The four-minute approach revisited : accelerating MRI-based multi-factorial age estimation

Objectives: This feasibility study aimed to investigate the reliability of multi-factorial age estimation based on MR data of the hand, wisdom teeth and the clavicles with reduced acquisition time. Methods: The raw MR data of 34 volunteers-acquired on a 3T system and using acquisition times (TA) of 3:46 min (hand), 5:29 min (clavicles) and 10:46 min (teeth)-were retrospectively undersampled applying the commercially available CAIPIRINHA technique. Automatic and radiological age estimation methods were applied to the original image data as well as undersampled data to investigate the reliability of age estimates with decreasing acquisition time. Reliability was investigated determining standard deviation (SSD) and mean (MSD) of signed differences, intra-class correlation (ICC) and by performing Bland-Altman analysis. Results: Automatic age estimation generally showed very high reliability (SSD < 0.90 years) even for very short acquisition times (SSD ≈ 0.20 years for a total TA of 4 min). Radiological age estimation provided highly reliable results for images of the hand (ICC ≥ 0.96) and the teeth (ICC ≥ 0.79) for short acquisition times (TA = 16 s for the hand, TA = 2:21 min for the teeth), imaging data of the clavicles allowed for moderate acceleration (TA = 1:25 min, ICC ≥ 0.71). Conclusions: The results demonstrate that reliable multi-factorial age estimation based on MRI of the hand, wisdom teeth and the clavicles can be performed using images acquired with a total acquisition time of 4 min

Dental and skeletal imaging in forensic age estimation : disparities in current approaches and the continuing search for optimization

Medical imaging for forensic age estimation in living adolescents and young adults continues to be controversial and a subject of discussion. Because age estimation based on medical imaging is well studied, it is the current gold standard. However, large disparities exist between the centers conducting age estimation, both between and within countries. This review provides an overview of the most common approaches applied in Europe, with case examples illustrating the differences in imaging modalities, in staging of development, and in statistical processing of the age data. Additionally, the review looks toward the future because several European research groups have intensified studies on age estimation, exploring four strategies for optimization: (1) increasing sample sizes of the reference populations, (2) combining single-site information into multifactorial information, (3) avoiding ionizing radiation, and (4) conducting a fully automated analysis

The influence of motion artefacts on magnetic resonance imaging of the clavicles for age estimation

Purpose To determine how motion affects stage allocation to the clavicle's sternal end on MRI. Materials and methods Eighteen volunteers (9 females, 9 males) between 14 and 30 years old were prospectively scanned with 3-T MRI. One resting-state scan was followed by five intentional motion scans. Additionally, a control group of 72 resting-state scans were selected from previous research. Firstly, six observers allocated developmental stages to the clavicles independently. Secondly, they re-assessed the images, allocating developmental statuses (immature, mature). Finally, the resting-state scans of the 18 volunteers were assessed in consensus to decide on the "correct" stage/status. Results were compared between groups (control, prospective resting state, prospective motion), and between staging techniques (stages/statuses). Results Inter-observer agreement was low (Krippendorff alpha 0.23-0.67). The proportion of correctly allocated stages (64%) was lower than correctly allocated statuses (83%). Overall, intentional motion resulted in fewer assessable images and less images of sufficient evidential value. The proportion of correctly allocated stages did not differ between resting-state (64%) and motion scans (65%), while correctly allocated statuses were more prevalent in resting-state scans (83% versus 77%). Remarkably, motion scans did not render a systematically higher or lower stage/status, compared to the consensus. Conclusion Intentional motion impedes clavicle MRI for age estimation. Still, in case of obvious disturbances, the forensic expert will consider the MRI unsuitable as evidence. Thus, the development of the clavicle as such and the staging technique seem to play a more important role in allocating a faulty stage for age estimation

