Information fusion for infant age estimation from deciduous teeth using machine learning.

OBJECTIVES: Over the past few years, several methods have been proposed to improve the accuracy of age estimation in infants with a focus on dental development as a reliable marker. However, traditional approaches have limitations in efficiently combining information from different teeth and features. In order to address these challenges, this article presents a study on age estimation in infants with Machine Learning (ML) techniques, using deciduous teeth. MATERIALS AND METHODS: The involved dataset comprises 114 infant skeletons from the Granada osteological collection of identified infants, aged between 5 months of gestation and 3 years of age. The samples consist of features such as the maximum length and mineralization and alveolar stages of teeth. For the purpose of designing a method capable of combining all the information available from each individual, a Multilayer Perceptron model is proposed, one of the most popular artificial neural networks. This model has been validated using the leave-one-out experimental validation protocol. Through different groups of experiments, the study examines the informativeness of the aforementioned features, individually and in combination. RESULTS: The results indicate that the fusion of different variables allows for more accurate age estimates (RMSE = 66 days) than when variables are analyzed separately (RMSE = 101 days). Additionally, the study demonstrates the benefits of involving multiple teeth, which significantly reduces the RMSE compared to a single tooth. DISCUSSION: This article underlines the clear advantages of ML-based methods, emphasizing their potential to improve the accuracy and robustness when estimating the age of infants.

Evaluating artificial intelligence for comparative radiography.

INTRODUCTION: Comparative radiography is a forensic identification and shortlisting technique based on the comparison of skeletal structures in ante-mortem and post-mortem images. The images (e.g., 2D radiographs or 3D computed tomographies) are manually superimposed and visually compared by a forensic practitioner. It requires a significant amount of time per comparison, limiting its utility in large comparison scenarios. METHODS: We propose and validate a novel framework for automating the shortlisting of candidates using artificial intelligence. It is composed of (1) a segmentation method to delimit skeletal structures' silhouettes in radiographs, (2) a superposition method to generate the best simulated "radiographs" from 3D images according to the segmented radiographs, and (3) a decision-making method for shortlisting all candidates ranked according to a similarity metric. MATERIAL: The dataset is composed of 180 computed tomographies and 180 radiographs where the frontal sinuses are visible. Frontal sinuses are the skeletal structure analyzed due to their high individualization capability. RESULTS: Firstly, we validate two deep learning-based techniques for segmenting the frontal sinuses in radiographs, obtaining high-quality results. Secondly, we study the framework's shortlisting capability using both automatic segmentations and superimpositions. The obtained superimpositions, based only on the superimposition metric, allowed us to filter out 40% of the possible candidates in a completely automatic manner. Thirdly, we perform a reliability study by comparing 180 radiographs against 180 computed tomographies using manual segmentations. The results allowed us to filter out 73% of the possible candidates. Furthermore, the results are robust to inter- and intra-expert-related errors.

Computer-aided craniofacial superimposition validation study: the identification of the leaders and participants of the Polish-Lithuanian January Uprising (1863-1864).

In 2017, a series of human remains corresponding to the executed leaders of the "January Uprising" of 1863-1864 were uncovered at the Upper Castle of Vilnius (Lithuania). During the archeological excavations, 14 inhumation pits with the human remains of 21 individuals were found at the site. The subsequent identification process was carried out, including the analysis and cross-comparison of post-mortem data obtained in situ and in the lab with ante-mortem data obtained from historical archives. In parallel, three anthropologists with diverse backgrounds in craniofacial identification and two students without previous experience attempted to identify 11 of these 21 individuals using the craniofacial superimposition technique. To do this, the five participants had access to 18 3D scanned skulls and 14 photographs of 11 different candidates. The participants faced a cross-comparison problem involving 252 skull-face overlay scenarios. The methodology follows the main agreements of the European project MEPROCS and uses the software Skeleton-ID™. Based on MEPROCS standard, a final decision was provided within a scale, assigning a value in terms of strong, moderate, or limited support to the claim that the skull and the facial image belonged (or not) to the same person for each case. The problem of binary classification, positive/negative, with an identification rate for each participant was revealed. The results obtained in this study make the authors think that both the quality of the materials used and the previous experience of the analyst play a fundamental role when reaching conclusions using the CFS technique.

Widespread resistance to anticoagulant rodenticides in Mus musculus domesticus in the city of Barcelona.

