June 2022
Volume 63, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2022
Detecting keratoconus on two different populations using an unsupervised hybrid artificial intelligence model.
Author Affiliations & Notes
  • Rossen M Hazarbassanov
    Department of Ophthalmology and Visual Sciences, Universidade Federal de Sao Paulo Escola Paulista de Medicina, Sao Paulo, SP, Brazil
  • Zaid Abdi Alkareem Alyasseri
    ECE Department-Faculty of Engineering, University of Kufa, Kufa, Najaf, Iraq
    Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
  • Ali Al-Timemy
    Biomedical Engineering Department, AL-Khwarizmi College of Engineering, University of Baghdad, Baghdad, Baghdad , Iraq
  • Alexandru Lavric
    Computers, Electronics and Automation Department, Stefan cel Mare University of Suceava, SUceava, Romania
  • Ammar Kamal Abasid
    Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, Ajman, United Arab Emirates
  • Hidenori Takahashi
    Department of Ophthalmology, Jichi Ika Daigaku, Shimotsuke, Tochigi, Japan
  • Jose Arthur Milhomens Filho
    Department of Ophthalmology and Visual Sciences, Universidade Federal de Sao Paulo Escola Paulista de Medicina, Sao Paulo, SP, Brazil
  • Mauro Campos
    Department of Ophthalmology and Visual Sciences, Universidade Federal de Sao Paulo Escola Paulista de Medicina, Sao Paulo, SP, Brazil
  • Siamak Yousefi
    Department of Ophthalmology, The University of Tennessee Health Science Center, Memphis, Tennessee, United States
    Department of Genetics, Genomics, and Informatics, The University of Tennessee Health Science Center, Memphis, Tennessee, United States
  • Footnotes
    Commercial Relationships   Rossen Hazarbassanov None; Zaid Abdi Alkareem Alyasseri None; Ali Al-Timemy None; Alexandru Lavric None; Ammar Abasid None; Hidenori Takahashi None; Jose Arthur Milhomens Filho None; Mauro Campos None; Siamak Yousefi None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2022, Vol.63, 2088 – F0077. doi:
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      Rossen M Hazarbassanov, Zaid Abdi Alkareem Alyasseri, Ali Al-Timemy, Alexandru Lavric, Ammar Kamal Abasid, Hidenori Takahashi, Jose Arthur Milhomens Filho, Mauro Campos, Siamak Yousefi; Detecting keratoconus on two different populations using an unsupervised hybrid artificial intelligence model.. Invest. Ophthalmol. Vis. Sci. 2022;63(7):2088 – F0077.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Several supervised machine learning models have been proposed to assist in keratoconus (KCN) detection, however, they require numerous well annotated data. Herein, we propose a new unsupervised hybrid artificial intelligence model to detect KCN.

Methods : We have developed an unsupervised model based on the k-means algorithm and adapted flower pollination algorithm (FPA). The proposed model was evaluated using two independent datasets: Pentacam (Oculus Inc., Germany) corneal imaging data collected from 5430 eyes from Department of Ophthalmology and Visual Sciences, Paulista Medical School, Federal University of Sao Paulo, Sao Paulo, Brazil; and CASIA (Tomey, Japan) corneal imaging data of 1531 eyes from Department of Ophthalmology, Jichi Medical University, Tochigi in Japan. Three different clustering scenarios were evaluated: 2 classes (normal vs KCN), 3 classes (normal vs KCN low grade (1-2 stage) vs KCN high grade (3-4 stage) and 5 classes (normal, stage KCN 1, KCN 2, KCN 3 and KCN 4). We compared the proposed method with three other standard unsupervised algorithms including K-means alone, K-medoids, and Spectral cluster (figure 1), following 25 runs. Accuracy (Ac) metrics analyses also included Precision (Pr), Recall (R), F-Score (F), and Purity (Pu).

Results : FPA-K-means outperformed the other algorithms in 2-, 3- and 5-class scenarios, except for Recall and Purity for the 5-class scenario. In the 2-class test, FPA-K-means achieved Ac=96.03, Pr=96.29, R=96.06, F=96.17, and Pu=96.03. When considering a 3-class scenario, FPA-K-means reached Ac=71.02, Pr=53.53, R=75.37, F=64.64, and Pu=80.85. While for the 5-class test, FPA-K-means attained Ac= 75.20, Pr= 35.01, R= 47.70, F= 48.97, and Pu= 53.64, whereas Spectral clustering method achieved R= 55.33 and Pu= 55.12

Conclusions : The proposed model consists of a new unsupervised algorithm that combines two computational learning techniques for detecting KCN from corneal image data. As it is unsupervised, the model is not affected by expert pre-labelling bias. Our study demonstrates that the proposed model can be used in the clinical and scientific setting for detection of KCN. Improvements to this model may be applied in clinics for diagnosing the corneal status of KCN patients.

This abstract was presented at the 2022 ARVO Annual Meeting, held in Denver, CO, May 1-4, 2022, and virtually.

 

Fig. 1. Proposed hybrid method versus single models for keratoconus detection.

Fig. 1. Proposed hybrid method versus single models for keratoconus detection.

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