometric prediction models showed promising results, with significant reductions in mean squared error (MSE) compared to actual clinical measurements. Integrating AI algorithms in the analysis of cranial measurements significantly enhances the accuracy of age and sex estimation. Furthermore, extending these models to predict cephalometric measurements from non-radiological data offers a promising tool for clinical applications, potentially reducing the need for radiographic imaging. Future research will expand the dataset and refine the models for broader applicability and improved accuracy. References 1. Del Bove, A.; Veneziano, A. A Generalised Neural Network Model to Estimate Sex from Cranial Metric Traits: A Robust Training and Testing Approach. Appl. Sci. 2022, 12, 9285. 2. Foreman, A.; Lazenby, R.A. Use of Craniometric Data in a Generalized Procrustes Analysis to Establish Human Biological Affinities. Forensic Sci. Int. 1996, 82, 39-53. 3. GADNN: A Revolutionary Hybrid Deep Learning Neural Network for Age and Sex Determination Utilizing Cone Beam Computed Tomography Images of Maxillary and Frontal Sinuses. BMC Med. Res. Methodol. 2023.