Identifying risk factors for Alzheimer's disease from multivariate longitudinal clinical data using temporal pattern mining.

Background: Patient data contain a wealth of information that could aid in understanding the onset and progression of disease. However, the task of modelling clinical data, which consist of multiple heterogeneous time series of different lengths, measured at different time intervals, is a complex one. A growing body of research has applied temporal pattern mining to this problem to identify common patterns in clinical attributes over time.

Predicting visual field global and local parameters from OCT measurements using explainable machine learning.

Glaucoma is characterised by progressive vision loss due to retinal ganglion cell deterioration, leading to gradual visual field (VF) impairment. The standard VF test may be impractical in some cases, where optical coherence tomography (OCT) can offer predictive insights into VF for multimodal diagnoses. However, predicting VF measures from OCT data remains challenging.

Zirconium ion incorporated β-cyclodextrin and chitosan composites for nitrate and fluoride pollution remedy.

Cyclodextrin and chitosan are derived from the biopolymers starch and chitin respectively. Both cyclodextrin and chitosan have primary alcohol in its structure and chitosan also possesses primary amine. In this study, these reactive functional groups were utilized to convert them into anion selective adsorbents.

OCT-based diagnosis of glaucoma and glaucoma stages using explainable machine learning.

Glaucoma poses a growing health challenge projected to escalate in the coming decades. However, current automated diagnostic approaches on Glaucoma diagnosis solely rely on black-box deep learning models, lacking explainability and trustworthiness. To address the issue, this study uses optical coherence tomography (OCT) images to develop an explainable artificial intelligence (XAI) tool for diagnosing and staging glaucoma, with a focus on its clinical applicability.

- a large-scale dataset of 3D medical shapes for computer vision.

Objectives: The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models).