Towards fully automated third molar development staging in panoramic radiographs

Staging third molar development is commonly used for age assessment in sub-adults. Current staging techniques are, at most, semi-automated and rely on manual interactions prone to operator variability. The aim of this study was to fully automate the staging process by employing the full potential of deep learning, using convolutional neural networks (CNNs) in every step of the procedure. The dataset used to train the CNNs consisted of 400 panoramic radiographs (OPGs), with 20 OPGs per developmental stage per sex, staged in consensus between three observers. The concepts of transfer learning, using pre-trained CNNs, and data augmentation were used to mitigate the issues when dealing with a limited dataset. In this work, a three-step procedure was proposed and the results were validated using fivefold cross-validation. First, a CNN localized the geometrical center of the lower left third molar, around which a square region of interest (ROI) was extracted. Second, another CNN segmented the third molar within the ROI. Third, a final CNN used both the ROI and the segmentation to classify the third molar into its developmental stage. The geometrical center of the third molar was found with an average Euclidean distance of 63 pixels. Third molars were segmented with an average Dice score of 93%. Finally, the developmental stages were classified with an accuracy of 54%, a mean absolute error of 0.69 stages, and a linear weighted Cohen’s kappa coefficient of 0.79. The entire automated workflow on average took 2.72 s to compute, which is substantially faster than manual staging starting from the OPG. Taking into account the limited dataset size, this pilot study shows that the proposed fully automated approach shows promising results compared with manual staging.Internal Funds KU Leuvenhttp://link.springer.com/journal/4142021-04-01hj2020Anatom

Magnitude and associated factors of intraoperative cardiac complications among geriatric patients who undergo non-cardiac surgery at public hospitals in the southern region of Ethiopia: a multi-center cross-sectional study in 2022/2023

BackgroundIntraoperative cardiac complications are a common cause of morbidity and mortality in non-cardiac surgery. The risk of these complications increased with the average age increasing from 65. In a resource-limited setting, including our study area, the magnitude and associated factors of intraoperative cardiac complications have not been adequately investigated. The aim of this study was to assess the magnitude and associated factors of intraoperative cardiac complications among geriatric patients undergoing non-cardiac surgery.MethodsAn institutional-based multi-center cross-sectional study was conducted on 304 geriatric patients at governmental hospitals in the southern region of Ethiopia, from 20 March 2022 to 25 August 2022. Data were collected by chart review and patient interviews. Epi Data version 4.6 and SPSS version 25 were used for analysis. The variables that had association (p &lt; 0.25) were considered for multivariable logistic regression. A p value &lt; 0.05 was considered significant for association.ResultThe overall prevalence of intraoperative cardiac complications was 24.3%. Preoperative ST-segment elevation adjusted odds ratio (AOR = 2.43, CI =2.06–3.67), history of hypertension (AOR = 3.42, CI =2.02–6.08), intraoperative hypoxia (AOR = 3.5, CI = 2.07–6.23), intraoperative hypotension (AOR = 6.2 9, CI =3.51–10.94), age &gt; 85 years (AOR = 6.01, CI = 5.12–12.21), and anesthesia time &gt; 3 h (AOR =2.27, CI = 2.0.2–18.25) were factors significantly associated with intraoperative cardiac complications.ConclusionThe magnitude of intraoperative cardiac complications was high among geriatric patients who had undergone non-cardiac surgery. The independent risk factors of intraoperative cardiac complications for this population included age &gt; 85, ST-segment elevation, perioperative hypertension (stage 3 with regular treatment), duration of anesthesia &gt;3 h, intraoperative hypoxia, and intraoperative hypotension. Holistic preoperative evaluation, optimization optimal and perioperative care for preventing perioperative risk factors listed above, and knowing all possible risk factors are suggested to reduce the occurrence of complications

Estimation de l'âge dentaire chez le sujet vivant : application des méthodes d'apprentissage machine chez les enfants et les jeunes adultes

