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

XAS: Automatic yet eXplainable Age and Sex determination by combining imprecise per-tooth predictions

Chronological age and biological sex estimation are two key tasks in a variety of procedures, including human identification and migration control. Issues such as these have led to the development of both semiautomatic and automatic prediction models, but the former are expensive in terms of time and human resources, while the latter lack the interpretability required to be applicable in real-life scenarios. This paper therefore proposes a new, fully automatic methodology for the estimation of age and sex. This first applies a tooth detection by means of a modified CNN with the objective of extracting the oriented bounding boxes of each tooth. Then, it feeds the image features inside the tooth boxes into a second CNN module designed to produce per-tooth age and sex probability distributions. The method then adopts an uncertainty-aware policy to aggregate these estimated distributions. Our approach yielded a lower mean absolute error than any other previously described, at 0.97 years. The accuracy of the sex classification was 91.82%, confirming the suitability of the teeth for this purpose. The proposed model also allows analyses of age and sex estimations on every tooth, enabling experts to identify the most relevant for each task or population cohort or to detect potential developmental problems. In conclusion, the performance of the method in both age and sex predictions is excellent and has a high degree of interpretability, making it suitable for use in a wide range of application scenariosS

Current and emerging artificial intelligence applications for pediatric musculoskeletal radiology

Artificial intelligence (AI) is playing an ever-increasing role in radiology (more so in the adult world than in pediatrics), to the extent that there are unfounded fears it will completely take over the role of the radiologist. In relation to musculoskeletal applications of AI in pediatric radiology, we are far from the time when AI will replace radiologists; even for the commonest application (bone age assessment), AI is more often employed in an AI-assist mode rather than an AI-replace or AI-extend mode. AI for bone age assessment has been in clinical use for more than a decade and is the area in which most research has been conducted. Most other potential indications in children (such as appendicular and vertebral fracture detection) remain largely in the research domain. This article reviews the areas in which AI is most prominent in relation to the pediatric musculoskeletal system, briefly summarizing the current literature and highlighting areas for future research. Pediatric radiologists are encouraged to participate as members of the research teams conducting pediatric radiology artificial intelligence research

The reliability of cephalometric tracing using AI

Introduction : L'objectif de cette étude est de comparer la différence entre l'analyse céphalométrique manuelle et l'analyse automatisée par l’intelligence artificielle afin de confirmer la fiabilité de cette dernière. Notre hypothèse de recherche est que la technique manuelle est la plus fiable des deux méthodes. Méthode : Un total de 99 radiographies céphalométriques latérales sont recueillies. Des tracés par technique manuelle (MT) et par localisation automatisée par intelligence artificielle (AI) sont réalisés pour toutes les radiographies. La localisation de 29 points céphalométriques couramment utilisés est comparée entre les deux groupes. L'erreur radiale moyenne (MRE) et un taux de détection réussie (SDR) de 2 mm sont utilisés pour comparer les deux groupes. Le logiciel AudaxCeph version 6.2.57.4225 est utilisé pour l'analyse manuelle et l'analyse AI. Résultats : Le MRE et SDR pour le test de fiabilité inter-examinateur sont respectivement de 0,87 ± 0,61mm et 95%. Pour la comparaison entre la technique manuelle MT et le repérage par intelligence artificielle AI, le MRE et SDR pour tous les repères sont respectivement de 1,48 ± 1,42 mm et 78 %. Lorsque les repères dentaires sont exclus, le MRE diminue à 1,33 ± 1,39 mm et le SDR augmente à 84 %. Lorsque seuls les repères des tissus durs sont inclus (excluant les points des tissus mous et dentaires), le MRE diminue encore à 1,25 ± 1,09 mm et le SDR augmente à 85 %. Lorsque seuls les points de repère des tissus mous sont inclus, le MRE augmente à 1,68 ± 1,89 mm et le SDR diminue à 78 %. Conclusion: La performance du logiciel est similaire à celles précédemment rapportée dans la littérature pour des logiciels utilisant un cadre de modélisation similaire. Nos résultats révèlent que le repérage manuel a donné lieu à une plus grande précision. Le logiciel a obtenu de très bons résultats pour les points de tissus durs, mais sa précision a diminué pour les tissus mous et dentaires. Nous concluons que cette technologie est très prometteuse pour une application en milieu clinique sous la supervision du docteur.Introduction: The objective of this study is to compare the difference between manual cephalometric analysis and automatic analysis by artificial intelligence to confirm the reliability of the latter. Our research hypothesis is that the manual technique is the most reliable of the methods and is still considered the gold standard. Method: A total of 99 lateral cephalometric radiographs were collected in this study. Manual technique (MT) and automatic localization by artificial intelligence (AI) tracings were performed for all radiographs. The localization of 29 commonly used landmarks were compared between both groups. Mean radial error (MRE) and a successful detection rate (SDR) of 2mm were used to compare both groups. AudaxCeph software version 6.2.57.4225 (Audax d.o.o., Ljubljana, Slovenia) was used for both manual and AI analysis. Results: The MRE and SDR for the inter-examinator reliability test were 0.87 ± 0.61mm and 95% respectively. For the comparison between the manual technique MT and landmarking with artificial intelligence AI, the MRE and SDR for all landmarks were 1.48 ± 1.42mm and 78% respectively. When dental landmarks are excluded, the MRE decreases to 1.33 ± 1.39mm and the SDR increases to 84%. When only hard tissue landmarks are included (excluding soft tissue and dental points) the MRE decreases further to 1.25 ± 1.09mm and the SDR increases to 85%. When only soft tissue landmarks are included the MRE increases to 1.68 ± 1.89mm and the SDR decreases to 78%. Conclusion: The software performed similarly to what was previously reported in literature for software that use analogous modeling framework. Comparing the software’s landmarking to manual landmarking our results reveal that the manual landmarking resulted in higher accuracy. The software operated very well for hard tissue points, but its accuracy went down for soft and dental tissue. Our conclusion is this technology shows great promise for application in clinical settings under the doctor’s supervision