Control of rodent populations is a big challenge because of the rapid evolution of resistance to commonly used rodenticides and the collateral negative impacts that these products may have on biodiversity. Second-generation anticoagulants are very efficient but different single nucleotide polymorphisms (SNPs) in the Vkorc1 gene may confer resistance in rodents. We sequenced exons 1, 2 and 3 of the Vkorc1 gene from 111 mice (Mus musculus domesticus) captured across the city of Barcelona and found SNPs associated with resistance to first- and second-generation anticoagulants in all of them. Although most of the SNPs were associated with resistance to bromadiolone, we also found SNPs associated with resistance to brodifacoum. Out of all the individuals analyzed, 94.59 % carried mutations associated to introgression events with Mus spretus, a sympatric rodent species. Currently most of the chemical products for rodent control commercialized in the area are based on bromadiolone, although recent public control campaigns have already shifted to other products. Thus, the widespread occurrence of resistant mice to bromadiolone represents a challenge for rodent control in Barcelona and may increase the risk of secondary poisoning of animals preying on this species. Public health managers, pest control companies and citizens should be aware that the use of bromadiolone based products is ineffective and represents a risk for the environment, including human and animal health.

Effectiveness of the Modification of Sewers to Reduce the Reproduction of Culex pipiens and Aedes albopictus in Barcelona, Spain.

Mosquitoes breeding in urban sewage infrastructure are both a source of nuisance to the local population and a public health risk, given that biting mosquitoes can transmit pathogenic organisms to humans. The increasing presence of the invasive mosquito species Aedes albopictus in European cities has further exacerbated the problems already caused by native Culex pipiens. We tested the effectiveness of modifications to sewage structures as an alternative to the use of biocides to prevent mosquito breeding. The placing of a layer of concrete at the bottom of sand sewers to prevent water accumulation completely eliminated mosquito reproduction, and so eliminates the need for biocides in modified structures. Sewer modification is thus a valid low-cost alternative for mosquito control.

Automatic landmark annotation in 3D surface scans of skulls: Methodological proposal and reliability study.

BACKGROUND AND OBJECTIVES: Craniometric landmarks are essential in many biomedical applications, such as morphometric analysis or forensic identification. The process of locating landmarks is usually a manual and slow task, highly influenced by fatigue, skills and the experience of the practitioner. Localization errors are propagated and magnified in subsequent steps, which can result in incorrect measurements or assumptions. Thereby, standardization, reliability and reproducibility lay the foundations for the necessary accuracy in subsequent measurements or anatomical analysis. In this paper, we present an automatic method to annotate 3D surface skull models taking into account anatomical and geometrical features. METHODS: The proposed method follows a hybrid structure where a deformable template is used to initialize the landmark positions. Then, a refinement stage is applied using prior anatomical knowledge to ensure a correct placement. Our proposal is validated over thirty 3D skull scans of male Caucasians, acquired by hand-held surface scanning, and a set of 58 craniometric landmarks. A statistical analysis was carried out to analyze the inter- and intra-observer variability of manual annotations and the automatic results, along with a visual assessment of the final results. RESULTS: Inter-observer errors show significant differences, which are reflected in the expert consensus used as reference. The average localization error was 2.19±1.5 mm when comparing the automatic landmarks to the reference location. The subsequent visual analysis confirmed the reliability of the refinement method for most landmarks. CONCLUSIONS: Repeated manual annotations show a high variability depending on both skills and expertise of the observer, and landmarks' location and characteristics. In contrast, the automatic method provides an accurate, robust and reproducible alternative to the tedious and error-prone task of manual landmarking.

Age estimation in forensic anthropology: methodological considerations about the validation studies of prediction models.

There is currently no clear consensus on how to calculate, express, and interpret the error when validating methods for age estimation in forensic anthropology. For this reason, it is likely that researchers are commonly drawing erroneous or confusing conclusions about the existence of population differences or the need to design new and increasingly complex estimation methods. In recent years, many researchers have highlighted these limitations. They propose new lines of research focused on the use of rigorous statistics and new technologies for the development of methods for estimating age. Our main objective in this study is to contribute to the strengthening of these novel ideas, for which we show the existing empirical evidence about the inadequacy of some age estimation methods in calculating, expressing, and interpreting the errors obtained. With this aim, a total of 500 simulations have been performed, in which hypothetical research teams develop and validate methods for age estimation. The data employed in this study was obtained from the "Centers for Disease Control and Prevention (CDC) Growth Charts: United States" released in 2000. The charts relate age with height, weight, and head circumference of US male children. Five learning algorithms have been employed as age estimators. We have performed three experiments in which the following aspects have been analyzed: frequency with which "negative" results can be obtained in the validation studies; which are the most appropriate criteria to compare and select the age estimation methods; and what analysis should be employed to carry out the validation studies. The results show possible errors in the interpretation of validation studies as a consequence of the confusion of statistical concepts. To conclude, we made a proposal of "good practices" for the correct calculation, expression, and interpretation of the error when validating age estimation methods in forensic anthropology.

