Atalla, Shadi

Graph neural networks (GNNs) are increasingly used for medical imaging analysis and biological data modeling, where the integration of radiomics, topology, geometry, and generative artificial intelligence (AI) may improve representation learning from medical images and related biomedical data. Across the reviewed literature, GNNs show particular value for modeling spatial relationships, multimodal interactions, graph-structured biological networks, and non-Euclidean imaging features that are difficult to capture using conventional convolutional architectures alone. Topology- and geometry-aware approaches further expand this capability by encoding multi-scale structure, higher-order relationships, curvature, geodesic organization, and equivariant spatial priors. Hybrid graph–transformer models and generative graph methods represent emerging directions for modeling long-range dependencies, augmenting scarce datasets, supporting synthetic pretraining, and improving representation learning in low-label or heterogeneous biomedical settings. However, clinical translation remains limited by variability in graph construction, limited external validation, computational cost, scalability constraints, interpretability challenges, and uncertainty regarding the biological realism of synthetic data. Overall, this review highlights that GNN-based medical imaging analysis is most likely to advance when graph construction is biologically justified, model performance is evaluated across diverse clinical cohorts, and technical gains are paired with transparent validation, interpretability, and implementation strategies.

Parkinson’s disease (PD) is a devastating neurological disease that cannot be identified with traditional plasma experiments, necessitating the development of a faster, less expensive diagnostic instrument. Due to the difficulty of quantifying PD in the past, doctors have tended to focus on some signs while ignoring others, primarily relying on an intuitive assessment scale because of the disease’s characteristics, which include loss of motor control and speech that can be utilized to detect and diagnose this disease. It is an illness that impacts both motion and non-motion functions. It takes years to develop and has a wide range of clinical symptoms and prognoses. Parkinson’s patients commonly display non-motor symptoms such as sleep problems, neurocognitive ailments, and cognitive impairment long before the diagnosis, even though scientists have been working to develop designs for diagnosing and categorizing the disease, only noticeable defects such as movement patterns, speech, or writing skills are offered in this paper. This article provides a thorough analysis of several AI-based ML and DL techniques used to diagnose PD and their influence on developing additional research directions. It follows the guidelines of Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). This review also examines the current state of PD diagnosis and the potential applications of data-driven AI technology. It ends with a discussion of future developments, which aids in filling critical gaps in the current Parkinson’s study.

Abstract Objective: Scar tissue is an identified cause for the development of malignant ventricular arrhythmias in patients of myocardial infarction, which ultimately leads to cardiac death, a fatal outcome. We aim to evaluate the left ventricular endocardial Scar tissue pattern using Radon descriptor-based machine learning. We performed automated LV segmentation to find the LV endocardial wall, performed morphological operations, and marked the region of the scar tissue on the endocardial wall of LV. Motivated by a Radon descriptor-based machine learning approach; the patches of 17 patients from CT images of the heart were used and categorized into “endocardial Scar tissue” and “normal tissue” groups. The ten feature vectors are extracted from patches using Radon descriptors and fed into a traditional machine learning model. Results: The decision tree has shown the best performance with 98.07% accuracy. This study is the first attempt to provide a Radon transform-based machine learning method to distinguish patterns between “endocardial Scar tissue” and “normal tissue” groups. Our proposed research method could be potentially used in advanced interventions.