دورية أكاديمية
Predicting persistent central serous chorioretinopathy using multiple optical coherence tomographic images by deep learning
| العنوان: | Predicting persistent central serous chorioretinopathy using multiple optical coherence tomographic images by deep learning |
|---|---|
| المؤلفون: | Donghyun Jee, Ji Hyun Yoon, Ho Ra, Jin-woo Kwon, Jiwon Baek |
| المصدر: | Scientific Reports, Vol 12, Iss 1, Pp 1-7 (2022) |
| بيانات النشر: | Nature Portfolio, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Medicine LCC:Science |
| مصطلحات موضوعية: | Medicine, Science |
| الوصف: | Abstract We sought to predict whether central serous chorioretinopathy (CSC) will persist after 6 months using multiple optical coherence tomography (OCT) images by deep convolutional neural network (CNN). This was a multicenter, retrospective, cohort study. Multiple OCT images, including B-scan and en face images of retinal thickness (RT), mid-retina, ellipsoid zone (EZ) layer, and choroidal layer, were collected from 832 eyes of 832 CSC patients (593 self-resolving and 239 persistent). Each image set and concatenated set were divided into training (70%), validation (15%), and test (15%) sets. Training and validation were performed using ResNet50 CNN architecture for predicting CSC requiring treatment. Model performance was analyzed using the test set. The accuracy of prediction was 0.8072, 0.9200, 0.6480, and 0.9200 for B-scan, RT, mid-retina, EZ, and choroid modalities, respectively. When image sets with high accuracy were concatenated, the accuracy was 0.9520, 0.8800, and 0.9280 for B-scan + RT, B-scan + EZ, and EZ + RT, respectively. OCT B-scan, RT, and EZ en face images demonstrated good performances for predicting the prognosis of CSC using CNN. The performance improved when these sets were concatenated. The results of this study can serve as a reference for choosing an optimal treatment for CSC patients. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2045-2322 |
| Relation: | https://doaj.org/toc/2045-2322 |
| DOI: | 10.1038/s41598-022-13473-x |
| URL الوصول: | https://doaj.org/article/d71a245e426241d799f1f7f55c6fc004 |
| رقم الأكسشن: | edsdoj.71a245e426241d799f1f7f55c6fc004 |
| قاعدة البيانات: | Directory of Open Access Journals |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 20452322 |
|---|---|
| DOI: | 10.1038/s41598-022-13473-x |