ArticleScientific reports2021
A deep learning approach for successful big-bubble formation prediction in deep anterior lamellar keratoplasty.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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.
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.
Who cites it
9 citing papers in PubMed.
- Artificial Intelligence for Predicting Postoperative Outcomes in Anterior Segment and Ocular Adnexa Surgery: A Systematic Review.Ophthalmology science · 2026Review
- [Intraoperative OCT (iOCT)/microscope-integrated OCT (MI-OCT) in anterior segment diagnostics and surgical treatment].Die Ophthalmologie · 2026Review
- AI-driven strategies for advancing corneal cell therapy: a promising frontier.Frontiers in medicine · 2025Review
- Potential applications of artificial intelligence in image analysis in cornea diseases: a review.Eye and vision (London, England) · 2024Review
- Article
- Artificial Intelligence for Anterior Segment Diseases: A Review of Potential Developments and Clinical Applications.Ophthalmology and therapy · 2023Review
- Guidelines for the application of artificial intelligence in the diagnosis of anterior segment diseases (2023).International journal of ophthalmology · 2023Article
- Understanding required to consider AI applications to the field of ophthalmology.Taiwan journal of ophthalmologyReview
- Keratoconus: exploring fundamentals and future perspectives - a comprehensive systematic review.Therapeutic advances in ophthalmologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The efficacy of deep learning in predicting successful big-bubble (SBB) formation during deep anterior lamellar keratoplasty (DALK) was evaluated. Medical records of patients undergoing DALK at the University of Cologne, Germany between March 2013 and July 2019 were retrospectively analyzed. Patients were divided into two groups: (1) SBB or (2) failed big-bubble (FBB). Preoperative images of anterior segment optical coherence tomography and corneal biometric values (corneal thickness, corneal curvature, and densitometry) were evaluated. A deep neural network model, Visual Geometry Group-16, was selected to test the validation data, evaluate the model, create a heat map image, and calculate the area under the curve (AUC). This pilot study included 46 patients overall (11 women, 35 men). SBBs were more common in keratoconus eyes (KC eyes) than in corneal opacifications of other etiologies (non KC eyes) (p = 0.006). The AUC was 0.746 (95% confidence interval [CI] 0.603-0.889). The determination success rate was 78.3% (18/23 eyes) (95% CI 56.3-92.5%) for SBB and 69.6% (16/23 eyes) (95% CI 47.1-86.8%) for FBB. This automated system demonstrates the potential of SBB prediction in DALK. Although KC eyes had a higher SBB rate, no other specific findings were found in the corneal biometric data.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.