Ocular and non-ocular diseases significantly impact millions of people worldwide, leading to vision impairment or blindness if not detected and managed early. Many individuals could be prevented from becoming blind by treating these diseases early on and stopping their progression. Despite advances in medical imaging and diagnostic tools, the manual detection of these diseases remains labor-intensive, time-consuming, and dependent on the expert's experience. Computer-aided diagnosis (CAD) has been transformed by machine learning (ML), providing promising methods for the automated detection and grading of diseases using various retinal imaging modalities. In this paper, we present a comprehensive systematic literature review that discusses the use of ML techniques to detect diseases from retinal images, utilizing both single and multi-modal imaging approaches. We analyze the efficiency of various Deep Learning and classical ML models, highlighting their achievements in accuracy, sensitivity, and specificity. Even with these advancements, the review identifies several critical challenges. We propose future research directions to address these issues. By overcoming these challenges, the potential of ML to enhance diagnostic accuracy and patient outcomes can be fully realized, opening the way for more reliable and effective ocular and non-ocular disease management.

Background/Objectives: Paediatric brain tumour cohorts are small, and deep networks trained directly on volumetric MRI often perform poorly on them. The usual response is a larger architecture. The contribution of the anatomical representation itself has not been isolated as an experimental factor, so it is not known how much of the difficulty is architectural and how much is representational. This study addresses that gap, using supratentorial/infratentorial tumour localisation as a task defined entirely by anatomical geometry. Methods: In 108 paediatric patients with multi-sequence MRI and manual annotation of the tumour and eight anatomical structures, the task, the cohort and the subject-grouped five-fold cross-validation assignment were held fixed while the representation of the input was varied: 3D convolutional networks on raw volumes, the same networks given coordinate and craniocaudal-profile priors, a geometric rule with no fitted parameters, and a patient-level graph whose nodes are anatomical structures, read out by logistic regression or by a graph attention network. All anatomy downstream of the landmarks was predicted out of fold. Comparisons were patient-paired, with bootstrap intervals and Holm-corrected McNemar tests. Results: Balanced accuracy rose from 0.522 on raw voxels to 0.652 with explicit spatial priors, 0.681 for the parameter-free geometric rule, 0.775 for a five-feature linear readout of the graph and 0.838 for the graph attention network; the same decision layer given manual masks reached 0.918. Only the graph attention network exceeded the strongest convolutional baseline significantly (+0.187, Holm-adjusted p = 0.009). Automated segmentation was associated with a 0.143 gap, driven by spatial mislocalisation rather than by missed tumours. Conclusions: For this geometrically defined task and cohort size, representing anatomical structure explicitly was more effective than learning the same geometry from voxels.

Cerebrospinal fluid (CSF) plays a crucial role in maintaining health, and its pulsatile movement driven primarily by the cardiac cycle and respiration serves as a valuable physiological signal, with abnormalities increasingly associated with neurological disorders. Phase-contrast magnetic resonance imaging (PC-MRI) offers a non-invasive means of capturing both anatomical structure and flow characteristics. In this study, we propose an unsupervised method to segment extraventricular brain regions using CSF flow dynamics derived from PC-MRI data acquired along the anterior commissure–posterior commissure (AC–PC) line. PC-MRI data were collected from eight healthy volunteers (VENC = 10 cm/s) with 23–30 images per cardiac cycle. Voxel-wise features — extracted from time-, frequency-, and wavelet-domain representations —were clustered using the K-means algorithm. An ablation study confirmed that frequency-domain features are the most discriminative single domain. The resulting segments, validated by expert neuroradiologists, showed strong alignment with anatomical regions- grey matter, white matter, vasculature, and CSF spaces- beyond the ventricles. Additionally, the CSF flow characteristics within the segmented regions were also examined and presented for comparative analysis. By focusing on extraventricular regions, this method enables a more comprehensive analysis of CSF dynamics across the entire brain, offering new insights into how CSF interacts with surrounding tissues. This brain-wide, label-free approach opens the door to earlier detection and improved monitoring of neurological conditions where CSF flow is disrupted and lays the groundwork for future research into the role of glymphatic transport, neuroinflammation, and fluid-tissue interactions in both health and disease.