ArticleAsian Pacific journal of cancer prevention : APJCP2026
Detection of Stages of Oral Cavity Lesions Using a Fluorescence Portable Device and Their Classification by Artificial Intelligence Tools.
Article in Asian Pacific journal of cancer prevention : APJCP, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveIn the present study, we utilized a fluorescence-based portable device for the detection of oral lesions, as well as AI tools for the classification of the spectroscopic data. The portable device comprises optical components (lens, filters, and beam splitter), a laser, a spectrometer, and other accessories (laptop, fibers, etc.). Laser light (λexc= 405 nm), irradiated onto the lateral border of the tongue (LBT) of the oral cavity tissue, excites the fluorophores FAD (λemi ≈ 500 nm) and Porphyrin (λemi ≈ 634 nm) more significantly than the others.
methodsFluorescence spectra were recorded from three groups, i.e., OSCC, Dysplastic, and Normal, in the range of 450 to 750 nm. Among these groups, OSCC and Dysplastic patients exhibited a significant rise in porphyrin fluorescence.
resultData analysis was accomplished using AI tools, namely Principal Component Analysis (PCA), K-Nearest Neighbors (KNN), Naïve Bayes, Linear and Quadratic Discriminant Analysis (LDA & QDA), and ROC analysis. Among these AI tools, the PCA-based QDA classifier was capable of discerning among the groups with slightly higher accuracy values (≈ 98%) than the other AI tools.
conclusionResults reveal that the in-house-built fluorescence device, along with QDA, would be an elegant tool for the identification of oral lesions at the preliminary stage.
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.