Abstract
Purpose
To create a model for prediction of postoperative visual acuity (VA) after vitrectomy for macular hole (MH) treatment using preoperative optical coherence tomography (OCT) images, using deep learning (DL)–based artificial intelligence.
Methods
This was a retrospective single-center study. We evaluated 259 eyes that underwent vitrectomy for MHs. We divided the eyes into four groups, based on their 6-month postoperative Snellen VA values: (A) ≥ 20/20; (B) 20/25–20/32; (C) 20/32–20/63; and (D) ≤ 20/100. Training data were randomly selected, comprising 20 eyes in each group. Test data were also randomly selected, comprising 52 total eyes in the same proportions as those of each group in the total database. Preoperative OCT images with corresponding postoperative VA values were used to train the original DL network. The final prediction of postoperative VA was subjected to regression analysis based on inferences made with DL network output. We created a model for predicting postoperative VA from preoperative VA, MH size, and age using multivariate linear regression. Precision values were determined, and correlation coefficients between predicted and actual postoperative VA values were calculated in two models.
Results
The DL and multivariate models had precision values of 46% and 40%, respectively. The predicted postoperative VA values on the basis of DL and on preoperative VA and MH size were correlated with actual postoperative VA at 6 months postoperatively (P < .0001 and P < .0001, r = .62 and r = .55, respectively).
Conclusion
Postoperative VA after MH treatment could be predicted via DL using preoperative OCT images with greater accuracy than multivariate linear regression using preoperative VA, MH size, and age.
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Acknowledgements
The analysis using AI based on DL was outsourced to and conducted in collaboration with Tiwaki Co., Ltd. We thank Yoshiki Ohashi, Taro Watasue, Tomohiro Nakagawa, and Xiang Ruan for contributions to our deep learning analysis. We thank Ryan Chastain-Gross, Ph.D., from Edanz Group (https://en-author-services.edanz.com/ac) for editing a draft of this manuscript. The Clinical Research and Advanced Center at Shiga University of Medical Science assisted with statistical analysis.
Funding
Funding for this study was provided by Shiga University of Medical Sciences. The Department of Ophthalmology (Shiga University of Medical Science) provides financial support to Tiwaki Co., Ltd. The funding body had no further input into the collection, analysis, and interpretation of the data, or manuscript preparation.
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All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The study was approved by the Institutional Review Board (IRB)/Ethics Committee Shiga University of Medical Science (Otsu, Japan).
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Obata, S., Ichiyama, Y., Kakinoki, M. et al. Prediction of postoperative visual acuity after vitrectomy for macular hole using deep learning–based artificial intelligence. Graefes Arch Clin Exp Ophthalmol 260, 1113–1123 (2022). https://doi.org/10.1007/s00417-021-05427-2
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DOI: https://doi.org/10.1007/s00417-021-05427-2