Evidence map›Paper›PMID 41792236›Full record

ArticleScientific reports2026

AI-guided surgical decision support system for corneal laceration repair: a prospective, non-randomized controlled feasibility study.

Jiusheng Zheng, Huamei Lu, Yiyi Chen, Wenting Liu, Wei Liu

Abstract read
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. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

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0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Jiusheng Zheng *Department of Ophthalmology, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, China.
Huamei Lu *Department of Ophthalmology, The Affiliated Suqian First People's Hospital of Nanjing Medical University, Suqian, Jiangsu, China.
Yiyi Chen *Department of Ophthalmology, The Yangzhong People's Hospital, Affiliated Hospital of Medical College, Yangzhou University, Yangzhong, Jiangsu, China.
Wenting LiuDepartment of Ophthalmology, Huadong Hospital Affiliated to Fudan University, Shanghai, China.
Wei LiuDepartment of Ophthalmology, The Affiliated Suqian First People's Hospital of Nanjing Medical University, Suqian, Jiangsu, China. m13813182298@163.com.

Funding

the Jiangsu Province Traditional Chinese Medicine Technology Development Program MS2022136
6 · The paper itself

Abstract

To evaluate the feasibility and application value of a transfer learning-based artificial intelligence (AI) system for wound recognition and suture position planning in patients with corneal lacerations. In this prospective, non-randomized controlled feasibility study, 25 consecutive patients were managed with either conventional suturing (n = 14) or AI-guided planning (n = 11) based on real-time system availability. The AI system automatically identified corneal laceration images and generated suture points. Best-corrected visual acuity (BCVA, logMAR) and cylinder (Cyl, diopters (D)) were recorded. Anterior corneal surface parameters, including corneal surface variation index (ISV), vertical asymmetry index (IVA), high asymmetry index (IHA), high eccentricity index (IHD), and minimum curvature radius (Rmin), were measured using Pentacam. All patients were followed up for at least 6 months. The transfer learning model achieved over 85% accuracy and sensitivity in corneal laceration recognition. AI guidance significantly improved visual outcomes, with a higher BCVA (0.677 vs. 0.331 logMAR, P < 0.05) and reduced astigmatism (Cyl: 2.023 D vs. 3.542 D, P < 0.05) compared to conventional suturing, and improved ISV, IVA, and IHA (all P < 0.05) compared to the conventional group. This feasibility study provides preliminary evidence that transfer learning-based wound recognition and suture position planning can be applied to corneal laceration treatment, suggesting its potential to provide more precise and effective suturing strategies for corneal trauma patients. Further validation with larger cohorts is warranted.

Indexed as

Artificial IntelligenceCorneal InjuriesLacerationsSurgery, Computer-AssistedAdolescentAdultCorneaFeasibility StudiesFemaleHumansIntelligent SystemsMaleMiddle AgedProspective StudiesSuture TechniquesVisual AcuityAnterior corneal surfaceArtificial intelligenceCorneal lacerationFeasibility studySuture position planning

Identifiers

PMID41792236
PMCPMC13083971

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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.