Evidence map›Paper›PMID 42368456›Full record

ArticleFrontiers in cell and developmental biology2026

Development and application of an artificial intelligence-based quantitative system for white without pressure in myopic retinopathy.

Pengcheng Wu, Lu Yang, Amei Wang, Yan Li, Yuqiu Zhang, Xin Zhao, Jie Wang, Yajie Zheng, Xingye Wang

Abstract read
In one paragraph

Article in Frontiers in cell and developmental biology, 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

9 authors.

Pengcheng WuDepartment of Ophthalmology, The Second Hospital and Clinical Medical School, Lanzhou University, Lanzhou, China.
Lu YangDepartment of Ophthalmology, The Second Hospital and Clinical Medical School, Lanzhou University, Lanzhou, China.
Amei WangDepartment of Ophthalmology, The Second Hospital and Clinical Medical School, Lanzhou University, Lanzhou, China.
Yan LiDepartment of Ophthalmology, The Second Hospital and Clinical Medical School, Lanzhou University, Lanzhou, China.
Yuqiu ZhangDepartment of Ophthalmology, The Second Hospital and Clinical Medical School, Lanzhou University, Lanzhou, China.
Xin ZhaoDepartment of Ophthalmology, The Second Hospital and Clinical Medical School, Lanzhou University, Lanzhou, China.
Jie WangEvision Technology (Beijing) Co., Ltd., Beijing, China.
Yajie ZhengEvision Technology (Beijing) Co., Ltd., Beijing, China.
Xingye WangEvision Technology (Beijing) Co., Ltd., Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

White Without Pressure (WWOP) is a common but under-recognized peripheral retinal finding associated with myopia-related complications, yet its objective assessment remains challenging. This study aimed to develop and validate an artificial intelligence (AI) system for the automated segmentation and quantitative assessment of WWOP lesions from ultra-widefield color fundus photographs. The system employs deep learning algorithms for automatic identification and multi-dimensional quantification of WWOP lesions, including WWOP total area, total number, zonal distribution (Area and Number for R1 (posterior pole), R2 (mid-periphery), and R3 (far-periphery)), and global area ratio. Applied to 119 myopic eyes (64 with WWOP, 53.8%), the system revealed that over 96% of the lesion area resides outside the posterior pole, predominantly in the temporal and peripheral retina. Quantitative parameters showed significant positive correlations with axial length (e.g., total area: R = 0.41, P < 0.001; peripheral area R3: R = 0.40, P < 0.001; total number: R = 0.43, P < 0.001). This AI-powered system provides an objective, standardized, and high-throughput tool for WWOP evaluation but should currently be considered a research tool requiring further validation. Nevertheless, it holds strong potential to enhance clinical screening and risk stratification in myopia management.

Indexed as

artificial intelligencehigh myopiaquantitative analysisultra-widefield imagingwhite without pressure

Identifiers

PMID42368456
PMCPMC13294601

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.