Evidence map›Paper›PMID 37943332›Full record

ReviewGraefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie2024

Corneal biomechanics in early diagnosis of keratoconus using artificial intelligence.

Yan Huo, Xuan Chen, Gauhar Ali Khan, Yan Wang

Abstract readReview
PubMed Publisher
In one paragraph

Review in Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Linking layered corneal compactness to macroscopic biomechanics:Frontiers in bioengineering and biotechnology · 2026
    Article
  3. A Novel Approach to Fabricate Early Keratoconus Phantom Models.Translational vision science & technology · 2025
    Article
  4. Article
  5. Review
  6. 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

4 authors.

Yan HuoSchool of Medicine, Nankai University, Tianjin, China.
Xuan ChenSchool of Medicine, Nankai University, Tianjin, China.
Gauhar Ali KhanClinical College of Ophthalmology, Tianjin Medical University, Tianjin, China.
Yan WangSchool of Medicine, Nankai University, Tianjin, China. wangyan7143@vip.sina.com.ORCID http://orcid.org/0000-0002-1257-6635

Funding

Innovative Research Group Project of the National Natural Science Foundation of China Nos. 81873684 and 82271118
6 · The paper itself

Abstract

Keratoconus is a blinding eye disease that affects activities of daily living; therefore, early diagnosis is crucial. Great efforts have been made toward an early diagnosis of keratoconus. Recent studies have shown that corneal biomechanics is associated with the occurrence and progression of keratoconus. Hence, detecting changes in corneal biomechanics may provide a novel strategy for early diagnosis. However, an early keratoconus diagnosis remains challenging due to the subtle and localized nature of its lesions. Artificial intelligence has been used to help address this problem. Herein, we reviewed the literature regarding three aspects of keratoconus (keratoconus, early keratoconus, and keratoconus grading) based on corneal biomechanical properties using artificial intelligence. Furthermore, we summarized the current research progress, limitations, and possible prospects.

Indexed as

KeratoconusActivities of Daily LivingArtificial IntelligenceBiomechanical PhenomenaCorneaCorneal TopographyEarly DiagnosisHumansArtificial intelligenceCorneal biomechanicsEarly keratoconusKeratoconusMachine learning

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.