E-CLAIR: Efficiency and Cost-effectiveness of Artificial Intelligence Based Diabetic Retinopathy Screening in Flanders

NCT05391659 | Unknown

• 1 other identifier: S64955
• Study Type: interventional
• Target: 1,200
• Locations: 1 country
• Sites: 4

Brief Summary

To evaluate the efficiency and cost-effectiveness of an artificial intelligence based diabetic retinopathy screening program in Flanders

Conditions

Diabetic Retinopathy

Outcome Measures

Primary Outcomes (3)

• sensitivity

  To evaluate the efficiency of the use of AI in screening for DRP: sensitivity

  6 months

• specificity

  To evaluate the efficiency of the use of AI in screening for DRP: specificity

  6 months

• AUC

  To evaluate the efficiency of the use of AI in screening for DRP: AUC

  6 months

Secondary Outcomes (5)

• precision

  6 months

• decision tree model

  6 months

• recall

  6 months

• F1 score

  6 months

• false positives and false negatives

  6 months

Study Arms (3)

current workflow in Flanders

ACTIVE COMPARATOR

patient visits ophthalmologist

Diagnostic Test: gold standard

AI-only workflow

ACTIVE COMPARATOR

patient is imaged, images are interpreted by DR AI tool, only referrable cases identified by DR AI tool will visit ophthalmologist

Device: deep learning

AI-human workflow

ACTIVE COMPARATOR

patient is imaged, images are interpreted by DR AI tool, referrable cases identified by DR AI tool will be remotely graded by a human, only the high risk patients will visit ophthalmologist

Diagnostic Test: remote grading of fundus images

Interventions

deep learning DEVICE

a form of artificial intelligence (AI), has been introduced for automated analysis of images

AI-only workflow

remote grading of fundus images DIAGNOSTIC_TEST

referrable cases identified by DR AI tool will be remotely graded by a human

AI-human workflow

gold standard DIAGNOSTIC_TEST

examination by ophthalmologist

current workflow in Flanders

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Diagnosis of diabetes mellitus
• Age \> 18 years old
• Patient is capable of giving informed consent
• Fluent in written and oral Dutch, or interpreter present

You may not qualify if:

• \- History of treatment for diabetic retinopathy or diabetic macular edema (laser or intravitreal injections)
• Participant is contraindicated for imaging by fundus imaging systems used in the study

Sponsors & Collaborators

• Universitaire Ziekenhuizen KU Leuven: lead

Study Sites (4)

UZA

Antwerp, Belgium

RECRUITING

ZNA

Antwerp, Belgium

RECRUITING

AZ sint Jan

Bruges, Belgium

RECRUITING

AZ Turnhout

Turnhout, Belgium

RECRUITING

Related Publications (16)

• Ogurtsova K, da Rocha Fernandes JD, Huang Y, Linnenkamp U, Guariguata L, Cho NH, Cavan D, Shaw JE, Makaroff LE. IDF Diabetes Atlas: Global estimates for the prevalence of diabetes for 2015 and 2040. Diabetes Res Clin Pract. 2017 Jun;128:40-50. doi: 10.1016/j.diabres.2017.03.024. Epub 2017 Mar 31.

  PMID: 28437734 BACKGROUND

• Kempen JH, O'Colmain BJ, Leske MC, Haffner SM, Klein R, Moss SE, Taylor HR, Hamman RF; Eye Diseases Prevalence Research Group. The prevalence of diabetic retinopathy among adults in the United States. Arch Ophthalmol. 2004 Apr;122(4):552-63. doi: 10.1001/archopht.122.4.552.

  PMID: 15078674 BACKGROUND

• Lee R, Wong TY, Sabanayagam C. Epidemiology of diabetic retinopathy, diabetic macular edema and related vision loss. Eye Vis (Lond). 2015 Sep 30;2:17. doi: 10.1186/s40662-015-0026-2. eCollection 2015.

  PMID: 26605370 BACKGROUND

• Liew G, Michaelides M, Bunce C. A comparison of the causes of blindness certifications in England and Wales in working age adults (16-64 years), 1999-2000 with 2009-2010. BMJ Open. 2014 Feb 12;4(2):e004015. doi: 10.1136/bmjopen-2013-004015.

  PMID: 24525390 BACKGROUND

• Farley TF, Mandava N, Prall FR, Carsky C. Accuracy of primary care clinicians in screening for diabetic retinopathy using single-image retinal photography. Ann Fam Med. 2008 Sep-Oct;6(5):428-34. doi: 10.1370/afm.857.

  PMID: 18779547 BACKGROUND

• Sussman EJ, Tsiaras WG, Soper KA. Diagnosis of diabetic eye disease. JAMA. 1982 Jun 18;247(23):3231-4.

  PMID: 7087063 BACKGROUND

• Harding SP, Broadbent DM, Neoh C, White MC, Vora J. Sensitivity and specificity of photography and direct ophthalmoscopy in screening for sight threatening eye disease: the Liverpool Diabetic Eye Study. BMJ. 1995 Oct 28;311(7013):1131-5. doi: 10.1136/bmj.311.7013.1131.

