ArticleZhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences2021
Construction and verification of an intelligent measurement model for diabetic foot ulcer.
Article in Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- Construction and validation of a deep learning-based diagnostic model for segmentation and classification of diabetic foot.Frontiers in endocrinology · 2025Article
- Empowering Foot Health: Harnessing the Adaptive Weighted Sub-Gradient Convolutional Neural Network for Diabetic Foot Ulcer Classification.Diagnostics (Basel, Switzerland) · 2023Article
- A comprehensive review of methods based on deep learning for diabetes-related foot ulcers.Frontiers in endocrinology · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
objectivesThe measurement of diabetic foot ulcers is important for the success in diabetic foot ulcer management. At present, it lacks the accurate and convenient measurement tools in clinical. In recent years, artificial intelligence technology has demonstrated the potential application value in the field of image segmentation and recognition. This study aims to construct an intelligent measurement model of diabetic foot ulcers based on the deep learning method, and to conduct preliminary verification.
methodsThe data of 1 042 diabetic foot ulcers clinical samples were collected. The ulcers and color areas were manually labeled, of which 782 were used as the training data set and 260 as the test data set. The Mask RCNN ulcer tissue color semantic segmentation and RetinaNet scale digital scale target detection were used to build a model. The training data set was input into the model and iterated. The test data set was used to verify the intelligent measurement model.
resultsThis study established an intelligent measurement model of diabetic foot ulcers based on deep learning. The mean average precision@.5 intersection over union (mAP@.5IOU) of the color region segmentation in the training set and the test set were 87.9% and 63.9%, respectively; the mAP@.5IOU of the ruler scale digital detection in the training set and the test set were 96.5% and 83.4%, respectively. Compared with the manual measurement result of the test sample, the average error of the intelligent measurement result was about 3 mm.
conclusionsThe intelligent measurement model has good accuracy and robustness in measuring the diabetic foot ulcers. Future research can further optimize the model with larger-scale data samples.
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.