A Survey on Artificial Intelligence Techniques for Biomedical Image Analysis in Skeleton-Based Forensic Human Identification

This paper represents the first survey on the application of AI techniques for the analysis of biomedical images with forensic human identification purposes. Human identification is of great relevance in today’s society and, in particular, in medico-legal contexts. As consequence, all technological advances that are introduced in this field can contribute to the increasing necessity for accurate and robust tools that allow for establishing and verifying human identity. We first describe the importance and applicability of forensic anthropology in many identification scenarios. Later, we present the main trends related to the application of computer vision, machine learning and soft computing techniques to the estimation of the biological profile, the identification through comparative radiography and craniofacial superimposition, traumatism and pathology analysis, as well as facial reconstruction. The potentialities and limitations of the employed approaches are described, and we conclude with a discussion about methodological issues and future research.Spanish Ministry of Science, Innovation and UniversitiesEuropean Union (EU) PGC2018-101216-B-I00Regional Government of Andalusia under grant EXAISFI P18-FR-4262Instituto de Salud Carlos IIIEuropean Union (EU) DTS18/00136European Commission H2020-MSCA-IF-2016 through the Skeleton-ID Marie Curie Individual Fellowship 746592Spanish Ministry of Science, Innovation and Universities-CDTI, Neotec program 2019 EXP-00122609/SNEO-20191236European Union (EU)Xunta de Galicia ED431G 2019/01European Union (EU) RTI2018-095894-B-I0

Ancestry-independent osteometric sex estimation from selected postcranial skeletal elements of South Africans: a machine learning approach

Sex estimation, as part of a biological profile, has the power to halve the number of possible identities of unidentified skeletal remains. Postcranial elements have been studied in South Africa (SA) for the purpose of sex estimation and have often proven to be more accurate than the cranium. Estimation techniques using postcranial elements in SA almost exclusively utilise discriminant analysis to evaluate sex, but international publications have shown success using alternative machine learning (ML) algorithms. SA methods and standards are often restricted by limited sample size, lack of robust statistical techniques in older publications and, the prerequisite of known or estimated ancestry. Most methods are specific to SA African, European or, more recently, Mixed ancestry groups and are unreliable when ancestry is unknown. The aim of this study was to apply a series of ML algorithms to train ancestry independent sex classification models using postcranial osteometric measurements from the cadaveric skeletal remains of modern South Africans, focussing on long bone joints. The study consisted of a roughly demographically representative, pooled sample, of 650 South Africans (325 male, 325 female). 12 osteometric measurements were taken from available left- and, or right-sided bones for each individual. All 12 mensurations were sexually dimorphic and differences between left- and right-sided bones were negligible. The dataset was subjected to ML algorithm training using univariate and multivariate predictor combinations. The best performing ML algorithm, given the sample size and available predictors was discriminant function analysis. Univariate model accuracies ranged from 80.5-89.1% and multivariate model accuracies ranged from 84.5%, using 2 predictors, to 92.8%, using 12 predictors. An optimised 3-predictor model was able to predict sex with 92.7% accuracy. Results from this study were comparable to those using ancestry-specific models and non-ancestry-specific models, where available. Findings from this study suggest that the inclusion of ancestry, when predicting sex using the elements examined, is not necessary as it does not significantly improve prediction accuracy

Predictive Modelling of Bone Age through Classification and Regression of Bone Shapes

Bone age assessment is a task performed daily in hospitals worldwide. This involves a clinician estimating the age of a patient from a radiograph of the non-dominant hand. Our approach to automated bone age assessment is to modularise the algorithm into the following three stages: segment and verify hand outline; segment and verify bones; use the bone outlines to construct models of age. In this paper we address the final question: given outlines of bones, can we learn how to predict the bone age of the patient? We examine two alternative approaches. Firstly, we attempt to train classifiers on individual bones to predict the bone stage categories commonly used in bone ageing. Secondly, we construct regression models to directly predict patient age. We demonstrate that models built on summary features of the bone outline perform better than those built using the one dimensional representation of the outline, and also do at least as well as other automated systems. We show that models constructed on just three bones are as accurate at predicting age as expert human assessors using the standard technique. We also demonstrate the utility of the model by quantifying the importance of ethnicity and sex on age development. Our conclusion is that the feature based system of separating the image processing from the age modelling is the best approach for automated bone ageing, since it offers flexibility and transparency and produces accurate estimate

Random forest age estimation model based on length of left hand bone for Asian population

In forensic anthropology, age estimation is used to ease the process of identifying the age of a living being or the body of a deceased person. Nonetheless, the specialty of the estimation models is solely suitable to a specific people. Commonly, the models are inter and intra-observer variability as the qualitative set of data is being used which results the estimation of age to rely on forensic experts. This study proposes an age estimation model by using length of bone in left hand of Asian subjects range from newborn up to 18-year-old. One soft computing model, which is Random Forest (RF) is used to develop the estimation model and the results are compared with Artificial Neural Network (ANN) and Support Vector Machine (SVM), developed in the previous case studies. The performance measurement used in this study and the previous case study are R-square and Mean Square Error (MSE) value. Based on the results produced, the RF model shows comparable results with the ANN and SVM model. For male subjects, the performance of the RF model is better than ANN, however less ideal than SVM model. As for female subjects, the RF model overperfoms both ANN and SVM model. Overall, the RF model is the most suitable model in estimating age for female subjects compared to ANN and SVM model, however for male subjects, RF model is the second best model compared to the both models. Yet, the application of this model is restricted only to experimental purpose or forensic practice

