July 2019
Volume 60, Issue 9
Free
ARVO Annual Meeting Abstract  |   July 2019
Artificial intelligence based detection of infectious keratitis using slit-lamp images
Author Affiliations & Notes
  • Kiran Kumar Vupparaboina
    Srujana Center for Innovation, L V Prasad Eye Institute, Hyderabad, Telangana, India
  • Sai Narsimha Vedula
    Electrical Engineering, Indian Institute of Technology Hyderabad, Hyderabad, Telangana, India
  • Snehith Aithu
    Electrical Engineering, Indian Institute of Technology Hyderabad, Hyderabad, Telangana, India
  • Sarforaz Bin Bashar
    Brien Holden Institute of Optometry and Vision Sciences, L V Prasad Eye Institute, Hyderabad, Telangana, India
  • Kiran Challa
    Tej Kohli Cornea Institute, L V Prasad Eye Institute, Hyderabad, Telangana, India
  • Abhinav Loomba
    Tej Kohli Cornea Institute, L V Prasad Eye Institute, Hyderabad, Telangana, India
  • Mukesh Taneja
    Tej Kohli Cornea Institute, L V Prasad Eye Institute, Hyderabad, Telangana, India
  • Sumohana Channapayya
    Electrical Engineering, Indian Institute of Technology Hyderabad, Hyderabad, Telangana, India
  • Ashutosh Richhariya
    Srujana Center for Innovation, L V Prasad Eye Institute, Hyderabad, Telangana, India
  • Footnotes
    Commercial Relationships   Kiran Vupparaboina, None; Sai Narsimha Vedula, None; Snehith Aithu, None; Sarforaz Bin Bashar, None; Kiran Challa, None; Abhinav Loomba, None; Mukesh Taneja, None; Sumohana Channapayya, None; Ashutosh Richhariya, None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 4236. doi:
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      Kiran Kumar Vupparaboina, Sai Narsimha Vedula, Snehith Aithu, Sarforaz Bin Bashar, Kiran Challa, Abhinav Loomba, Mukesh Taneja, Sumohana Channapayya, Ashutosh Richhariya; Artificial intelligence based detection of infectious keratitis using slit-lamp images. Invest. Ophthalmol. Vis. Sci. 2019;60(9):4236.

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

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Abstract

Purpose :
Slit-lamp examination, to detect ocular surface diseases including fungal keratitis (FK), bacterial keratitis (BK), acanthameoba keratitis (AK), and post-surgical infections, is intricate which requires high expertise. There is a dearth of skilled professionals in developing countries. Against this backdrop, we envisage to develop an artificial intelligence (AI) based system to enable early diagnosis, high throughput screening and remote eye-care. To this end, we attempted binary (healthy-diseased) classification based on slit-lamp images as a first step towards developing a comprehensive AI based disease detection tool.

Methods : Noting the prevalence, we considered only three diseases (FK, BK and AK) for initial AI model development. 928 subjects (364-healthy-both-eyes, 291-FK-single-eye, 96-BK-single-eye and 177-AK-single-eye) were recruited for the study. Slit-lamp (Haag-Streit BX 100 with Canon 40D camera) examination was performed under diffused illumination and 10X magnification. A total of 1292 images were acquired (Figure). Inspired from the ability of deep convolution neural networks (CNNs) to classify image data on par with humans, we trained and tested three CNN based architectures AlexNet, VGG-16 and VGG-19. Training and testing was performed with a split ratio of 80:20.

Results : Training accuracies of all three models (AlexNet, VGG-16 and VGG-19) were observed to be 100% while the testing accuracies were observed to be 100%, 99.1% and 99.6% respectively (Table). Further, the sensitivity (specificity) values for three models were observed to be 100% (100%) and greater than 99% (99%) during training and testing.

Conclusions : The proposed deep learning models have shown close to 100% accuracy, with AlexNet performing marginally better. Although the results look promising, it is wise to corroborate the performance with a larger dataset which is under progress.

This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.

 

Dataset used in the study, training and testing was performed with a split ratio of 80:20

Dataset used in the study, training and testing was performed with a split ratio of 80:20

 

Performance comparison of three CNN architectures under consideration

Performance comparison of three CNN architectures under consideration

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