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Table 1.  

No. of patients/eyes 54/107
Gender, number (%)
Male 33 (61.1)
Female 21 (38.9)
Diabetes type, number of patients (%)
1 20 (37.0)
2 32 (59.3)
No records 2 (3.7)
Hb1A1C level (%), mean ± SD 7.6 ± 1.7
BCVA, mean ± SD 0.99 ± 0.25
Arterial hypertension, no. (%) 21 (38.2)
No records 1 (1.8)
Hyperlipidemia, no. (%) 19 (35.2)
No records 1 (1.9)
History of stroke, no. (%) 2 (3.6)
No records 4 (7.3)
History of myocardial infarction, no. (%) 2 (3.6)
No records 4 (7.3)
Glaucoma, no. (%) 3 (5.5)
No records 1 (1.8)
Insulin therapy (%) 31 (56.4)
No records 1 (1.8)

Table 1. Patient characteristics.

Table 2.  

  Autonomous AI output, no. patients (%)
No DR/mild DR Moderate/vision-hreatening (severe DR)
7F-mask area grading on UWF images, no. patients (%)
No DR/mild DR 16 (29.6%) 18 (33.3%)
Moderate DR/severe DR 0 20 (37.0%)

Table 2. Comparison of autonomous AI outputs with 7F-mask area grading on UWF images with DR stage of the patient defined by the eye with the worse DR stage.

Table 3.  

  Autonomous AI output, no. patients (%)
No DR/mild DR Moderate/vision-hreatening (severe DR)
UWF full-field grading, no. patients (%)
No DR/mild DR 15 (27.8%) 17 (31.5%)
Moderate DR/severe DR 1 (1.9%) 21 (38.9%)

Table 3. Comparison of autonomous AI output with UWF full-field grading with DR stage of the patient defined by the eye with the worse DR stage.

Table 4.  

(A) Patients, no. 7F-mask area grading on
UWF images
Autonomous
AI output
Eye A Eye B
4 Moderate DR Mild DR Moderate DR
3 No DR Moderate DR Moderate DR
1 Moderate DR Severe DR Vision-threatening DR
1 No DR Mild DR negative
5 No DR Mild DR Moderate DR
3 Moderate DR Mild DR Vision-threatening DR
B) Patients, no. UWF images grading IDx-DR output
Eye A Eye B
4 Moderate DR Mild DR Moderate DR
4 No DR Moderate DR Moderate DR
1 Moderate DR Severe DR Vision-hreatening DR
1 No DR Mild DR negative
3 No DR Mild DR Moderate DR
1 Moderate DR Mild DR Vision-threatening DR

Table 4. A. Patients with different DR stages between eyes diagnosed with 7F-mask area grading on UWF images. B. Patients with different DR stages between eyes were diagnosed with UWF full-field grading.

CME / ABIM MOC

Comparison of Early Diabetic Retinopathy Staging in Asymptomatic Patients Between Autonomous AI-Based Screening and Human-Graded Ultra-Widefield Colour Fundus Images

  • Authors: Aleksandra Sedova, MD; Dorottya Hajdu, MD; Felix Datlinger, MD; Irene Steiner, MSc; Martina Neschi, PhD; Julia Aschauer, MD; Bianca S Gerendas, MD, MSc, PhD; Ursula Schmidt-Erfurth, MD; Andreas Pollreisz, MD
  • CME / ABIM MOC Released: 2/7/2022
  • Valid for credit through: 2/7/2023
Start Activity

  • Credits Available

    Physicians - maximum of 1.00 AMA PRA Category 1 Credit(s)™

    ABIM Diplomates - maximum of 1.00 ABIM MOC points

    You Are Eligible For

    • Letter of Completion
    • ABIM MOC points

Target Audience and Goal Statement

This activity is intended for ophthalmologists, endocrinologists/diabetologists, internists, and other clinicians caring for patients with diabetes who may be at risk for diabetic retinopathy (DR).

The goal of this activity is to compare DR severity scores of ophthalmologically asymptomatic people with diabetes between outputs from an autonomous artificial intelligence (AI)-based system (IDx-DR, Digital Diagnostics) and human-graded ultra-widefield (UWF) color fundus imaging, including the overlay of an Early Treatment Diabetic Retinopathy Study (ETDRS) 7-field area.

