Evidence map›Paper›PMID 42437755›Full record

ArticleScientific reports2026

Deep learning single-cell analysis for cytologic evaluation of oral potentially malignant disorders.

Michael P McRae, Kritika S Rajsri, Nadarajah Vigneswaran, A Ross Kerr, Spencer W Redding, Martin H Thornhill, Craig Murdoch, Paul M Speight, Nancy Ruel, Rachelle Wolk and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

12 authors.

Michael P McRaeCustom DX Solutions LLC, Houston, TX, US.
Kritika S RajsriDepartment of Molecular Pathobiology, Division of Biomaterials, Bioengineering Institute, New York University College of Dentistry, New York, NY, US.
Nadarajah VigneswaranDepartment of Diagnostic and Biomedical Sciences, The University of Texas Health Science Center at Houston, Houston, TX, US.
A Ross KerrDepartment of Oral and Maxillofacial Pathology, Radiology & Medicine, New York University College of Dentistry, New York, NY, US.
Spencer W ReddingDepartment of Comprehensive Dentistry and Mays Cancer Center, The University of Texas at San Antonio Health Science Center, San Antonio, TX, US.
Martin H ThornhillDepartment of Oral & Maxillofacial Medicine, Surgery and Pathology, School of Clinical Dentistry, University of Sheffield, Sheffield, UK.
Craig MurdochDepartment of Oral & Maxillofacial Medicine, Surgery and Pathology, School of Clinical Dentistry, University of Sheffield, Sheffield, UK.
Paul M SpeightDepartment of Oral & Maxillofacial Medicine, Surgery and Pathology, School of Clinical Dentistry, University of Sheffield, Sheffield, UK.
Nancy RuelDepartment of Molecular Pathobiology, Division of Biomaterials, Bioengineering Institute, New York University College of Dentistry, New York, NY, US.
Rachelle WolkDepartment of Oral and Maxillofacial Pathology, Radiology & Medicine, New York University College of Dentistry, New York, NY, US.
Ryan R RuffDepartment of Epidemiology and Health Promotion, New York University College of Dentistry, New York, NY, US.
John T McDevittDepartment of Molecular Pathobiology, Division of Biomaterials, Bioengineering Institute, New York University College of Dentistry, New York, NY, US. mcdevitt@nyu.edu.

Funding

Oral Dysplasia and Oral Cavity Cancer Risk in Dental and Medical Surveillance Settings Using a Chairside Chip-Based Cytopathology ToolR01DE031319 · NIDCR · NEW YORK UNIVERSITY · PI JOHN T MCDEVITT · 2022 to 2026
$3.7M
National Science Foundation 2524897NIDCR NIH HHS 1R01DE031319-01NIDCR NIH HHS 1RC2DE020785-01NIDCR NIH HHS R01 DE031319
6 · The paper itself

Abstract

Oral potentially malignant disorders (OPMDs) such as leukoplakia and erythroplakia may harbor dysplasia or progress to oral squamous cell carcinoma (OSCC), yet visual inspection alone is unreliable for risk assessment. Cytology offers a minimally invasive adjunct, but conventional approaches depend on manual feature extraction and subjective review. Herein we report a deep learning (DL) object detection model that directly classifies four cell phenotypes: differentiated squamous epithelial (DSE) cells, small round (SR) cells, leukocytes, and lone nuclei. The DL model produced cytology-derived parameters that correlated strongly with histopathologic diagnoses across 692 subjects with OPMDs, OSCC, and healthy controls, including declining DSE cell proportion and increasing SR cells and leukocytes with disease severity (p < 0.0001). The oral cancer numerical index (OCNI) achieved AUROC values up to 0.99 for malignant versus healthy lesions, with excellent reliability (intra-class correlation coefficient ≥ 0.96). This reproducible, minimally invasive test provides a robust platform for early detection and surveillance of OPMDs.

Indexed as

Carcinoma, Squamous CellDeep LearningMouth NeoplasmsPrecancerous ConditionsSingle-Cell AnalysisHumansArtificial intelligenceDeep learningIntelligent cytology microfluidicsOral epithelial dysplasiaOral potentially malignant disordersOral squamous cell carcinoma

Identifiers

PMID42437755
PMCPMC13357536

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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.