Application of machine-learning methods in age-at-death estimation from 3D surface scans of the adult acetabulum - Štepanovský, Michal; Buk, Zdeněk; Pilmann Kotěrová, Anežka; Brůžek, Jaroslav; Bejdová, Šárka; Techataweewan, Nawaporn; Velemínská, Jana (Forensic science international, 2024)
OBJECTIVE: Age-at-death estimation is usually done manually by experts. As such, manual estimation is subjective and greatly depends on the past experience and proficiency of the expert. This becomes even more critical if experts need to evaluate individuals with unknown population affinity or with affinity that they are not familiar with. The purpose of this study is to design a novel age-at-death estimation method allowing for automatic evaluation on computers, thus eliminating the human factor. METHODS: We used a traditional machine-learning approach with explicit feature extraction. First, we identified and described the features that are relevant for age-at-death estimation. Then, we created a multi-linear regression model combining these features. Finally, we analysed the model performance in terms of Mean Absolute Error (MAE), Mean Bias Error (MBE), Slope of Residuals (SoR) and Root Mean Squared Error (RMSE). RESULTS: The main result of this study is a population-independent method of estimating an individual's age-at-death using the acetabulum of the pelvis. Apart from data acquisition, the whole procedure of pre-processing, feature extraction and age estimation is fully automated and implemented as a computer program. This program is a part of a freely available web-based software tool called CoxAGE3D, which is available at https://coxage3d.fit.cvut.cz/. Based on our dataset, the MAE of the presented method is about 10.7 years. In addition, five population-specific models for Thai, Lithuanian, Portuguese, Greek and Swiss populations are also given. The MAEs for these populations are 9.6, 9.8, 10.8, 10.5 and 9.2 years, respectively. Our age-at-death estimation method is suitable for individuals with unknown population affinity and provides acceptable accuracy. The age estimation error cannot be completely eliminated, because it is a consequence of the variability of the ageing process of different individuals not only across different populations but also within a certain population.
Keywords: Imaging, Three-Dimensional; Software; Forensic Anthropology; lineární modely; software; soudní antropologie; Humans; Acetabulum; lidé středního věku; Female; Aged; mladý dospělý; dospělí; Machine Learning; určení kostního věku; senioři; lidé; Adult; ženské pohlaví; strojové učení; Male; Middle Aged; Linear Models; Age Determination by Skeleton; Aged, 80 and over; Young Adult; acetabulum; zobrazování trojrozměrné; senioři nad 80 let; mužské pohlaví