Evidence map›Paper›PMID 38259111›Full record

ArticleJournal of applied clinical medical physics2024

Improvements to a GLCM-based machine-learning approach for quantifying posterior capsule opacification.

Chang Liu, Ying Hu, Yan Chen, Jian Fang, Ruhan Liu, Lei Bi, Xunan Tan, Bin Sheng, Qiang Wu

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of applied clinical medical physics, 2024. 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
4.3field-weighted citation impact, top 6% of its field
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, 8 citations in OpenAlex.

  1. Article
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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

9 authors at 4 institutions in 1 country.

Chang LiuDepartment of Ophthalmology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Ying HuDepartment of Ophthalmology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yan ChenDepartment of Ophthalmology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Jian FangDepartment of Ophthalmology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Ruhan LiuFurong Laboratory, Central South University, Changsha, Hunan, China.
Lei BiInstitute of Translational Medicine, Shanghai Jiao Tong University, Shanghai, China.
Xunan TanShanghai University of Sport School of Exercise and Health, Shanghai, China.
Bin ShengDepartment of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China.
Qiang WuDepartment of Ophthalmology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Shanghai Jiao Tong University · CNCentral South University · CNShanghai Sixth People's Hospital · CNShanghai University of Sport · CN

Funding

Clinical Study of Shanghai Municipal Health Commission 20194Y0290
6 · The paper itself

Abstract

backgroundPosterior capsular opacification (PCO) is a common complication following cataract surgery that leads to visual disturbances and decreased quality of vision. The aim of our study was to employ a machine-learning methodology to characterize and validate enhancements applied to the grey-level co-occurrence matrix (GLCM) while assessing its validity in comparison to clinical evaluations for evaluating PCO.

methodsOne hundred patients diagnosed with age-related cataracts who were scheduled for phacoemulsification surgery were included in the study. Following mydriasis, anterior segment photographs were captured using a high-resolution photographic system. The GLCM was utilized as the feature extractor, and a supported vector machine as the regressor. Three variations, namely, GLCM, GLCM+C (+axial information), and GLCM+V (+regional voting), were analyzed. The reference value for regression was determined by averaging clinical scores obtained through subjective analysis. The relationships between the predicted PCO outcome scores and the ground truth were assessed using Pearson correlation analysis and a Bland-Altman plot, while agreement between them was assessed through the Bland-Altman plot.

resultsRelative to the ground truth, the GLCM, GLCM+C, and GLCM+V methods exhibited correlation coefficients of 0.706, 0.768, and 0.829, respectively. The relationship between the PCO score predicted by the GLCM+V method and the ground truth was statistically significant (p < 0.001). Furthermore, the GLCM+V method demonstrated competitive performance comparable to that of two experienced clinicians (r = 0.825, 0.843) and superior to that of two junior clinicians (r = 0.786, 0.756). Notably, a high level of agreement was observed between predictions and the ground truth, without significant evidence of proportional bias (p > 0.05).

conclusionsOverall, our findings suggest that a machine-learning approach incorporating the GLCM, specifically the GLCM+V method, holds promise as an objective and reliable tool for assessing PCO progression. Further studies in larger patient cohorts are warranted to validate these findings and explore their potential clinical applications.

Indexed as

Capsule OpacificationCataract ExtractionLens Capsule, CrystallineHumansReproducibility of Resultscataract surgerygrey level co-occurrence matrixmachine learningposterior capsular opacification quantification

Identifiers

PMID38259111
PMCPMC10860560
OpenAlexW4391145156

What OpenQuestion holds

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LicenceCC BY
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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.