Deep learning-based algorithm for staging secondary caries in bitewings.

INTRODUCTION: Despite the notable progress in developing artificial intelligence (AI)-based tools for caries detection in bitewings, limited research has addressed the detection and staging of secondary caries. Therefore, we aimed to develop a Convolutional neural network (CNN)-based algorithm for these purposes using a novel approach for determining lesion severity. METHODS: We used a dataset from a Dutch dental practice-based research network containing 2,612 restored teeth in 413 bitewings from 383 patients aged 15 to 88 years and trained the Mask R-CNN architecture with a Swin Transformer backbone. Two-stage training fine-tuned caries detection accuracy and severity assessment. Annotations of caries around restorations were made by two evaluators and checked by two other experts. Aggregated accuracy metrics (mean ± Standard Deviation - SD) in detecting teeth with secondary caries were calculated considering two thresholds: detecting all lesions and dentine lesions. The correlation between the lesion severity scores obtained with the algorithm and the annotators' consensus was determined using the Pearson correlation coefficient and Bland-Altman plots. RESULTS: Our refined algorithm showed high specificity in detecting all lesions (0.966 ± 0.025) and dentine lesions (0.964 ± 0.019). Sensitivity values were lower: 0.737 ± 0.079 for all lesions and 0.808 ± 0.083 for dentine lesions. The areas under ROC curves (SD) were 0.940 (0.025) for all lesions and 0.946 (0.023) for dentine lesions. The correlation coefficient for severity scores was 0.802. CONCLUSION: We developed an improved algorithm to support clinicians in detecting and staging secondary caries in bitewing, incorporating an innovative approach for annotation, considering the lesion severity as a continuous outcome.

Thoracic Aortic Diameter and Cardiovascular Events and Mortality among Women and Men.

Background Thoracic aortic diameter may have a role as a biomarker for major adverse cardiovascular events. Purpose To evaluate the sex-specific association of the diameters of the ascending (AA) and descending (DA) thoracic aorta with risk of stroke, coronary heart disease, heart failure, cardiovascular mortality, and all-cause mortality. Materials and Methods Study participants from the population-based Rotterdam Study who underwent multidetector-row CT between 2003 and 2006 were evaluated. Cox proportional hazard models were conducted to evaluate the associations of AA and DA diameters indexed and not indexed for body mass index (BMI) with cardiovascular events and mortality for men and women. Hazard ratios (HRs) were calculated per 1-unit greater SD of aortic diameters. Results A total of 2178 participants (mean age, 69 years; 55% women) were included. Mean follow-up was 9 years. Each 0.23-mm/(kg/m2) larger BMI-indexed AA diameter was associated with a 33% higher cardiovascular mortality risk in women (HR, 1.33; 95% CI: 1.03, 1.73). Each 0.16-mm/(kg/m2) larger BMI-indexed DA diameter was associated with a 38% higher risk of stroke (HR, 1.38; 95% CI: 1.07, 1.78) and with a 46% greater risk of cardiovascular mortality (HR, 1.46; 95% CI: 1.10, 1.94) in women. Larger BMI-indexed AA and DA diameters were associated with greater risk of all-cause mortality in both sexes. Conclusion Larger ascending and descending thoracic aortic diameters indexed by body mass index were associated with greater risk of adverse cardiovascular outcomes and mortality in women and men. Clinical trial registration no. NTR6831 © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Williams in this issue.

Growth patterns and shape development of the paediatric mandible - A 3D statistical model.

Background/aim: To develop a 3D morphable model of the normal paediatric mandible to analyse shape development and growth patterns for males and females. Methods: Computed tomography (CT) data was collected for 242 healthy children referred for CT scan between 2011 and 2018 aged between 0 and 47 months (mean, 20.6 ± 13.4 months, 59.9% male). Thresholding techniques were used to segment the mandible from the CT scans. All mandible meshes were annotated using a defined set of 52 landmarks and processed such that all meshes followed a consistent triangulation. Following this, the mandible meshes were rigidly aligned to remove translation and rotation effects, while size effects were retained. Principal component analysis (PCA) was applied to the processed meshes to construct a generative 3D morphable model. Partial least squares (PLS) regression was also applied to the processed data to extract the shape modes with which to evaluate shape differences for age and sex. Growth curves were constructed for anthropometric measurements. Results: A 3D morphable model of the paediatric mandible was constructed and validated with good generalisation, compactness, and specificity. Growth curves of the assessed anthropometric measurements were plotted without significant differences between male and female subjects. The first principal component was dominated by size effects and is highly correlated with age at time of scan (Spearman's r = 0.94, p < 0.01). As with PCA, the first extracted PLS mode captures much of the size variation within the dataset and is highly correlated with age (Spearman's r = -0.94, p < 0.01). Little correlation was observed between extracted shape modes and sex with either PCA or PLS for this study population. Conclusion: The presented 3D morphable model of the paediatric mandible enables an understanding of mandibular shape development and variation by age and sex. It allowed for the construction of growth curves, which contains valuable information that can be used to enhance our understanding of various disorders that affect the mandibular development. Knowledge of shape changes in the growing mandible has potential to improve diagnostic accuracy for craniofacial conditions that impact the mandibular morphology, objective evaluation, surgical planning, and patient follow-up.

Sex-specific distributions and determinants of thoracic aortic diameters in the elderly.

OBJECTIVE: To provide population-based distributions of thoracic aortic diameters in men and women aged 55 years or older and to identify determinants of thoracic aortic diameters. METHODS: From 2003 to 2006, 2505 participants (1208 men, mean age 69.1±6.8 years) from the prospective population-based Rotterdam Study underwent non-enhanced cardiac CT. The diameter of the ascending (AA) and descending aorta (DA) was measured at the level of the pulmonary bifurcation. RESULTS: The mean diameter of the ascending and descending aorta was substantially larger in men (38±4 mm and 30±2 mm) than in women (35±3 mm and 27±2 mm). An ascending aortic diameter of larger than 40 mm was found in 228 (18.9%) men and 76 (5.9%) women and a descending aortic diameter larger than 40 mm was found in two men and no women. Male sex was found to be independently associated with larger DA diameter (standardised ß 0.24, 95% CI 0.19 to 0.30), while a statistically non-significant trend was found for the AA diameter (standardised ß 0.06, 95% CI 0.00 to 0.12). Age, height, weight and traditional cardiovascular risk factors were also associated with larger AA and/or DA diameters. Diabetes was associated with smaller AA and DA diameters. We found no evidence for effect modification by sex. CONCLUSIONS: In persons aged 55 years or older, an ascending aortic diameter of 40 mm or larger was found in 18.9% of men and 5.9% of women. Given the importance of sex, sex-specific distribution values may prove useful in clinical practice, even when correcting for body surface area or height.