Estimation of 2D to 3D dimensions and proportionality indices for facial examination.

Photo-anthropometry is a metric-based facial image comparison technique where measurements of the face are taken from an image using predetermined facial landmarks. In particular, dimensions and proportionality indices (DPIs) are compared to DPIs from another facial image. Different studies concluded that photo-anthropometric facial comparison, as it is currently practiced, is unsuitable for elimination purposes. The major limitation is the need for images acquired under very restrictive, controlled conditions. To overcome this latter issue, we propose a novel methodology to estimate 3D DPIs from 2D ones. It uses computer graphic techniques to simulate thousands of facial photographs under known camera conditions and regression to derive the mathematical relationship between 2D and 3D DPIs automatically. Additionally, we present a methodology that makes use of the estimated 3D DPIs for reducing the number of potential matches of a given unknown facial photograph within a set of known candidates. The error in the estimation of the 3D DPIs can be as large as 35%, but both I and III quartiles are consistently inside the ±5% range. The methodology for filtering cases has demonstrated to be useful in the task of narrowing down the list of possible candidates for a given photograph. It is able to remove on average (validated using cross-validation technique) 57% and 24% of the negative cases, depending on the amounts of DPIs available. Limitations of the work developed together with open research lines are included within the Discussion section.

Study on the criteria for assessing skull-face correspondence in craniofacial superimposition.

Craniofacial superimposition has the potential to be used as an identification method when other traditional biological techniques are not applicable due to insufficient quality or absence of ante-mortem and post-mortem data. Despite having been used in many countries as a method of inclusion and exclusion for over a century it lacks standards. Thus, the purpose of this research is to provide forensic practitioners with standard criteria for analysing skull-face relationships. Thirty-seven experts from 16 different institutions participated in this study, which consisted of evaluating 65 criteria for assessing skull-face anatomical consistency on a sample of 24 different skull-face superimpositions. An unbiased statistical analysis established the most objective and discriminative criteria. Results did not show strong associations, however, important insights to address lack of standards were provided. In addition, a novel methodology for understanding and standardizing identification methods based on the observation of morphological patterns has been proposed.

Biomedical image segmentation using geometric deformable models and metaheuristics.

This paper describes a hybrid level set approach for medical image segmentation. This new geometric deformable model combines region- and edge-based information with the prior shape knowledge introduced using deformable registration. Our proposal consists of two phases: training and test. The former implies the learning of the level set parameters by means of a Genetic Algorithm, while the latter is the proper segmentation, where another metaheuristic, in this case Scatter Search, derives the shape prior. In an experimental comparison, this approach has shown a better performance than a number of state-of-the-art methods when segmenting anatomical structures from different biomedical image modalities.

Intensity-based image registration using scatter search.

OBJECTIVE: We present a novel intensity-based algorithm for medical image registration (IR). METHODS AND MATERIALS: The IR problem is formulated as a continuous optimization task, and our work focuses on the development of the optimization component. Our method is designed over an advanced scatter search template, and it uses a combination of restart and dynamic boundary mechanisms integrated within a multi-resolution strategy. RESULTS: The experimental validation is performed over two datasets of human brain magnetic resonance imaging. The algorithm is evaluated in both a stand-alone registration application and an atlas-based segmentation process targeted to the deep brain structures, considering a total of 16 and 18 scenarios, respectively. Five established IR techniques, both feature- and intensity-based, are considered for comparison purposes, and ground-truth data is used to quantitatively assess the quality of the results. Our approach ranked first in both studies and it is able to outperform all competitors in 12 of 16 registration scenarios and in 14 of 18 registration-based segmentation tasks. A statistical analysis confirms with high confidence (p<0.014) the accuracy and applicability of our method. CONCLUSIONS: With a proper, problem-specific design, scatter search is able to provide a robust, global optimization. The accuracy and reliability of the registration process are superior to those of classic gradient-based techniques.