Upon completion of this activity, participants will:

  • Compare diabetic retinopathy (DR) severity scores of ophthalmologically asymptomatic people with diabetes between outputs from an artificial intelligence (AI)-based system and human-graded ultra-widefield (UWF) color fundus imaging, according to a clinical study
  • Compare manual 7F-mask gradings vs UWF full-field gradings and describe correlation with patient characteristics, according to a clinical study
  • Describe clinical implications of the comparison between DR severity scores of ophthalmologically asymptomatic people with diabetes outputs using outputs from an AI-based system and human-graded UWF color fundus imaging, according to a clinical study


Disclosures

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All relevant financial relationships for anyone with the ability to control the content of this educational activity are listed below and have been mitigated according to Medscape policies. Others involved in the planning of this activity have no relevant financial relationships.


Faculty

  • Aleksandra Sedova, MD

    Department of Ophthalmology and Optometry
    Medical University Vienna
    Vienna, Austria

  • Dorottya Hajdu, MD

    Department of Ophthalmology and Optometry
    Medical University Vienna
    Vienna, Austria

  • Felix Datlinger, MD

    Department of Ophthalmology and Optometry
    Medical University Vienna
    Vienna, Austria

  • Irene Steiner, MSc

    Center for Medical Statistics
    Informatics and Intelligent Systems
    Section for Medical Statistics
    Medical University Vienna
    Vienna, Austria

  • Martina Neschi, PhD

    Department of Ophthalmology and Optometry
    Medical University Vienna
    Vienna, Austria

  • Julia Aschauer, MD

    Department of Ophthalmology and Optometry
    Medical University Vienna
    Vienna, Austria

  • Bianca S Gerendas, MD, MSc, PhD

    Department of Ophthalmology and Optometry
    Medical University Vienna
    Vienna, Austria

  • Ursula Schmidt-Erfurth, MD

    Department of Ophthalmology and Optometry
    Medical University Vienna
    Vienna, Austria

  • Andreas Pollreisz, MD

    Department of Ophthalmology and Optometry
    Medical University Vienna
    Vienna, Austria

CME Author

  • Laurie Barclay, MD

    Freelance writer and reviewer
    Medscape, LLC

    Disclosures

    Disclosure: Laurie Barclay, MD, has disclosed no relevant financial relationships.

Editor

  • Sobha Sivaprasad, MD

    Editor, Eye

    Disclosures

    Disclosure: Sobha Sivaprasad, MD, has disclosed the following relevant financial relationships:
    Consultant or advisor for the following ineligible company(ies): Allergan, Inc.; Bayer HealthCare Pharmaceuticals; Boehringer Ingelheim Pharmaceuticals, Inc.; Heidelberg Pharma GmbH; Novartis Pharmaceuticals Corporation; Optos; Roche
    Speaker or a member of a speakers bureau for the following ineligible company(ies): Allergan, Inc.; Bayer HealthCare Pharmaceuticals; Boehringer Ingelheim Pharmaceuticals, Inc.; Novartis Pharmaceuticals Corporation; Optos; Roche
    Receive research funding from the following ineligible company(ies): Bayer HealthCare Pharmaceuticals; Boehringer Ingelheim Pharmaceuticals, Inc.; Novartis Pharmaceuticals Corporation; Optos
    Employed by or have executive role with the following ineligible company(ies): Data Monitoring Chair for Phase 2 study sponsored by Bayer HealthCare
    Pharmaceuticals; Scientific Committee Member of EyeBio Steering Committee for FOCUS sponsored by Novo Nordisk
    Other: Trustee member for Macular Society Scientific/ Research Advisory Committee Member for Sight UK, Retina UK, Macular Society

CME Reviewer

  • Stephanie Corder, ND, RN, CHCP

    Associate Director, Accreditation and Compliance
    Medscape, LLC

    Disclosures

    Disclosure: Stephanie Corder, ND, RN, CHCP, has disclosed no relevant financial relationships.


