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Application of Optical Coherence Tomography to a Mouse Model of Retinopathy
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Predicting persistent central serous chorioretinopathy using multiple optical coherence tomographic images by deep

Donghyun Jee1, Ji Hyun Yoon2, Ho Ra2

  • 1Department of Ophthalmology, St. Vincent Hospital, College of Medicine, The Catholic University of Korea, Suwon, Gyeonggi-do, Republic of Korea.

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|June 6, 2022
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Deep convolutional neural networks accurately predict central serous chorioretinopathy (CSC) persistence using optical coherence tomography (OCT) images. Combining OCT B-scan, retinal thickness, and ellipsoid zone data improves prediction accuracy for CSC prognosis.

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Central serous chorioretinopathy (CSC) is a condition affecting vision.
  • Predicting CSC persistence is crucial for effective treatment selection.
  • Optical coherence tomography (OCT) provides detailed retinal imaging.

Purpose of the Study:

  • To predict the 6-month persistence of CSC using deep convolutional neural networks (CNNs) and OCT images.
  • To evaluate the performance of different OCT modalities and their combinations for CSC prognosis prediction.
  • To assess the utility of CNNs in identifying CSC cases requiring intervention.

Main Methods:

  • A multicenter, retrospective cohort study involving 832 CSC patients.
  • Collection of multiple OCT images (B-scan, en face) including retinal thickness (RT), ellipsoid zone (EZ), and choroidal layers.
  • Training and validation of a ResNet50 CNN model on 70% and 15% of data, respectively, with testing on the remaining 15%.

Main Results:

  • Individual OCT modalities showed varying prediction accuracies: B-scan (0.8072), RT (0.9200), mid-retina (0.6480), EZ (0.9200), and choroid (0.9200).
  • Concatenated image sets significantly improved prediction accuracy: B-scan + RT (0.9520), B-scan + EZ (0.8800), and EZ + RT (0.9280).
  • CNNs demonstrated strong performance in predicting CSC prognosis, especially with combined OCT data.

Conclusions:

  • OCT B-scan, RT, and EZ en face images, analyzed by CNNs, are effective for predicting CSC prognosis.
  • Combining these OCT image sets enhances predictive accuracy, offering a valuable tool for clinical decision-making.
  • This study provides a reference for selecting optimal treatments for CSC patients based on predictive imaging analysis.