A deep learning approach for successful big-bubble formation prediction in deep anterior lamellar keratoplasty
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...
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Nature Portfolio
2021
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oai:doaj.org-article:b919681a42f845cd992eb3f80e6cb74e2021-12-02T15:15:35ZA deep learning approach for successful big-bubble formation prediction in deep anterior lamellar keratoplasty10.1038/s41598-021-98157-82045-2322https://doaj.org/article/b919681a42f845cd992eb3f80e6cb74e2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98157-8https://doaj.org/toc/2045-2322Abstract 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.Takahiko HayashiHiroki MasumotoHitoshi TabuchiNaofumi IshitobiMao TanabeMichael GrünBjörn BachmannClaus CursiefenSebastian SiebelmannNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-6 (2021) |
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Medicine R Science Q Takahiko Hayashi Hiroki Masumoto Hitoshi Tabuchi Naofumi Ishitobi Mao Tanabe Michael Grün Björn Bachmann Claus Cursiefen Sebastian Siebelmann A deep learning approach for successful big-bubble formation prediction in deep anterior lamellar keratoplasty |
| description |
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. |
| format |
article |
| author |
Takahiko Hayashi Hiroki Masumoto Hitoshi Tabuchi Naofumi Ishitobi Mao Tanabe Michael Grün Björn Bachmann Claus Cursiefen Sebastian Siebelmann |
| author_facet |
Takahiko Hayashi Hiroki Masumoto Hitoshi Tabuchi Naofumi Ishitobi Mao Tanabe Michael Grün Björn Bachmann Claus Cursiefen Sebastian Siebelmann |
| author_sort |
Takahiko Hayashi |
| title |
A deep learning approach for successful big-bubble formation prediction in deep anterior lamellar keratoplasty |
| title_short |
A deep learning approach for successful big-bubble formation prediction in deep anterior lamellar keratoplasty |
| title_full |
A deep learning approach for successful big-bubble formation prediction in deep anterior lamellar keratoplasty |
| title_fullStr |
A deep learning approach for successful big-bubble formation prediction in deep anterior lamellar keratoplasty |
| title_full_unstemmed |
A deep learning approach for successful big-bubble formation prediction in deep anterior lamellar keratoplasty |
| title_sort |
deep learning approach for successful big-bubble formation prediction in deep anterior lamellar keratoplasty |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/b919681a42f845cd992eb3f80e6cb74e |
| work_keys_str_mv |
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