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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 associati

The early detection of Alzheimer's Disease is imperative to ensure early treatment and improve patient outcomes. There has consequently been extenstive research into detecting AD and its intermediate phase, mild cognitive impairment (MCI). However, there is very small literature in predicting the conversion to AD and MCI from normal cognitive condition. Recently, multiple studies have applied convolutional neural networks (CNN) which integrate Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) to classify MCI and AD. However, in these works, the fusion of MRI and PET features are simply achieved through concatenation, resulting in a lack of cross-modal interactions. In this paper, we propose a novel multimodal neuroimaging attention-based CNN architecture, MNA-net, to predict whether cognitively normal (CN) individuals will develop MCI or AD within a period of 10 years. To address the lack of interactions across neuroimaging modalities seen in previous works, MNA-net utilises attention mechanisms to form shared representations of the MRI and PET images. The proposed MNA-net is tested in OASIS-3 dataset and is able to predict CN individuals who converted to MCI or AD with an accuracy of 83%, true negative rate of 80%, and true positive rate of 86%. The new state of the art results improved by 5% and 10% for accuracy and true negative rate by the use of attention mechanism. These results demonstrate the potential of the proposed model to predict cognitive impairment and attention based mechanisms in the fusion of different neuroimaging modalities to improve the prediction of cognitive decline.

Cardiovascular Disease (CVD) is the leading cause of death worldwide. Calcification in the Abdominal Aorta is a stable marker of CVD development and, hence, it's early detection is considered crucial to saving lives. Imaging techniques such as Computed Tomography (CT) and Digital X-Ray Imaging can be used to accurately predict and localize Abdominal Aortic Calcification (AAC), however, these methods are not only expensive but also expose the patients to high ionizing radiation. In contrast, Dual Energy X-ray Absorptiometry (DXA) is an efficient, cost-effective and low radiation exposure-based imaging alternative, but with challenges like low resolution and vague vertebral boundaries. This poses a bottleneck in identifying the vertebrae and their boundaries which is crucial in manual as well as automatic scoring of AAC from DXA scans. In this paper, we address this research gap by proposing a framework which first localizes the vertebrae T12, L1, L2, L3, L4 and L5 and then generates Inter-Vertebral Guides (IVGs) between them. Our deep model is trained on lateral view DXA spine images and shows promising results in generating IVGs with high accuracy, which we believe can greatly reduce inter-observer variability in AAC scoring in DXA imaging domain.

Sharif, N., Nadeem, U., Shah, S. A. A., Bennamoun, M., & Liu, W. (2020). Vision to language: Methods, metrics and datasets. In G. A. Tsihrintzis & L. C. Jain (Eds.), Machine learning paradigms (pp. 9-62). Springer, Cham. https://doi.org/10.1007/978-3-030-49724-8_2