Hayashi, Takahiko, Masumoto, Hiroki, Tabuchi, Hitoshi, Ishitobi, Naofumi, Tanabe, Mao, Gruen, Michael, Bachmann, Bjoern, Cursiefen, Claus and Siebelmann, Sebastian (2021). A deep learning approach for successful big-bubble formation prediction in deep anterior lamellar keratoplasty. Sci Rep, 11 (1). BERLIN: NATURE PORTFOLIO. ISSN 2045-2322
Full text not available from this repository.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.
Item Type: | Journal Article | ||||||||||||||||||||||||||||||||||||||||
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URN: | urn:nbn:de:hbz:38-592437 | ||||||||||||||||||||||||||||||||||||||||
DOI: | 10.1038/s41598-021-98157-8 | ||||||||||||||||||||||||||||||||||||||||
Journal or Publication Title: | Sci Rep | ||||||||||||||||||||||||||||||||||||||||
Volume: | 11 | ||||||||||||||||||||||||||||||||||||||||
Number: | 1 | ||||||||||||||||||||||||||||||||||||||||
Date: | 2021 | ||||||||||||||||||||||||||||||||||||||||
Publisher: | NATURE PORTFOLIO | ||||||||||||||||||||||||||||||||||||||||
Place of Publication: | BERLIN | ||||||||||||||||||||||||||||||||||||||||
ISSN: | 2045-2322 | ||||||||||||||||||||||||||||||||||||||||
Language: | English | ||||||||||||||||||||||||||||||||||||||||
Faculty: | Unspecified | ||||||||||||||||||||||||||||||||||||||||
Divisions: | Unspecified | ||||||||||||||||||||||||||||||||||||||||
Subjects: | no entry | ||||||||||||||||||||||||||||||||||||||||
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URI: | http://kups.ub.uni-koeln.de/id/eprint/59243 |
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