Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes

This from Hong and coworkers under the aegis of David Klonoff. We're pleased to have participated as well. Artificial intelligence can use real-world data to create models capable of making predictions and medical diagnosis for diabetes and its complications. The aim of this commentary article is to provide a general perspective and present recent advances... Continue Reading →

Individualised risk prediction for improved chronic wound management @USC_Vascular @ResearchatUSC @ALPSlimb #AI #DeepLearning #WoundHealing

This manuscript from our combined team led by Vladica Velickovic exploring deep learning models to improve our diagnostic and therapeutic methods to measure what we manage. Significance Chronic wounds are associated with significant morbidity, marked loss of quality of life and considerable economic burden. Evidence-based risk prediction to guide improved wound prevention and treatment is... Continue Reading →

A Novel Machine Learning Approach for Severity Classification of Diabetic Foot Complications Using Thermogram Images #ActAgainstAmputation

We are seeing and better approaches to assisting us in identifying subtle asymmetries and personalized discontinuities in thermometry. This work from our Qatari colleagues is just such an example. Diabetes mellitus (DM) is one of the most prevalent diseases in the world, and is correlated to a high index of mortality. One of its major... Continue Reading →

Transcriptomic Fingerprint of Bacterial Infection in Lower Extremity Ulcers @ALPSLimb #ActAgainstAmputation

Important work from our Danish and Aussie colleagues Abstract Background and purpose: Clinicians and researchers utilize subjective, clinical classification systems to stratify lower extremity ulcer infections for treatment and research. The purpose of this study was to examine whether these clinical classifications are reflected in the ulcer's transcriptome. Methods: RNA-sequencing (RNA-seq) was performed on biopsies from clinically... Continue Reading →

An explainable machine learning model for predicting in‐hospital amputation rate of patients with diabetic foot ulcer

Just published this past week by our collective Chongqing/Singapore/and local LA squad. Diabetic foot ulcer (DFU) is one of the most serious and alarming diabetic complications, which often leads to high amputation rates in diabetic patients. Machine learning is a part of the field of artificial intelligence, which can automatically learn models from data and... Continue Reading →

Toward Machine-Learning-Based Decision Support in Diabetes Care: A Risk Stratification Study on Diabetic Foot Ulcer and Amputation

Great effort from our colleagues in Aarhus and Copenhagen. Diabetes mellitus is associated with serious complications, with foot ulcers and amputation of limbs among the most debilitating consequences of late diagnosis and treatment of foot ulcers. Thus, prediction and on-time treatment of diabetic foot ulcers (DFU) are of great importance for improving and maintaining patients'... Continue Reading →

Up ↑

%d bloggers like this: