Evidence map›Paper›PMID 39568787›Full record

SynthesisFrontiers in oral health2024

Diagnostic performance of artificial intelligence in detecting oral potentially malignant disorders and oral cancer using medical diagnostic imaging: a systematic review and meta-analysis.

Rakesh Kumar Sahoo, Krushna Chandra Sahoo, Girish Chandra Dash, Gunjan Kumar, Santos Kumar Baliarsingh, Bhuputra Panda, Sanghamitra Pati

Abstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Frontiers in oral health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 4 pooled it
–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

22 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Diagnostic imaging modalities to detect regional nodal involvement in oral squamous cell carcinoma: a systematic review and meta-analysis.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
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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

7 authors.

Rakesh Kumar SahooSchool of Public Health, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneswar, India.
Krushna Chandra SahooHealth Technology Assessment in India (HTAIn), Department of Health Research, Ministry of Health & Family Welfare, Govt. of India, New Delhi, India.
Girish Chandra DashAll India Institute of Medical Sciences, Jodhpur, India.
Gunjan KumarKalinga Institute of Dental Sciences, KIIT Deemed to be University, Bhubaneswar, India.
Santos Kumar BaliarsinghSchool of Computer Engineering, KIIT Deemed to be Uuniversity, Bhubaneswar, India.
Bhuputra PandaSchool of Public Health, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneswar, India.
Sanghamitra PatiHealth Technology Assessment in India (HTAIn), ICMR-Regional Medical Research Centre, Bhubaneswar, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Oral cancer is a widespread global health problem characterised by high mortality rates, wherein early detection is critical for better survival outcomes and quality of life. While visual examination is the primary method for detecting oral cancer, it may not be practical in remote areas. AI algorithms have shown some promise in detecting cancer from medical images, but their effectiveness in oral cancer detection remains Naïve. This systematic review aims to provide an extensive assessment of the existing evidence about the diagnostic accuracy of AI-driven approaches for detecting oral potentially malignant disorders (OPMDs) and oral cancer using medical diagnostic imaging. Methods: Adhering to PRISMA guidelines, the review scrutinised literature from PubMed, Scopus, and IEEE databases, with a specific focus on evaluating the performance of AI architectures across diverse imaging modalities for the detection of these conditions. Results: The performance of AI models, measured by sensitivity and specificity, was assessed using a hierarchical summary receiver operating characteristic (SROC) curve, with heterogeneity quantified through I Conclusion: These findings suggest that AI algorithms have the potential to function as reliable tools for the early diagnosis of OPMDs and oral cancer, offering significant advantages, particularly in resource-constrained settings. Systematic Review Registration: https://www.crd.york.ac.uk/, PROSPERO (CRD42023476706).

Indexed as

AI algorithmsdeep learningdiagnostic performanceearly detectionmedical diagnostic imagingOPMDsoral cancer

Identifiers

PMID39568787
PMCPMC11576460

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.