Evidence map›Paper›PMID 40133572›Full record

ArticleScientific reports2025

Integration of the fluorescence based portable device with the AI tools for the real-time monitoring of oral mucosal lesions.

Pavan Kumar, Shashikant Rathod

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
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

2 authors.

Pavan KumarFaculty of Engineering and Technology (FEAT), Datta Meghe Institute of Higher Education and Research (DMIHER), Wardha, 442001, India. pavan2012iitk@gmail.com.ORCID http://orcid.org/0000-0001-6845-9959
Shashikant RathodDepartment of Instrumentation and Control Engineering, COEP Technological University, Pune, 411005, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There is a need for non-invasive, sensitive, real-time, and user-friendly optical devices integrated with artificial intelligence (AI) based tools for the detection of oral mucosal lesions at early stage. Research on the development of optical devices has been executed by several research groups for the cancer detection and it is still being continued. We have also contributed towards it by developing a steady- state fluorescence-based portable device. The in-house developed device is equipped with 405 nm laser diode, UV visible spectrometer, optical components, and other accessories. Laser light irradiated on the oral cavity of diseased (cancerous) and non-diseased (normal) groups, excites the two endogenous fluorophores namely FAD and porphyrin. We observed an enhancement in the porphyrin fluorescence of cancerous patients (OSCC and Dysplasia) than the normal group. Data analysis carried out by AI tools i.e., Naïve Bayes, LDA, and QDA showed slightly higher accuracy for QDA. QDA was able to discriminate among Normal to OSCC, Normal to Dysplasia, and Dysplasia to OSCC with accuracies of 95.34%, 100%, and 97.43% respectively.

Indexed as

Diagnosis, Computer-AssistedMouth MucosaMouth NeoplasmsSpectrometry, FluorescenceSquamous Cell Carcinoma of Head and NeckArtificial IntelligenceBayes TheoremEquipment DesignFlavin-Adenine DinucleotideFluorescenceHumansImage Processing, Computer-AssistedOptical DevicesPorphyrinsSpectrophotometry, UltravioletFlavin-Adenine DinucleotidePorphyrinsAI toolsFluorescence spectroscopyOral mucosal lesionsPorphyrin

Identifiers

PMID40133572
PMCPMC11937504

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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.