ReviewOral diseases2026
A Review on Image Processing and Fractal Analysis in Oral Potentially Malignant Disorders.
Review in Oral diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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
1 citing paper in PubMed.
- Fractal analysis in dentistry: a bibliometric analysis (1991-2024).Oral radiology · 2026Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
aimThe clinical evaluation of patients with oral potentially malignant disorders is primarily based on physical examination and observable clinical features. Clinical photographs play a key role in patient monitoring and help identify signs that may indicate malignant transformation. These images can be accessed by artificial intelligence techniques and fractal analysis, and image processing and segmentation are crucial for these tasks. This review discusses image processing techniques applied to clinical photographs of oral potentially malignant disorders, with emphasis on fractal analysis.
methodsStudies investigating fractal dimension and lacunarity as measures of lesion complexity and homogeneity were reviewed, along with research on segmentation and binarization.
resultsA search of scientific databases identified 10 studies reporting the access of texture parameters on clinical images to measure fractal dimension and lacunarity.
conclusionsThe reviewed studies showed that fractal dimension and texture parameters were useful for evaluating lesions and distinguishing normal and pathological tissues; however, there is a lack of methodological standardization. Although fractal dimension was assessed in all articles, lacunarity was examined in just one. Binarization and segmentation impact fractal dimension, lacunarity, and other texture measurements, which can be significantly enhanced by appropriate image processing. In contrast, poorly executed preprocessing may lead to misinterpretation of results.
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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.