  PMID: 7580708 BACKGROUND

• Lin DY, Blumenkranz MS, Brothers RJ, Grosvenor DM. The sensitivity and specificity of single-field nonmydriatic monochromatic digital fundus photography with remote image interpretation for diabetic retinopathy screening: a comparison with ophthalmoscopy and standardized mydriatic color photography. Am J Ophthalmol. 2002 Aug;134(2):204-13. doi: 10.1016/s0002-9394(02)01522-2.

  PMID: 12140027 BACKGROUND

• Sayres R, Taly A, Rahimy E, Blumer K, Coz D, Hammel N, Krause J, Narayanaswamy A, Rastegar Z, Wu D, Xu S, Barb S, Joseph A, Shumski M, Smith J, Sood AB, Corrado GS, Peng L, Webster DR. Using a Deep Learning Algorithm and Integrated Gradients Explanation to Assist Grading for Diabetic Retinopathy. Ophthalmology. 2019 Apr;126(4):552-564. doi: 10.1016/j.ophtha.2018.11.016. Epub 2018 Dec 13.

  PMID: 30553900 BACKGROUND

• Abramoff MD, Folk JC, Han DP, Walker JD, Williams DF, Russell SR, Massin P, Cochener B, Gain P, Tang L, Lamard M, Moga DC, Quellec G, Niemeijer M. Automated analysis of retinal images for detection of referable diabetic retinopathy. JAMA Ophthalmol. 2013 Mar;131(3):351-7. doi: 10.1001/jamaophthalmol.2013.1743.

  PMID: 23494039 BACKGROUND

• Kapetanakis VV, Rudnicka AR, Liew G, Owen CG, Lee A, Louw V, Bolter L, Anderson J, Egan C, Salas-Vega S, Rudisill C, Taylor P, Tufail A. A study of whether automated Diabetic Retinopathy Image Assessment could replace manual grading steps in the English National Screening Programme. J Med Screen. 2015 Sep;22(3):112-8. doi: 10.1177/0969141315571953. Epub 2015 Mar 5.

  PMID: 25742804 BACKGROUND

• Xie Y, Nguyen QD, Hamzah H, Lim G, Bellemo V, Gunasekeran DV, Yip MYT, Qi Lee X, Hsu W, Li Lee M, Tan CS, Tym Wong H, Lamoureux EL, Tan GSW, Wong TY, Finkelstein EA, Ting DSW. Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study. Lancet Digit Health. 2020 May;2(5):e240-e249. doi: 10.1016/S2589-7500(20)30060-1. Epub 2020 Apr 23.

  PMID: 33328056 BACKGROUND

• Wolff J, Pauling J, Keck A, Baumbach J. The Economic Impact of Artificial Intelligence in Health Care: Systematic Review. J Med Internet Res. 2020 Feb 20;22(2):e16866. doi: 10.2196/16866.

  PMID: 32130134 BACKGROUND

• Tufail A, Rudisill C, Egan C, Kapetanakis VV, Salas-Vega S, Owen CG, Lee A, Louw V, Anderson J, Liew G, Bolter L, Srinivas S, Nittala M, Sadda S, Taylor P, Rudnicka AR. Automated Diabetic Retinopathy Image Assessment Software: Diagnostic Accuracy and Cost-Effectiveness Compared with Human Graders. Ophthalmology. 2017 Mar;124(3):343-351. doi: 10.1016/j.ophtha.2016.11.014. Epub 2016 Dec 23.

  PMID: 28024825 BACKGROUND

• Tufail A, Kapetanakis VV, Salas-Vega S, Egan C, Rudisill C, Owen CG, Lee A, Louw V, Anderson J, Liew G, Bolter L, Bailey C, Sadda S, Taylor P, Rudnicka AR. An observational study to assess if automated diabetic retinopathy image assessment software can replace one or more steps of manual imaging grading and to determine their cost-effectiveness. Health Technol Assess. 2016 Dec;20(92):1-72. doi: 10.3310/hta20920.

  PMID: 27981917 BACKGROUND

• Heydon P, Egan C, Bolter L, Chambers R, Anderson J, Aldington S, Stratton IM, Scanlon PH, Webster L, Mann S, du Chemin A, Owen CG, Tufail A, Rudnicka AR. Prospective evaluation of an artificial intelligence-enabled algorithm for automated diabetic retinopathy screening of 30 000 patients. Br J Ophthalmol. 2021 May;105(5):723-728. doi: 10.1136/bjophthalmol-2020-316594. Epub 2020 Jun 30.

  PMID: 32606081 BACKGROUND

MeSH Terms

Conditions

Diabetic Retinopathy

Condition Hierarchy (Ancestors)

Retinal Diseases; Eye Diseases; Diabetic Angiopathies; Vascular Diseases; Cardiovascular Diseases; Diabetes Complications; Diabetes Mellitus; Endocrine System Diseases

Study Officials

• Julie Jacob, MD PhD

  Universitaire Ziekenhuizen KU Leuven

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Liesje Prové

CONTACT

003216342874; lies.prove@uzleuven.be

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Ophthalmologist

Study Record Dates

• First Submitted: October 1, 2021
• First Posted: May 26, 2022
• Study Start: June 17, 2021
• Primary Completion: November 1, 2022
• Study Completion: December 1, 2022
• Last Updated: May 26, 2022

Record last verified: 2022-05

Data Sharing

• IPD Sharing: Will not share

Locations

BE (4)