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Diabetes Mellitus

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A novel multi-feature learning model for disease diagnosis using face skin images.

Computers in biology and medicine
BACKGROUND: Facial skin characteristics can provide valuable information about a patient's underlying health conditions.

Exhaled breath signal analysis for diabetes detection: an optimized deep learning approach.

Computer methods in biomechanics and biomedical engineering
In this study, a flexible deep learning system for breath analysis is created using an optimal hybrid deep learning model. To improve the quality of the gathered breath signals, the raw data are first pre-processed. Then, the most relevant features l...

Machine Learning and Deep Learning Techniques Applied to Diabetes Research: A Bibliometric Analysis.

Journal of diabetes science and technology
BACKGROUND: The use of machine learning and deep learning techniques in the research on diabetes has garnered attention in recent times. Nonetheless, few studies offer a thorough picture of the knowledge generation landscape in this field. To address...

A novel approach for diabetic foot diagnosis: Deep learning-based detection of lower extremity arterial stenosis.

Diabetes research and clinical practice
PURPOSE OF THE STUDY: Assessing the lower extremity arterial stenosis scores (LEASS) in patients with diabetic foot ulcer (DFU) is a challenging task that requires considerable time and efforts from physicians, and it may yield varying results. The p...

A Smart Sensing Technologies-Based Intelligent Healthcare System for Diabetes Patients.

Sensors (Basel, Switzerland)
An Artificial Intelligence (AI)-enabled human-centered smart healthcare monitoring system can be useful in life saving, specifically for diabetes patients. Diabetes and heart patients need real-time and remote monitoring and recommendation-based medi...

Automated segmentation of ultra-widefield fluorescein angiography of diabetic retinopathy using deep learning.

The British journal of ophthalmology
BACKGROUND/AIMS: Retinal capillary non-perfusion (NP) and neovascularisation (NV) are two of the most important angiographic changes in diabetic retinopathy (DR). This study investigated the feasibility of using deep learning (DL) models to automatic...

Automatic interpretation and clinical evaluation for fundus fluorescein angiography images of diabetic retinopathy patients by deep learning.

The British journal of ophthalmology
BACKGROUND/AIMS: Fundus fluorescein angiography (FFA) is an important technique to evaluate diabetic retinopathy (DR) and other retinal diseases. The interpretation of FFA images is complex and time-consuming, and the ability of diagnosis is uneven a...

A framework for integrating artificial intelligence for clinical care with continuous therapeutic monitoring.

Nature biomedical engineering
The complex relationships between continuously monitored health signals and therapeutic regimens can be modelled via machine learning. However, the clinical implementation of the models will require changes to clinical workflows. Here we outline Clin...

Deep-learning-based natural-language-processing models to identify cardiovascular disease hospitalisations of patients with diabetes from routine visits' text.

Scientific reports
Writing notes is the most widespread method to report clinical events. Therefore, most of the information about the disease history of a patient remains locked behind free-form text. Natural language processing (NLP) provides a solution to automatica...

Predicting of diabetic retinopathy development stages of fundus images using deep learning based on combined features.

PloS one
The number of diabetic retinopathy (DR) patients is increasing every year, and this causes a public health problem. Therefore, regular diagnosis of diabetes patients is necessary to avoid the progression of DR stages to advanced stages that lead to b...