Evidence map›Paper›PMID 41681691›Full record

ReviewDiagnostics (Basel, Switzerland)2026

Machine Learning Applications for Venous Ulcer Assessment and Wound Care: A Review.

Miloš Madić, Nikola Vitković, Zoran Damnjanović, Sanja Stojanović

Abstract readReview
In one paragraph

Review in Diagnostics (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Miloš MadićDepartment of Production and Information Technologies, Faculty of Mechanical Engineering, University of Niš, 18000 Niš, Serbia.
Nikola VitkovićDepartment of Production and Information Technologies, Faculty of Mechanical Engineering, University of Niš, 18000 Niš, Serbia.ORCID 0000-0001-6956-8540
Zoran DamnjanovićDepartment of Surgery and Anesthesiology with Reanimatology, University of Niš, 18000 Niš, Serbia.
Sanja StojanovićDepartment of Biology with Human Genetics, Faculty of Medicine, University of Niš, 18000 Niš, Serbia.

Funding

Science Fund of the Republic of Serbia 7617
6 · The paper itself

Abstract

Over recent years, venous ulcer wound care has experienced significant advancements through the application of machine learning (ML) models. The aim of the present study is a systematic, comprehensive analysis of prior research studies in this field covering the period between 2001 and August 2025. By searching multiple academic databases, including the Web of Science, Scopus, and PubMed, using relevant keywords and different queries, and screening reference lists of previously published manuscripts and review papers with a focus on the application of artificial intelligence in dermatology and medicine, an initial set of potential studies for review was obtained. To ensure the scope and relevance of the review, several inclusion and exclusion criteria were used to derive the final set of relevant research studies upon which a database for research data management was created. As a result, a total of 79 relevant research studies were comprehensively analysed, upon which detailed meta-analysis and analysis of application areas of ML models within venous ulcer wound care were conducted. Afterwards, a summary of benefits for medical systems and patients was given along with a general discussion regarding ML model limitations, trends, and opportunities, as well as research studies' limitations and possible future research directions. The presented analyses may be valuable for researchers interested in applying ML models not only to venous ulcer wound care but also to other types of chronic wound care.

Indexed as

chronic woundsmachine learningvenous ulcerswound care

Identifiers

PMID41681691
PMCPMC12896939

What OpenQuestion holds

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Registered trials

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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.