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From Eye
CME / ABIM MOC

Comparison of Early Diabetic Retinopathy Staging in Asymptomatic Patients Between Autonomous AI-Based Screening and Human-Graded Ultra-Widefield Colour Fundus Images

Authors: Aleksandra Sedova, MD; Dorottya Hajdu, MD; Felix Datlinger, MD; Irene Steiner, MSc; Martina Neschi, PhD; Julia Aschauer, MD; Bianca S Gerendas, MD, MSc, PhD; Ursula Schmidt-Erfurth, MD; Andreas Pollreisz, MDFaculty and Disclosures

CME / ABIM MOC Released: 2/7/2022

Valid for credit through: 2/7/2023

processing....

Introduction

Diabetic retinopathy (DR) is a vision-threatening disease affecting approximately one-third of individuals diagnosed with diabetes mellitus [1]. It has been predicted that by the year 2030 there will be 439 million adults affected worldwide, rising to an estimated 629 million by 2045 [2, 3]. The number of patients with vision-threatening DR is expected to increase dramatically over the next years [4]. Scientific and clinical evidence proved that early diagnosis and well-timed treatment are crucial in preventing visual loss in these patients [5].

Over the last decades, advances in machine learning and deep learning have made it possible to automatically identify various ophthalmological diseases from colour fundus images such as DR, age-related macular degeneration, or glaucoma [6–9].

Multiple automated algorithms for DR detection from retinal colour photographs have been developed [7, 10–12]. IDx-DR was the first autonomous artificial intelligence (AI)-based diagnostic system approved by the U.S. Food and Drug Administration (FDA). It consists of a robotic fundus camera and two types of algorithms, namely for image quality assessment as well as immediate diagnosis of the DR stage in case of sufficient image quality from four colour fundus images. IDx-DR provides one output per patient including both eyes. In a preregistered trial, IDx-DR was validated against the ETDRS protocol prognostic standard, and showed 87.2% sensitivity and 90.7% specificity for identifying ETDRS 35 and above, or any form of macular oedema, which includes moderate and vision-threatening DR that require consultation of an ophthalmologist [13, 14].

To date there are several different classification systems for DR. The Airlie House Classification, which was modified for the Early Treatment Diabetic Retinopathy Study (ETDRS), remains the gold standard for diagnosis of DR in a research setting as it correlates with the risk of DR progression [15, 16]. Stereoscopic images with a field of 30° of the standard 7-fields are evaluated and graded in 13 severity levels, ranging from 10 (no diabetic retinopathy) to 85 (e.g. severe retinopathy with retinal detachment at macula) [16]. In order to simplify DR classification for clinical use, the International Clinical Disease (ICDR) Severity Scale was introduced according to the findings of ETDRS and the Wisconsin Epidemiologic Study of Diabetic Retinopathy (WESDR). Five stages of DR were described as following—‘no apparent retinopathy,’ ’mild non-proliferative retinopathy (NPDR),’ ‘moderate NPDR,’ ‘severe NPDR,’ ‘proliferative diabetic retinopathy (PDR).’ Additionally, clinically significant and centre-involved diabetic macular oedema (DMO) can occur in any stage of DR [17].

With modern imaging modalities such as widefield (WF) imaging and ultra-widefield (UWF) imaging of the retina, it is now possible to obtain valuable information from peripheral retinal areas that could otherwise be missed with conventional imaging [18]. It has been demonstrated that diabetic retinal lesions are present in areas outside the standardised 7 ETDRS fields in about 40% of diabetic eyes, resulting in more severe DR levels in 10% of eyes [19, 20]. However, the prognostic impact of these peripheral lesions, if any, is subject to study.

WF images are defined to depict the retina in all 4 quadrants up to and including the region of the vortex vein ampullae, while UWF images extend the field of view beyond their anterior edge [21]. Current laser-based retinal imaging systems allow the capture of WF or UWF images either by image montages or a single-shot, visualising a field of view of up to 200°, which corresponds to about 82% of the total retinal area [21, 22]. A new DR staging system is under development, and UWF and other new modalities are being considered for being part of it [23].

In this study, we aimed to compare DR severity scores of ophthalmologically asymptomatic people with diabetes between outputs from an autonomous AI-based system (IDx-DR, Digital Diagnostics) and human-graded UWF colour images including the overlay of an ETDRS 7-field area.