On diabetic foot, wound healing, amputation prevention, and the merger of consumer tech with medical devices
I Reflections on new work from the International Flow and Toe Research Team (iFORT) One of the most persistent challenges in limb preservation is not the first ulcer โ it is the next one. Recurrence remains a defining feature of the disease, with up to 60% of people developing a new ulcer within three years... Continue Reading →
A new manuscript in the Journal of Diabetes Science and Technology from our global team explores how machine learning is being used to detect, diagnose, and even predict complications of diabetes. The paper, authored by Agatha Scheideman, Mandy Shao, Henry Zelada, Jorge Cuadros, Joshua Foreman, Pinaki Sarder, Cindy Ho, Niels Ejskjaer, Jesper Fleischer, Simon Lebech... Continue Reading →
In the realm of medical imaging and diagnostics, the Diabetic Foot Ulcers Grand Challenge (DFUC) 2022 stands out as a potential milemarker. Spearheaded by Moi Hoon Yap and coworkers, this challenge has pushed the boundaries of what's possible in the segmentation of diabetic foot ulcers (DFUs) using deep learning techniques. The challenge not only provided... Continue Reading →
"Healing Profiles in Patients with a Chronic Diabetic Foot Ulcer: An Exploratory Study with Machine Learning" focuses on the complex healing process of diabetic foot ulcers (DFU). It uses machine learning and decision trees to assess healing and prognosis risk profiles in patients. Evaluating various factors, including clinical, biochemical, and psychological aspects, the study identifies... Continue Reading →
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 →
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 →
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 →
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 →
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 →
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 →
