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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Screening Candidates for Refractive Surgery With Corneal Tomographic-Based Deep Learning.

Yi Xie1, Lanqin Zhao1, Xiaonan Yang1

  • 1State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.

JAMA Ophthalmology
|March 28, 2020
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Summary

A new deep learning model, the Pentacam InceptionResNetV2 Screening System (PIRSS), accurately screens refractive surgery candidates by analyzing corneal tomographic scans. This artificial intelligence system shows high detection accuracy, comparable to experienced ophthalmologists, for identifying at-risk corneas.

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Pre-refractive surgery screening requires evaluation of corneal morphology to exclude patients with at-risk corneas or keratoconus.
  • Previous screening methods utilized machine learning based on specific corneal parameters.
  • Deep learning algorithms had not yet been combined with corneal tomographic scans for this purpose.

Purpose of the Study:

  • To investigate the efficacy of a deep learning model for screening refractive surgery candidates.
  • To develop and evaluate an artificial intelligence system for analyzing corneal tomographic scans.

Main Methods:

  • A diagnostic, cross-sectional study was conducted using 6465 corneal tomographic images from 1385 patients.
  • The Pentacam HR system was employed for data collection.
  • A deep learning model, Pentacam InceptionResNetV2 Screening System (PIRSS), was developed and trained on deidentified images, with analysis performed by ophthalmologists and the AI model.

Main Results:

  • The PIRSS model achieved an overall detection accuracy of 94.7% on the validation dataset and 95% on an independent test dataset.
  • The PIRSS model's performance was comparable to that of senior ophthalmologists (92.8%).
  • PIRSS demonstrated superior performance (95% vs. 81%) in distinguishing contraindications compared to existing Pentacam HR system classifiers in an Asian patient cohort.

Conclusions:

  • The PIRSS deep learning model is effective in classifying corneal images and identifying at-risk corneas for refractive surgery.
  • PIRSS can assist refractive surgeons in screening candidates and has potential for broader clinical application in Asian populations.
  • Further validation in diverse populations is recommended to confirm its generalizability.