Machine-Learning Assessed Abdominal Aortic Calcification is Associated with Long-Term Fall and Fracture Risk in Community-Dwelling Older Australian Women.

Abdominal aortic calcification (AAC), a recognized measure of advanced vascular disease, is associated with higher cardiovascular risk and poorer long-term prognosis. AAC can be assessed on dual-energy X-ray absorptiometry (DXA)-derived lateral spine images used for vertebral fracture assessment at the time of bone density screening using a validated 24-point scoring method (AAC-24). Previous studies have identified robust associations between AAC-24 score, incident falls, and fractures. However, a major limitation of manual AAC assessment is that it requires a trained expert. Hence, we have developed an automated machine-learning algorithm for assessing AAC-24 scores (ML-AAC24). In this prospective study, we evaluated the association between ML-AAC24 and long-term incident falls and fractures in 1023 community-dwelling older women (mean age, 75 ± 3 years) from the Perth Longitudinal Study of Ageing Women. Over 10 years of follow-up, 253 (24.7%) women experienced a clinical fracture identified via self-report every 4-6 months and verified by X-ray, and 169 (16.5%) women had a fracture hospitalization identified from linked hospital discharge data. Over 14.5 years, 393 (38.4%) women experienced an injurious fall requiring hospitalization identified from linked hospital discharge data. After adjusting for baseline fracture risk, women with moderate to extensive AAC (ML-AAC24 ≥ 2) had a greater risk of clinical fractures (hazard ratio [HR] 1.42; 95% confidence interval [CI], 1.10-1.85) and fall-related hospitalization (HR 1.35; 95% CI, 1.09-1.66), compared to those with low AAC (ML-AAC24 ≤ 1). Similar to manually assessed AAC-24, ML-AAC24 was not associated with fracture hospitalizations. The relative hazard estimates obtained using machine learning were similar to those using manually assessed AAC-24 scores. In conclusion, this novel automated method for assessing AAC, that can be easily and seamlessly captured at the time of bone density testing, has robust associations with long-term incident clinical fractures and injurious falls. However, the performance of the ML-AAC24 algorithm needs to be verified in independent cohorts. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Artificial Intelligence in the Image-Guided Care of Atrial Fibrillation.

Atrial fibrillation arises mainly due to abnormalities in the cardiac conduction system and is associated with anatomical remodeling of the atria and the pulmonary veins. Cardiovascular imaging techniques, such as echocardiography, computed tomography, and magnetic resonance imaging, are crucial in the management of atrial fibrillation, as they not only provide anatomical context to evaluate structural alterations but also help in determining treatment strategies. However, interpreting these images requires significant human expertise. The potential of artificial intelligence in analyzing these images has been repeatedly suggested due to its ability to automate the process with precision comparable to human experts. This review summarizes the benefits of artificial intelligence in enhancing the clinical care of patients with atrial fibrillation through cardiovascular image analysis. It provides a detailed overview of the two most critical steps in image-guided AF management, namely, segmentation and classification. For segmentation, the state-of-the-art artificial intelligence methodologies and the factors influencing the segmentation performance are discussed. For classification, the applications of artificial intelligence in the diagnosis and prognosis of atrial fibrillation are provided. Finally, this review also scrutinizes the current challenges hindering the clinical applicability of these methods, with the aim of guiding future research toward more effective integration into clinical practice.

Machine learning for abdominal aortic calcification assessment from bone density machine-derived lateral spine images.

BACKGROUND: Lateral spine images for vertebral fracture assessment can be easily obtained on modern bone density machines. Abdominal aortic calcification (AAC) can be scored on these images by trained imaging specialists to assess cardiovascular disease risk. However, this process is laborious and requires careful training. METHODS: Training and testing of model performance of the convolutional neural network (CNN) algorithm for automated AAC-24 scoring utilised 5012 lateral spine images (2 manufacturers, 4 models of bone density machines), with trained imaging specialist AAC scores. Validation occurred in a registry-based cohort study of 8565 older men and women with images captured as part of routine clinical practice for fracture risk assessment. Cox proportional hazards models were used to estimate the association between machine-learning AAC (ML-AAC-24) scores with future incident Major Adverse Cardiovascular Events (MACE) that including death, hospitalised acute myocardial infarction or ischemic cerebrovascular disease ascertained from linked healthcare data. FINDINGS: The average intraclass correlation coefficient between imaging specialist and ML-AAC-24 scores for 5012 images was 0.84 (95% CI 0.83, 0.84) with classification accuracy of 80% for established AAC groups. During a mean follow-up 4 years in the registry-based cohort, MACE outcomes were reported in 1177 people (13.7%). With increasing ML-AAC-24 scores there was an increasing proportion of people with MACE (low 7.9%, moderate 14.5%, high 21.2%), as well as individual MACE components (all p-trend <0.001). After multivariable adjustment, moderate and high ML-AAC-24 groups remained significantly associated with MACE (HR 1.54, 95% CI 1.31-1.80 & HR 2.06, 95% CI 1.75-2.42, respectively), compared to those with low ML-AAC-24. INTERPRETATION: The ML-AAC-24 scores had substantial levels of agreement with trained imaging specialists, and was associated with a substantial gradient of risk for cardiovascular events in a real-world setting. This approach could be readily implemented into these clinical settings to improve identification of people at high CVD risk. FUNDING: The study was supported by a National Health and Medical Research Council of Australia Ideas grant and the Rady Innovation Fund, Rady Faculty of Health Sciences, University of Manitoba.