June 2020
Volume 61, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2020
Pilot: A validation study of a novel, fully-automated, disease grading and clinical decision support system for screening and telemedicine-based diagnosis of the sight threatening condition of diabetic retinopathy (DR)
Author Affiliations & Notes
  • Muhammad AlAjmi
    AlBahar Eye Center, Ministry of Health, Kuwait, Kuwait
    Vitreoretinal division, King Khaled Eye Specialized Hospital, Riyadh, Saudi Arabia
  • Adel Al Akeely
    Vitreoretinal division, King Khaled Eye Specialized Hospital, Riyadh, Saudi Arabia
  • Hamad Al Subaie
    Vitreoretinal division, King Khaled Eye Specialized Hospital, Riyadh, Saudi Arabia
  • Abdullah Al Bahlal
    Vitreoretinal division, King Khaled Eye Specialized Hospital, Riyadh, Saudi Arabia
  • Abdulaziz AlAgeely
    King Khaled Eye Specialized Hospital, Riyadh, Saudi Arabia
  • Marco Mura
    Vitreoretinal division, King Khaled Eye Specialized Hospital, Riyadh, Saudi Arabia
    ILLINOIS EYE AND EAR INFIRMARY, University of Illinois at Chicago, Chicago, Illinois, United States
  • Hassan Al Dhibi
    Vitreoretinal division, King Khaled Eye Specialized Hospital, Riyadh, Saudi Arabia
  • Deepak Edward
    Ophthalmology and Visual Sciences, The University of Illinois, Chicago, Illinois, United States
    King Khaled Eye Specialized Hospital, Riyadh, Saudi Arabia
  • Footnotes
    Commercial Relationships   Muhammad AlAjmi, None; Adel Al Akeely, None; Hamad Al Subaie, None; Abdullah Al Bahlal, None; Abdulaziz AlAgeely, None; Marco Mura, None; Hassan Al Dhibi, None; Deepak Edward, None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2020, Vol.61, 2009. doi:
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      Muhammad AlAjmi, Adel Al Akeely, Hamad Al Subaie, Abdullah Al Bahlal, Abdulaziz AlAgeely, Marco Mura, Hassan Al Dhibi, Deepak Edward; Pilot: A validation study of a novel, fully-automated, disease grading and clinical decision support system for screening and telemedicine-based diagnosis of the sight threatening condition of diabetic retinopathy (DR). Invest. Ophthalmol. Vis. Sci. 2020;61(7):2009.

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

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Abstract

Purpose : Regular screening and early detection of diabetic retinopathy (DR) in patients with diabetes are paramount in reducing preventable blindness and impaired vision associated with DR. Due to limited ophthalmology resources at public hospitals in Saudi Arabia, screening all patients with diabetes becomes costly and inefficient, with increasing inappropriate referrals and a prolonged waiting list for ophthalmology consultations. The recent emergence of artificial intelligence (AI) – based, deep learning algorithms enhance the opportunity for early detection of DR from retinal images without the need for a specialist opinion and refer only those requiring treatment

Methods : A total of 101 patients with diabetic retinopathy at King khaled Eye Specialized Hospital (KKESH) were enrolled in this pilot study. Adult patients with diabetes were included. Pregnant patients and Eyes with media opacities were excluded. Retinal images collected from patients stored in the medical images server located at KKESH were assessed and graded for DR by two sets of ophthalmologists to create a 'Gold Standard'. Same images were run through AI-based DR detection system (TeleMedC DR grader) and results compared with the Gold standard. The degree of agreement between automated analysis and manual grading was quantified and assessed using multiple statistical analysis tools for sensitivity, specificity, and DR grading accuracy. For all statistical tests, p-value <0.05 were considered significant.

Results : In this pilot, 101 images were obtained, 8 of which were excluded for poor quality for AI based grading and removed from calculations by the AI based quality control system
Compared with the Gold Standard, Sensitivity for detecting DR was 97.7% . All 3 images with no disease have been graded correctly by AI (TeleMedC DR grader)
AI grading Accuracy was found to be 97.85% compared to the Gold standard.

Conclusions : AI-based DR detection systems are becoming a powerful tool for grading and clinical decision support system for screening and telemedicine-based diagnosis and referral for DR. The Validation process is being continued with a larger sample size, and when completed, the accuracy, and time and cost saving benefits will lead to adoption by national DR screening programs in large populations

This is a 2020 ARVO Annual Meeting abstract.

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