To Evaluate the Added Value of the Use of Artificial Intelligence in the Diagnosis of Referable Diabetic Retinopathy in a Teaching Hospital SettingPEARLNCT05260281
Performance Evaluation of Artificial Intelligence Assisted Diabetic Retinopathy Grading in the Leuven University Hospital: Can Technology Improve the Resident?
An observational study of To Evaluate the Added Value of the Use of Artificial Intelligence in the Diagnosis of Referable Diabetic Retinopathy in a Teaching Hospital Setting, run by Universitaire Ziekenhuizen KU Leuven.
Trial summary
To evaluate the added value of the use of artificial intelligence in the diagnosis of referable diabetic retinopathy in a teaching hospital setting Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- To Evaluate the Added Value of the Use of Artificial Intelligence in the Diagnosis of Referable Diabetic Retinopathy in a Teaching Hospital Setting
- What is being tested?
- MONA algorithm
- Type of treatment
- Medical device
- How many people
- 139 participants
- Where
- No sites listed yet
- Study dates
- 1 Mar 2022to 1 Nov 2022
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: The study group consists of patients with diabetes mellitus who receive their annual ophthalmological screening at the UZ Leuven
Who may be able to join
- - Diagnosis of diabetes mellitus
- Age > 18 years old
- Patient is capable of giving informed consent
- Fluent in written and oral Dutch, or interpreter present
Who cannot join
- - 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
About this study
To evaluate the added value of the use of artificial intelligence in the diagnosis of referable diabetic retinopathy in a teaching hospital setting
Read the 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.
- Being tested
- MONA algorithm
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
MONA algorithmDevice
most artificial intelligence algorithms, like the MONA algorithm used in this study, are trained on 45-degree fundus photographs. In order to incorporate the use of the algorithm, the study intervention consists of taking 1 extra 45-degree fundus photograph per eye per patient using the Zeiss Cirrus Photo 800 system.
What is being measured
The primary outcome is the main question the study is designed to answer.
does AI augment diagnostic performance of resident
sensitivity and specificity
Measured: 4 months
Secondary outcomes (1)
does physical consultation augment diagnostic performance of AI
sensitivity and specificity
Measured: 6 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted2 Mar 2022
- Study start1 Mar 2022 (estimated)
- Primary completion1 Aug 2022 (estimated)
- Study completion1 Nov 2022 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (10)Papers the sponsor has linked to this study.
- Other trials for To Evaluate the Added Value of the Use of Artificial Intelligence in the Diagnosis of Referable Diabetic Retinopathy in a Teaching Hospital SettingStudies of the same condition, open and under way.
- More trials run by Universitaire Ziekenhuizen KU LeuvenThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 139 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Universitaire Ziekenhuizen KU Leuven
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, JulieJacob, Universitaire Ziekenhuizen KU Leuven
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05260281
- Sponsor's protocol ID
- S65943
- Acronym
- PEARL
- Status last verified
- Feb 2022
- First submitted
- 21 Jan 2022
- Last updated
- 2 Mar 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
- Individual data sharing
- No
Publications
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.
PubMed 28437734Kempen 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.
PubMed 15078674Liew 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.
PubMed 24525390Lee 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.
PubMed 26605370Farley 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.
PubMed 18779547Sussman EJ, Tsiaras WG, Soper KA. Diagnosis of diabetic eye disease. JAMA. 1982 Jun 18;247(23):3231-4.
PubMed 7087063Harding 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.
PubMed 7580708Lin 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.
PubMed 12140027Sayres 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.
PubMed 30553900Abramoff 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.
PubMed 23494039
Study at a glance
From the registry record, last updated by the sponsor on 2 Mar 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Performance Evaluation of Artificial Intelligence Assisted Diabetic Retinopathy Grading in the Leuven University Hospital: Can Technology Improve the Resident?
- ClinicalTrials.gov ID
- NCT05260281
- Acronym / sponsor ID
- PEARL / S65943
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 139 participants (estimated)
- Lead sponsor
- Universitaire Ziekenhuizen KU LeuvenUniversity, hospital or other
- Study start
- 1 Mar 2022 (estimated)
- Primary completion
- 1 Aug 2022 (estimated)
- Study completion
- 1 Nov 2022 (estimated)
- First posted
- 2 Mar 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
To evaluate the added value of the use of artificial intelligence in the diagnosis of referable diabetic retinopathy in a teaching hospital setting
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 design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study group consists of patients with diabetes mellitus who receive their annual ophthalmological screening at the UZ Leuven
Key inclusion criteria (4 listed)
- - Diagnosis of diabetes mellitus
- Age > 18 years old
- Patient is capable of giving informed consent
- Fluent in written and oral Dutch, or interpreter present
Key exclusion criteria (2 listed)
- - 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| MONA algorithmmost artificial intelligence algorithms, like the MONA algorithm used in this study, are trained on 45-degree fundus photographs. In order to incorporate the use of the algorithm, the study intervention consists of taking 1 extra 45-degree fundus photograph per eye per patient using the Zeiss Cirrus Photo 800 system. | Device | – |
Primary outcome
does AI augment diagnostic performance of resident
Time frame: 4 months
sensitivity and specificity
1 secondary outcome
does physical consultation augment diagnostic performance of AI
Time frame: 6 months
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 2 Mar 2022.
- Background: PubMed 28437734Ogurtsova 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.
- Background: PubMed 15078674Kempen 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.
- Background: PubMed 24525390Liew 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.
- Background: PubMed 26605370Lee 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.
- Background: PubMed 18779547Farley 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.
- Background: PubMed 7087063Sussman EJ, Tsiaras WG, Soper KA. Diagnosis of diabetic eye disease. JAMA. 1982 Jun 18;247(23):3231-4.
- Background: PubMed 7580708Harding 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.
- Background: PubMed 12140027Lin 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.
- Background: PubMed 30553900Sayres 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.
- Background: PubMed 23494039Abramoff 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.
- Individual participant dataSharing planned: No
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
An observational study of To Evaluate the Added Value of the Use of Artificial Intelligence in the Diagnosis of Referable Diabetic Retinopathy in a Teaching Hospital Setting, run by Universitaire Ziekenhuizen KU Leuven.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 2 Mar 2022. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
does AI augment diagnostic performance of resident (time frame: 4 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05260281, which also lists 10 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 2 Mar 2022.