Validation of Neural Network Predictions for the Outcome of Refractive Surgery for Myopia

  • سال انتشار: -612
  • محل انتشار: مجله نظریه پردازی در چشم پزشکی، دوره: 0، شماره: 2020
  • کد COI اختصاصی: JR_MEOP-NaN-2020_003
  • زبان مقاله: انگلیسی
  • تعداد مشاهده: 27
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نویسندگان

Miltos Balidis

Ioanna Papadopoulou

Dimitris Malandris

Zachos Zachariadis

Dimitrios Sakellaris

Solon Asteriadis

Marios Poulos

Zisis Gatzioufas

George Anogeianakis

چکیده

Abstract Background: Refractive surgery (RS) for myopia has made a very big progress regarding its safety and predictability of the outcome. Still, a small percentage of operations require retreatment. Therefore, both legally and ethically, patients should be informed that sometimes a corrective RS may be required. We addressed this issue using Neural Networks (NN) in RS for myopia. This was a recently developed validation study of a NN. Methods: We anonymously searched the Ophthalmica Institute of Ophthalmology and Microsurgery database for patients who underwent RS with PRK, LASEK, Epi-LASIK or LASIK between ۲۰۱۰ and ۲۰۱۸. We used a total of ۱۳ factors related to RS. Data was divided into four sets of successful RS outcomes used for training the NN, successful RS outcomes used for testing the NN performance, RS outcomes that required retreatment used for training the NN and RS outcomes that required retreatment used for testing the NN performance. We created eight independent Learning Vector Quantization (LVQ) networks, each one responding to a specific query with ۰ (for the retreat class) or ۱ (for the correct class). The results of the ۸ LVQs were then averaged so we could obtain a best estimate of the NN performance. Finally, a voting procedure was used to reach to a conclusion. Results: There was a statistically significant agreement (Cohen’s Kapp = ۰.۷۶۵۸) between the predicted and the actual results regarding the need for retreatment. Our predictions had good sensitivity (۰.۸۸۳۶) and specificity (۰.۹۱۸۶). Conclusion: We validated our previously published results and confirmed our expectations for the NN we developed. Our results allow us to be optimistic about the future of NNs in predicting the outcome and, eventually, in planning RS.

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