Automating the decision making process of Todd’s age estimation method from the pubic symphysis with explainable machine learning

Age estimation is a fundamental task in forensic anthropology for both the living and the dead. The procedure consists of analyzing properties such as appearance, ossification patterns, and morphology in different skeletonized remains. The pubic symphysis is extensively used to assess adults’ age-at-death due to its reliability. Nevertheless, most methods currently used for skeleton-based age estimation are carried out manually, even though their automation has the potential to lead to a considerable improvement in terms of economic resources, effectiveness, and execution time. In particular, explainable machine learning emerges as a promising means of addressing this challenge by engaging forensic experts to refine and audit the extracted knowledge and discover unknown patterns hidden in the complex and uncertain available data. In this contribution we address the automation of the decision making process of Todd’s pioneering age assessment method to assist the forensic practitioner in its application. To do so, we make use of the pubic bone data base available at the Physical Anthropology lab of the University of Granada. The machine learning task is significantly complex as it becomes an imbalanced ordinal classification problem with a small sample size and a high dimension. We tackle it with the combination of an ordinal classification method and oversampling techniques through an extensive experimental setup. Two forensic anthropologists refine and validate the derived rule base according to their own expertise and the knowledge available in the area. The resulting automatic system, finally composed of 34 interpretable rules, outperforms the state-of-the-art accuracy. In addition, and more importantly, it allows the forensic experts to uncover novel and interesting insights about how Todd’s method works, in particular, and the guidelines to estimate age-at-death from pubic symphysis characteristics, generally.Ministry of Science and Innovation, Spain (MICINN) Spanish GovernmentAgencia Estatal de Investigacion (AEI) PID2021-122916NB-I00 Spanish Government PGC2018-101216-B-I00Junta de AndaluciaUniversity of Granada P18 -FR -4262 B-TIC-456-UGR20European CommissionUniversidad de Granada/CBU

Predictive Modelling of Bone Ageing

Bone age assessment (BAA) is a task performed daily by paediatricians in hospitalsworldwide. The main reasons for BAA to be performed are: fi�rstly, diagnosis of growth disorders through monitoring skeletal development; secondly, prediction of final adult height; and fi�nally, verifi�cation of age claims. Manually predicting bone age from radiographs is a di�fficult and time consuming task. This thesis investigates bone age assessment and why automating the process will help. A review of previous automated bone age assessment systems is undertaken and we investigate why none of these systems have gained widespread acceptance. We propose a new automated method for bone age assessment, ASMA (Automated Skeletal Maturity Assessment). The basic premise of the approach is to automatically extract descriptive shape features that capture the human expertise in forming bone age estimates. The algorithm consists of the following six modularised stages: hand segmentation; hand segmentation classifi�cation; bone segmentation; feature extraction; bone segmentation classifi�cation; bone age prediction. We demonstrate that ASMA performs at least as well as other automated systems and that models constructed on just three bones are as accurate at predicting age as expert human assessors using the standard technique. We also investigate the importance of ethnicity and gender in skeletal development. Our conclusion is that the feature based system of separating the image processing from the age modelling is the best approach, since it off�ers flexibility and transparency, and produces accurate estimates

Body Mass Estimation from the Human Skeleton

The established methods for estimating average body mass from the skeleton are of two types: biomechanical and morphometric. Neither technique currently addresses the extremes of body mass (e.g. emaciation or obesity). The goal of this research is to explore several different biomechanical methods, using data collected from high resolution computed tomographic scans and macroscopic analysis of 150 known modern individuals from the William M. Bass Donated Skeleton Collection at the University of Tennessee, Knoxville. This research will review the biomechanics of human gait and the biomechanical accommodations that occur with increased obesity and load bearing. The analysis will include cross-sectional geometry of the human femur at five locations along the diaphysis, bone mineral density scans of the proximal femur and a macroscopic evaluation of degenerative changes of the articulations of the spine, hip, knee and foot. The best single indicator of body mass for both males and females is the cross-sectional area of the proximal femur and BMD. By using pathologies combined, an accuracy rate of 87% for predicting obesity was achieved using a classification tree with sexes pooled. Furthermore, severe obesity has such a profound effect on the human skeleton as to leave a suite of traits affecting the load bearing elements of the lower limb and vertebral column

Knowledge Based Systems: A Critical Survey of Major Concepts, Issues, and Techniques

This Working Paper Series entry presents a detailed survey of knowledge based systems. After being in a relatively dormant state for many years, only recently is Artificial Intelligence (AI) - that branch of computer science that attempts to have machines emulate intelligent behavior - accomplishing practical results. Most of these results can be attributed to the design and use of Knowledge-Based Systems, KBSs (or ecpert systems) - problem solving computer programs that can reach a level of performance comparable to that of a human expert in some specialized problem domain. These systems can act as a consultant for various requirements like medical diagnosis, military threat analysis, project risk assessment, etc. These systems possess knowledge to enable them to make intelligent desisions. They are, however, not meant to replace the human specialists in any particular domain. A critical survey of recent work in interactive KBSs is reported. A case study (MYCIN) of a KBS, a list of existing KBSs, and an introduction to the Japanese Fifth Generation Computer Project are provided as appendices. Finally, an extensive set of KBS-related references is provided at the end of the report