ReviewTouchREVIEWS in endocrinology2021
The DFUC 2020 Dataset: Analysis Towards Diabetic Foot Ulcer Detection.
Review in TouchREVIEWS in endocrinology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Towards Home-Based Diabetic Foot Ulcer Monitoring: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- AI-Enhanced System for Diabetic Foot Ulcer Localization and Classification Using Multi-Scale Neural CNN Model.Health science reports · 2026Article
- An AI-driven framework for diabetic foot ulcer classification, segmentation, and depth estimation.Scientific reports · 2026Article
- SurgWound-Bench: a benchmark for surgical wound diagnosis.NPJ digital medicine · 2026Article
- 3D Measurement of Chronic Wounds in Routine Care: A Review with Practical Guidance for Smartphone Photogrammetry.Annals of biomedical engineering · 2026Review
- Comparing YOLO and U-net deep learning algorithms in chronic wound image segmentation.BMC medical imaging · 2026Article
- A severity-aware multi-task CNN-transformer framework for explainable diabetic foot ulcer triage and mobile deployment.Frontiers in artificial intelligence · 2026Article
- Hybrid GAN-LSTM framework for diabetic foot ulcer image synthesis and automated diagnosis.Frontiers in medicine · 2026Article
- Advances in Image-Based Diagnosis of Diabetic Foot Ulcers Using Deep Learning and Machine Learning: A Systematic Review.Biomedicines · 2025Review
- Combining the Variational and Deep Learning Techniques for Classification of Video Capsule Endoscopic Images.Journal of imaging informatics in medicine · 2025Article
- UFOS-Net leverages small-scale feature fusion for diabetic foot ulcer segmentation.Scientific reports · 2025Article
- Developing an AI-powered wound assessment tool: a methodological approach to data collection and model optimization.BMC medical informatics and decision making · 2025Observational
- Advancing Diabetic Foot Ulcer Care: AI and Generative AI Approaches for Classification, Prediction, Segmentation, and Detection.Healthcare (Basel, Switzerland) · 2025Review
- E-DFu-Net: An efficient deep convolutional neural network models for Diabetic Foot Ulcer classification.Biomolecules & biomedicine · 2025Article
- Artificial Intelligence to Diagnose Complications of Diabetes.Journal of diabetes science and technology · 2025Review
- DFU_DIALNet: Towards reliable and trustworthy diabetic foot ulcer detection with synergistic confluence of Grad-CAM and LIME.PloS one · 2025Article
- Hyperspectral reconstruction for mobile diabetic foot blood perfusion monitoring.BMC artificial intelligence · 2025Article
- Advances in Machine Learning-Aided Thermal Imaging for Early Detection of Diabetic Foot Ulcers: A Review.Biosensors · 2024Review
- Machine learning-driven discovery of novel therapeutic targets in diabetic foot ulcers.Molecular medicine (Cambridge, Mass.) · 2024Article
- Differential Diagnosis of Diabetic Foot Osteomyelitis and Charcot Neuropathic Osteoarthropathy with Deep Learning Methods.Journal of imaging informatics in medicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
Every 20 seconds a limb is amputated somewhere in the world due to diabetes. This is a global health problem that requires a global solution. The International Conference on Medical Image Computing and Computer Assisted Intervention challenge, which concerns the automated detection of diabetic foot ulcers (DFUs) using machine learning techniques, will accelerate the development of innovative healthcare technology to address this unmet medical need. In an effort to improve patient care and reduce the strain on healthcare systems, recent research has focused on the creation of cloud-based detection algorithms. These can be consumed as a service by a mobile app that patients (or a carer, partner or family member) could use themselves at home to monitor their condition and to detect the appearance of a DFU. Collaborative work between Manchester Metropolitan University, Lancashire Teaching Hospitals and the Manchester University NHS Foundation Trust has created a repository of 4,000 DFU images for the purpose of supporting research toward more advanced methods of DFU detection. This paper presents a dataset description and analysis, assessment methods, benchmark algorithms and initial evaluation results. It facilitates the challenge by providing useful insights into state-of-the-art and ongoing research.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.