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A Deep Learning-Based Graphical User Interface for Predicting Corneal Ectasia Scores from Raw Optical Coherence

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Summary

A new deep learning GUI predicts corneal ectasia scores using raw optical coherence tomography data. This approach ensures consistent keratoconus diagnosis, unaffected by software changes, improving early detection.

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CNNOCTcorneadeep learningectasiaeyekeratoconusraw datavision

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Keratoconus is a progressive corneal thinning condition impacting visual acuity.
  • Early diagnosis of keratoconus is crucial for vision preservation.
  • Raw optical coherence tomography (OCT) data offers consistent analytical advantages over preprocessed data.

Purpose of the Study:

  • To develop a deep learning-based graphical user interface (GUI) for predicting corneal ectasia scores.
  • To utilize raw OCT data for enhanced diagnostic consistency in keratoconus detection.

Main Methods:

  • A GUI was built using Tkinter, accepting raw Casia2 OCT (3dv format) data.
  • Raw OCT images were processed by a modified EfficientNet-B0 convolutional neural network after cropping and resizing.
  • The GUI displays predicted corneal ectasia scores and classifications: 'No detectable ectasia pattern,' 'Suspected ectasia,' or 'Clinical ectasia.'

Main Results:

  • The EfficientNet-B0 model achieved a Mean Absolute Error of 6.65 on the test dataset.
  • For two-class classification, the model reported 87.96% accuracy, 82.41% sensitivity, and 89.33% F1 score.
  • For three-class classification, a weighted-average F1 score of 84.95% and 84.75% overall accuracy were attained.

Conclusions:

  • The developed GUI provides numerical ectasia scores, enhancing diagnostic precision beyond categorical labels.
  • Utilizing raw OCT data ensures diagnostic consistency, independent of software version updates.
  • This study demonstrates the viability of using raw OCT data for reliable keratoconus diagnosis.