Performance Evaluation of Artificial Intelligence Assisted Diabetic Retinopathy Grading in the Leuven University Hospital: Can Technology Improve the Resident? (PEARL)

February 18, 2022 updated by: JulieJacob, Universitaire Ziekenhuizen KU Leuven
To evaluate the added value of the use of artificial intelligence in the diagnosis of referable diabetic retinopathy in a teaching hospital setting

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Detailed Description

Diabetes mellitus is one of the major health challenges of our era. It is estimated that 642 million people will be diagnosed with this disease worldwide by 2040. Diabetes is a disease effecting the entire body and comes with many possible complications due to its' effect on the microvasculature. The most prevalent of these complications is diabetic retinopathy which is caused by both microvascular and neural damage.

According to studies in the United States by the Eye Diseases Prevalence Research group, about 40% of patients present with some degree of retinopathy. 8% of patients even have vision-threatening diabetic retinopathy.

Diabetic retinopathy is one of the main causes of blindness in our current society. However annual screening and timely referral for treatment can prevent this from occurring. The best illustration is the fact that since the implementation of a nationwide screening program, diabetes mellitus is no longer the leading cause of blindness in the UK.

Therefore, many countries have organized some sort of screening program. However, there are big organizational differences between countries. This can range from an annual dilated fundoscopy by an ophthalmologist (as is the case in Belgium) to non-mydriatic fundus photographs evaluated by a trained grader who is not a (para)medic.

Even with the most efficient screening pathway possible, the increase of patient numbers will become a problem since the human factor in the screening pathway (doctor, optometrist, trained grader,…) cannot increase its' capacity with the same speed. The current system will reach its limits at one point or another. Furthermore, it is known that a significant proportion of diabetes patients do not comply with the recommended annual screening. These problems will result in longer waiting lists, underdiagnosis because of overworked doctors, long waiting lists and possibly lack of high quality care.

Simply replacing the ophthalmologist by a trained grader probably won't solve all these problems. It will merely postpone them and will still remain costly and labor-intensive. The situation in countries which already use trained graders confirms these suspicions. Furthermore there is also room for improvement in the quality of care and the accuracy of diagnosis in these set ups.

In recent years, artificial intelligence, more specifically deep learning, has been postulated as a means to solve these problems. Even in the first studies, deep learning algorithms have already been shown to reach high sensitivity and specificity in detecting referable diabetic retinopathy. Further development of these algorithms and more thorough research have confirmed these findings. The use of AI has been studied in many medical fields, however diabetic retinopathy screening remains the pioneer, as is confirmed by the first-ever FDA authorization for an AI medical application being the diabetic retinopathy screening system IDx.

Current research mostly focusses of the performance of an artificial intelligence algorithm as an autonomous diagnostic tool without interaction with a human besides the acquisition of the images. Fear exists among medical professionals that artificial intelligence will start replacing them partially in the near future and make them obsolete on the long term. However, these novel technologies could also be used to aid the health professional in making the diagnosis in a more accurate way rather than replacing them.

Therefore, in the PEARL project, we wish to evaluate the use of an artificial intelligence algorithm as a diagnostic aid to improve the diagnostic accuracy of the physician rather than replacing the physician, certainly in a training context.

Study Type

Observational

Enrollment (Anticipated)

139

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

The study group consists of patients with diabetes mellitus who receive their annual ophthalmological screening at the UZ Leuven

Description

Inclusion Criteria:

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

Exclusion Criteria:

  • - 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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Observational Models: Cohort
  • Time Perspectives: Prospective

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
does AI augment diagnostic performance of resident
Time Frame: 4 months
sensitivity and specificity
4 months

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
does physical consultation augment diagnostic performance of AI
Time Frame: 6 months
sensitivity and specificity
6 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Universitaire Ziekenhuizen KU Leuven

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Anticipated)

March 1, 2022

Primary Completion (Anticipated)

August 1, 2022

Study Completion (Anticipated)

November 1, 2022

Study Registration Dates

First Submitted

January 21, 2022

First Submitted That Met QC Criteria

February 18, 2022

First Posted (Actual)

March 2, 2022

Study Record Updates

Last Update Posted (Actual)

March 2, 2022

Last Update Submitted That Met QC Criteria

February 18, 2022

Last Verified

February 1, 2022

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • S65943

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

No

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

Yes

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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