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Universal architecture of corneal segmental tomography biomarkers for artificial intelligence-driven diagnosis of early keratoconus
  1. Gairik Kundu1,
  2. Rohit Shetty2,
  3. Pooja Khamar2,
  4. Ritika Mullick2,
  5. Sneha Gupta2,
  6. Rudy Nuijts3,
  7. Abhijit Sinha Roy4
  1. 1Cornea and Refractive, Narayana Nethralaya, Bangalore, Karnataka, India
  2. 2Department of Cornea and Refractive Surgery, Narayana Nethralaya, Bangalore, Karnataka, India
  3. 3Department of Cornea and Refractive Surgery, Maastricht University, Maastricht, Limburg, The Netherlands
  4. 4Department of Imaging, Biomechanics and Telemedicine, Narayana Nethralaya Foundation, Bangalore, India
  1. Correspondence to Dr Abhijit Sinha Roy, Department of Imaging, Biomechanics and Telemedicine, Narayana Nethralaya Foundation, Bangalore 560010, India; asroy27{at}yahoo.com

Abstract

Aims To develop a comprehensive three-dimensional analyses of segmental tomography (placido and optical coherence tomography) using artificial intelligence (AI).

Methods Preoperative imaging data (MS-39, CSO, Italy) of refractive surgery patients with stable outcomes and diagnosed with asymmetric or bilateral keratoconus (KC) were used. The curvature, wavefront aberrations and thickness distributions were analysed with Zernike polynomials (ZP) and a random forest (RF) AI model. For training and cross-validation, there were groups of healthy (n=527), very asymmetric ectasia (VAE; n=144) and KC (n=454). The VAE eyes were the fellow eyes of KC patients but no further manual segregation of these eyes into subclinical or forme-fruste was performed.

Results The AI achieved an excellent area under the curve (0.994), accuracy (95.6%), recall (98.5%) and precision (92.7%) for the healthy eyes. For the KC eyes, the same were 0.997, 99.1%, 98.7% and 99.1%, respectively. For the VAE eyes, the same were 0.976, 95.5%, 71.5% and 91.2%, respectively. Interestingly, the AI reclassified 36 (subclinical) of the VAE eyes as healthy though these eyes were distinct from healthy eyes. Most of the remaining VAE (n=104; forme fruste) eyes retained their classification, and were distinct from both KC and healthy eyes. Further, the posterior surface features were not among the highest ranked variables by the AI model.

Conclusions A universal architecture of combining segmental tomography with ZP and AI was developed. It achieved an excellent classification of healthy and KC eyes. The AI efficiently classified the VAE eyes as ‘subclinical’ and ‘forme-fruste’.

  • cornea
  • degeneration
  • imaging
  • optics and refraction

Data availability statement

No data are available. Data not available.

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Data availability statement

No data are available. Data not available.

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Footnotes

  • Contributors GK, RS, PK, RM and SG were involved in data collection. GK, PK, RM, SG and ASR were involved in data analyses. GK, RS, ASR and RM were involved in writing of the manuscript. RS, ASR and RN were involved in critical revision the manuscript. RS, PK and ASR were involved in design of the study.

  • Funding This study was funded in part by the Indo-Dutch collaborative research project funded by Department of Biotechnology, Government of India.

  • Competing interests RS and ASR have a USA patent on imaging of Bowman’s layer and its application for assessing progression of disease.

  • Provenance and peer review Not commissioned; externally peer reviewed.