ReviewJournal of biomedical optics2024
Review of machine learning for optical imaging of burn wound severity assessment.
Review in Journal of biomedical optics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed, 14 citations in OpenAlex.
- Article
- Two-step regression analysis approach to assess burn wound severity using spatial frequency domain imaging.Journal of biomedical optics · 2026Article
- Exploring gene expression heterogeneity in burn wounds through machine learning models:Annals of medicine and surgery (2012) · 2025Article
- Evidence-Based Management of Burns: A Narrative Review of Evolving Practices.European burn journal · 2025Review
- Non-invasive Resonance Raman Spectroscopy provides an early estimation of depth in a pig model of multi-depth burns.Journal of translational medicine · 2025Article
- Review
- How Can Technology Improve Burn Wound Care: A Review of Wound Imaging Technologies and Their Application in Burns-UK Experience.Diagnostics (Basel, Switzerland) · 2025Review
- Burn Progression in Human Skin-A Review of Current Knowledge and Opportunities for Future Research.Journal of burn care & research : official publication of the American Burn Association · 2025Review
- Efficient quality control of platelet-rich plasma preparation using computer vision and deep learning.Journal of biomedical optics · 2025Article
- Precision diagnosis of burn injuries using imaging and predictive modeling for clinical applications.Scientific reports · 2025Article
- An Optical Tomography-Based Score to Assess Pediatric Hand Burns.European burn journal · 2024Article
- Progress in the application of artificial intelligence in skin wound assessment and prediction of healing time.American journal of translational research · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 6 institutions in 2 countries.
Funding
Abstract
Significance: Over the past decade, machine learning (ML) algorithms have rapidly become much more widespread for numerous biomedical applications, including the diagnosis and categorization of disease and injury. Aim: Here, we seek to characterize the recent growth of ML techniques that use imaging data to classify burn wound severity and report on the accuracies of different approaches. Approach: To this end, we present a comprehensive literature review of preclinical and clinical studies using ML techniques to classify the severity of burn wounds. Results: The majority of these reports used digital color photographs as input data to the classification algorithms, but recently there has been an increasing prevalence of the use of ML approaches using input data from more advanced optical imaging modalities (e.g., multispectral and hyperspectral imaging, optical coherence tomography), in addition to multimodal techniques. The classification accuracy of the different methods is reported; it typically ranges from Conclusions: The field would benefit from systematic analysis of the effects of different input data modalities, training/testing sets, and ML classifiers on the reported accuracy. Despite this current limitation, ML-based algorithms show significant promise for assisting in objectively classifying burn wound severity.
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.