Evidence map›Paper›PMID 40278928›Full record

ArticleClinical oral investigations2025

Application of image enhancement in the auxiliary diagnosis of oral potentially malignant disorders.

Jiaqi Wang, Jiawang Liu, Yao Liu, Feiran Lin, Sen Yang, Xiaobing Guan

Abstract read
PubMed Publisher
In one paragraph

Article in Clinical oral investigations, 2025. 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

6 authors.

Jiaqi WangDepartment of Oral Medicine, Beijing Stomatological Hospital, Capital Medical University, No. 9, Fanjiacun Road, Fengtai District, Beijing, 100070, P.R. China.
Jiawang LiuDepartment of Oral Medicine, Beijing Stomatological Hospital, Capital Medical University, No. 9, Fanjiacun Road, Fengtai District, Beijing, 100070, P.R. China.
Yao LiuDepartment of Oral Medicine, Beijing Stomatological Hospital, Capital Medical University, No. 9, Fanjiacun Road, Fengtai District, Beijing, 100070, P.R. China.
Feiran LinDepartment of Oral Medicine, Beijing Stomatological Hospital, Capital Medical University, No. 9, Fanjiacun Road, Fengtai District, Beijing, 100070, P.R. China.
Sen YangDepartment of Oral Medicine, Beijing Stomatological Hospital, Capital Medical University, No. 9, Fanjiacun Road, Fengtai District, Beijing, 100070, P.R. China.
Xiaobing GuanDepartment of Oral Medicine, Beijing Stomatological Hospital, Capital Medical University, No. 9, Fanjiacun Road, Fengtai District, Beijing, 100070, P.R. China. guanxbing2013@qq.com.

Funding

Beijing Municipal Administration of Hospitals Incubating Program PX2025054Innovation Foundation of Beijing Stomatological Hospital, Capital Medical University 21-09-26Young Scientist Program of Beijing Stomatological Hospital, Capital Medical University YSP202306
6 · The paper itself

Abstract

objectivesThe images of oral potentially malignant disorders (OPMDs) frequently encounter problems related to visual quality and image distortion, which may lead to serious misdiagnosis and missed diagnosis. This study is to explore the auxiliary effects of different optical image enhancement algorithms on the object detection of OPMDs lesions.

methodsDigital images of OPMDs were collected, including white plaques, white stripes, and erosive lesions. The dataset was divided into a training set (6,488 images) and a validation set (2,592 images). Original images were processed using multiscale retinex (MSR), adaptive histogram equalization (AHE), and adaptive contrast enhancement (ACE), respectively. Object detection models based on You Only Look Once version 8 (YOLOv8) were used for lesion detection, and the diagnostic performance was evaluated using 328 images taken at different times.

resultsThe model performance in the MSR-enhanced image set was superior to that in the original image set, with total accuracy increased for all three lesion types, and the sensitivity of complete correct recognition for complex multi-lesion images improved. Models trained with AHE and ACE preprocessing showed reduced diagnostic performance.

conclusionImage enhancement algorithms can enhance the visual quality of OPMDs images, and the MSR algorithm is capable of strengthening the object detection ability in the computer vision model. CLINICAL RELEVANCE: This study provides an approach to reduce the misdiagnosis and missed diagnosis of OPMDs lesions in object detection model.

Indexed as

Image EnhancementMouth NeoplasmsAlgorithmsHumansImage Processing, Computer-AssistedArtificial intelligenceAuxiliary diagnosisImage enhancementOral potentially malignant disorders

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.