Surgical treatment and outcomes of scoliosis and cervical spine instability in Children

Structural changes in the spine are the most common children's musculoskeletal abnormalities, as they cover 70% of all musculoskeletal disorders in children and adolescent. Idiopathic scoliosis is the most common of these structural changes. The congenital structural problems of the spine form an entity of their own. Changes in the development of the spine during fetal period range from changes in individual vertebral to being part of a wider developmental disorder. Careful follow-up and research are the basis of care. Genetically induced syndromes form a wide heterogeneous group that have vertebral problems often seen in cervical development and growth. There are a number of rare diseases in this group. One of the primary aims of this thesis was to assess whether en bloc vertebral column derotation provides an efficient control or correction of thoracic rib hump as compared with no derotation in adolescents with an idiopathic scoliosis. The outcomes of hybrid and total pedicle screw instrumentation were compared in children undergoing surgery for neuromuscular scoliosis or severe scoliosis. Within the rare bone dysplastia group, we studied the outcomes of upper cervical spine fusion in this heterogeneous group.We showed that en bloc derotation provides an effective initial correction of the rib hump,but the effect diminishes during two year follow-up. Comparing hybrid technique with total pedicle screw method we proved that surgery with pedicle screw technique is more effective in correcting neuromuscular and severe scoliosis. Blood loss was significantly smaller (2000 ml) and patients had better major curve correction (two year follow-up 75% vs 59%) with less need for anteroposterior surgery when comparing these techniques in the neuromuscular group. Pedicle screw instrumentation provided shorter operative time (1 hour 39minutes), diminished blood loss(1600ml), enabled better major curve correction (73% vs 59%) with less need for anteroposterior surgery as compared with hybrid constructs in patients with severe over 90degrees scoliosis. Feasibility of different techniques were investigated in the rare disease group. Cervical spine instability in the patients with rare bone dysplasia surgery was found effective.Although results are encouraging , risks and complications are common.Surgery has become an important part of treatment in many types of spinal disorders.Better techniques evolve from old methods and procedures only if these are studied meticulously. Keywords: scoliosis, pedicle, rib hump, total pedicle screw technique, coronalbalance, sagittal balance, rare bone dysplasia, cervical spin

Carpal Bone Analysis using Geometric and Deep Learning Models

The recent trend for analyzing 3D shapes in medical application has arisen new challenges for a vast amount of research activities. Quantitative shape comparison is a fundamental problem in computer vision, geometry processing and medical imaging. This thesis is motivated by the availability of carpal bone shape dataset to develop efficient techniques for diagnosis of a variety of wrist diseases and examine human skeletal. This study is conducted in two sections. First, we propose a spectral graph wavelet approach for shape analysis of carpal bones of the human wrist. More precisely, we employ spectral graph wavelets to represent the cortical surface of a carpal bone via the spectral geometric analysis of the Laplace-Beltrami operator in the discrete domain. We then propose global spectral graph wavelet (GSGW) descriptor that is isometric invariant, efficient to compute and combines the advantages of both low-pass and band-pass filters. Subsequently, we perform experiments on shapes of the carpal bones of ten women and ten men from a publicly-available database of wrist bones. Using one-way multivariate analysis of variance (MANOVA) and permutation testing, we show through extensive experiments that the proposed GSGW framework gives a much better performance compared to the global point signature (GPS) embedding approach for comparing shapes of the carpal bones across populations. In the second section, we evaluate bone age to assess children’s biological maturity and to diagnose any growth disorders in children. Manual bone age assessment (BAA) methods are timeconsuming and prone to observer variability by even expert radiologists. These drawbacks motivate us for proposing an accurate computerized BAA method based on human wrist bones X-ray images. We also investigate automated BAA methods using state-of-the-art deep learning models that estimate the bone age more accurate than the manual methods by eliminating human observation variations. The presented approaches provide faster assessment process and cost reduction in the hospitals/clinics. The accuracy of our experiments is evaluated using mean absolute error (MAE), and the results demonstrate that exploiting InceptionResNet-V2 model in our architecture achieves higher performance compared to the other used pre-trained models