Exposé du problème : Chez l'individu vivant, l'estimation de l'âge dentaire est un paramètre utilisé en orthopédie ou en orthodontie dentofaciale, ou en pédiatrie pour situer l'individu sur sa courbe de croissance. En médecine légale l'estimation de l'âge dentaire permet d'inférer l'âge chronologique sous forme d'une régression ou d'une classification par rapport à un âge clé. Il existe des méthodes physiques et radiologiques. Si ces dernières sont plus précises, il n'existe pas de méthode universelle. Demirjian a créé il y a presque 50 ans la méthode radiologique la plus utilisée, mais elle est critiquée pour sa précision et pour l'utilisation de tables de références basées sur un échantillon de population franco-canadien. Objectif : L'intelligence artificielle et plus particulièrement l'apprentissage machine a permis le développement de différents outils ayant une capacité d'apprentissage sur une base de données annotées. L'objectif de cette thèse a été de comparer la performance de différents algorithmes d'apprentissage machine ; dans un premier temps par rapport à deux méthodes classiques d'estimation de l'âge dentaire, puis entre elles en ajoutant des prédicteurs supplémentaires. Matériel et méthode : Dans une première partie, les différentes méthodes d'estimation de l'âge dentaire sur des individus vivants enfants et jeunes adultes sont présentées. Les limites de ces méthodes sont exposées et les possibilités d'y répondre avec l'utilisation de l'apprentissage machine sont proposées. A partir d'une base de données de 3605 radiographies panoramiques d'individus âgés de 2 à 24 ans (1734 filles et 1871 garçons), différentes méthodes d'apprentissage machine ont été testées pour estimer l'âge dentaire. Les précisions de ces méthodes ont été comparées entre elles et par rapport à deux méthodes classiques de Demirjian et Willems. Ce travail a abouti à la parution d'un article dans l'International Journal of Legal Medicine. Dans une deuxième partie, les différentes méthodes d'apprentissage machine sont décrites et discutées. Puis les résultats obtenus dans l'article sont remis en perspective avec les publications sur le sujet en 2021. Enfin une mise en perspective des résultats des méthodes d'apprentissage machine par rapport à leur utilisation dans l'estimation de l'âge dentaire est réalisée. Résultats : Les résultats montrent que toutes les méthodes d'apprentissage machine présentent une meilleure précision que les méthodes classiques testées pour l'estimation de l'âge dentaire dans les conditions d'utilisation de ces dernières. Elles montrent également que l'utilisation du stade de maturation des troisièmes molaires sur une plage d'utilisation étendue à 24 ans ne permet pas l'estimation de l'âge dentaire pour une question légale. Conclusion : Les méthodes d'apprentissage machine s'intègrent dans le processus global d'automatisation de la détermination de l'âge dentaire. La partie spécifique d'apprentissage profond semble intéressante à investiguer pour des tâches de classification de l'âge dentaire.Statement of the problem: In the living individual, the estimation of dental age is a parameter used in orthopedics or dentofacial orthodontics or in pediatrics to locate the individual on its growth curve. In forensic medicine, the estimation of dental age allows to infer the chronological age for a regression or a classification task. There are physical and radiological methods. While the latter are more accurate, there is no universal method. Demirjian created the most widely used radiological method almost 50 years ago, but it is criticized for its accuracy and for using reference tables based on a French-Canadian population sample. Objective: Artificial intelligence, and more particularly machine learning, has allowed the development of various tools with a learning capacity on an annotated database. The objective of this thesis was to compare the performance of different machine learning algorithms first against two classical methods of dental age estimation, and then between them by adding additional predictors. Material and method: In a first part, the different methods of dental age estimation on living children and young adults are presented. The limitations of these methods are exposed and the possibilities to address them with the use of machine learning are proposed. Using a database of 3605 panoramic radiographs of individuals aged 2 to 24 years (1734 girls and 1871 boys), different machine learning methods were tested to estimate dental age. The accuracies of these methods were compared with each other and with two classical methods by Demirjian and Willems. This work resulted in an article published in the International Journal of Legal Medicine. In a second part, the different machine learning methods are described and discussed. Then, the results obtained in the article are put in perspective with the publications on the subject in 2021. Finally, a perspective of the results of the machine learning methods in relation to their use in dental age estimation is made. Results: The results show that all machine learning methods have better accuracy than the conventional methods tested for dental age estimation under the conditions of their use. They also show that the use of the maturation stage of third molars over an extended range of use to 24 years does not allow the estimation of dental age for a legal issue. Conclusion: Machine learning methods fit into the overall process of automating dental age determination. The specific part of deep learning seems interesting to investigate for dental age classification tasks