Retinopathy is characterized by pathological alterations in the retina that can lead to partial or complete vision loss. It can result from a variety of causes, including diabetes, hypertension, and autoimmune disorders, such as Autoimmune Retinopathy, which is particularly challenging to detect and diagnose. Due to its visually similar features, Autoimmune Retinopathy is often mistaken for other retinal diseases that require distinct treatments. The objective of this work is to develop a system for detecting clinically relevant anomalies in retinal images using a Transformer-based Deep Learning model. By leveraging Transformers, the model captures subtle variations in retinal images, enhancing diagnostic accuracy. This approach aims to assist ophthalmologists in precise disease detection, improving early diagnosis and enabling personalized treatment strategies.
Breast cancer remains one of the most prevalent and life-threatening diseases worldwide, being the most frequently diagnosed cancer among women and the second leading cause of cancer-related mortality. Precise tumor segmentation is essential for breast cancer assessment, as it enables accurate estimation of tumor size, monitoring of disease progression, and evaluation of treatment effectiveness. Despite the importance of this task, the development of reliable automatic methods is hindered by the scarcity of fully annotated datasets, which makes manual labeling both time-consuming and subject to inter-observer variability. In this study, we propose an unsupervised 3D tumor segmentation method based on Fuzzy C-Means (FCM) clustering, specifically designed for volumetric Dynamic Contrast-Enhanced MRI (DCE-MRI) of the breast. Unlike supervised deep learning approaches, our method does not require manual annotations for training, making it especially valuable in scenarios with limited labeled data. The proposed pipeline combines preprocessing, region-of-interest extraction, and FCM-based clustering to generate accurate segmentation masks with minimal human intervention. We evaluated our approach using clinical data from the ACRIN-6698 dataset, comparing the automatic segmentations against expert manual annotations. The method achieved high performance across multiple metrics, including accuracy, precision, recall, specificity, Dice-Sorensen coefficient (DSC), and Jaccard index (IoU). These results demonstrate the feasibility of unsupervised clustering techniques for volumetric breast tumor segmentation, offering a promising alternative to supervised methods in clinical contexts where annotated data is limited.
Obstructive Sleep Apnea (OSA) management struggles from static retrospective adherence models which build their assessment on rigid compliance benchmarks. This paper describes a modular AI-based framework for CPAP (Continuous Positive Airway Pressure) personalization, which combines real-time digital biomarkers and dynamic therapystage clustering. Patients are stratified into four progressive stages, new, adherent, attempting and non-adherent, using unsupervised learning on the CPAP usage data, clinical history, and behavioral surveys. The system dynamically adjusts biomarker tracking such as HRV, ODI and RVO, produces weekly risk scores and triggers phase specific interventions such as alerts, education modules and physician reviewed actions. Unlike previous models, the framework continually improves clusters and provides interventions depending on physiological feedback and engagement history. Preliminary timescale review shows its ability of identifying dropout threat and adaptation of interventions in response to real time surveillance. The proposed system enables scalable, explainable, and medically integrated for biomarker-driven real-time CPAP personalization methods throughout the OSA care pathway.
Lung cancer remains one of the deadliest cancers and a major public health concern. Although numerous studies have identified various risk factors, further research is essential, particularly in the biological domain. Existing data sources compile biological information on lung cancer and its subtypes but differ in structure and format, complicating data extraction and integration for artificial intelligence (AI) models. Ontologies and semantic technologies address this challenge by enabling the construction of unified knowledge graphs that promote interoperability. Lung-CABO is an ontology specifically designed for lung cancer, supporting the creation of a knowledge graph for risk factor identification and AI applications. Its modular design allows expansion to integrate additional data, such as environmental factors, further enhancing its utility and reusability.
The circadian rhythm is essential for regulating physiological functions and is compromised in people with dementia. Objective measurements are needed to track circadian trend and stability, usually from actigraphy data. This paper explores the feasibility of applying the stability model on activity counts obtained from raw accelerometer, and discusses preliminary results limited to two participants included in a randomized controlled trial that investigates the effect of virtual darkness as a treatment.
