Artificial intelligence-based prediction of diabetic retinopathy evolution (EviRed): protocol for a prospective cohort
Introduction An important obstacle in the fight against diabetic retinopathy (DR) is the use of a classification system based on old imaging techniques and insufficient data to accurately predict its evolution. New imaging techniques generate new valuable data, but we lack an adapted classification...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMJ Publishing Group
2024-04-01
|
| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/14/4/e084574.full |
| _version_ | 1827144010760716288 |
|---|---|
| author | Eric Vicaut Ramin Tadayoni Pascale Massin Aude Couturier Sophie Bonnin Stéphanie Magazzeni Bruno Lay Alexandre Le Guilcher Gwenolé Quellec EviRed Investigators |
| author_facet | Eric Vicaut Ramin Tadayoni Pascale Massin Aude Couturier Sophie Bonnin Stéphanie Magazzeni Bruno Lay Alexandre Le Guilcher Gwenolé Quellec EviRed Investigators |
| author_sort | Eric Vicaut |
| collection | DOAJ |
| description | Introduction An important obstacle in the fight against diabetic retinopathy (DR) is the use of a classification system based on old imaging techniques and insufficient data to accurately predict its evolution. New imaging techniques generate new valuable data, but we lack an adapted classification based on these data. The main objective of the Evaluation Intelligente de la Rétinopathie Diabétique, Intelligent evaluation of DR (EviRed) project is to develop and validate a system assisting the ophthalmologist in decision-making during DR follow-up by improving the prediction of its evolution.Methods and analysis A cohort of up to 5000 patients with diabetes will be recruited from 18 diabetology departments and 14 ophthalmology departments, in public or private hospitals in France and followed for an average of 2 years. Each year, systemic health data as well as ophthalmological data will be collected. Both eyes will be imaged by using different imaging modalities including widefield photography, optical coherence tomography (OCT) and OCT-angiography. The EviRed cohort will be divided into two groups: one group will be randomly selected in each stratum during the inclusion period to be representative of the general diabetic population. Their data will be used for validating the algorithms (validation cohort). The data for the remaining patients (training cohort) will be used to train the algorithms.Ethics and dissemination The study protocol was approved by the French South-West and Overseas Ethics Committee 4 on 28 August 2020 (CPP2020-07-060b/2020-A01725-34/20.06.16.41433). Prior to the start of the study, each patient will provide a written informed consent documenting his or her agreement to participate in the clinical trial. Results of this research will be disseminated in peer-reviewed publications and conference presentations. The database will also be available for further study or development that could benefit patients.Trial registration number NCT04624737 |
| first_indexed | 2024-04-24T08:56:26Z |
| format | Article |
| id | doaj.art-eb577c99e6aa4a3da5dfcfa4515461b7 |
| institution | Directory Open Access Journal |
| issn | 2044-6055 |
| language | English |
| last_indexed | 2025-03-20T19:51:55Z |
| publishDate | 2024-04-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj.art-eb577c99e6aa4a3da5dfcfa4515461b72024-08-20T23:20:10ZengBMJ Publishing GroupBMJ Open2044-60552024-04-0114410.1136/bmjopen-2024-084574Artificial intelligence-based prediction of diabetic retinopathy evolution (EviRed): protocol for a prospective cohortEric Vicaut0Ramin Tadayoni1Pascale Massin2Aude Couturier3Sophie Bonnin4Stéphanie Magazzeni5Bruno Lay6Alexandre Le Guilcher7Gwenolé Quellec8EviRed Investigators9Unité de recherche clinique, AP-HP, Hôpital Lariboisière, Paris Cité University, Paris, FranceOphthalmology Departement, Adolphe de Rothschild Ophthalmological Foundation, Paris, FranceOphthalmology Department, Université Paris Cité, AP-HP, Lariboisiere Hospital, Paris, FranceOphthalmology Department, Université Paris Cité, AP-HP, Lariboisiere Hospital, Paris, FranceOphthalmology, Adolphe de Rothschild Ophthalmological Foundation, Paris, FranceCarl Zeiss Meditec Inc, Dublin, Ohio, USAADCIS, Saint-Contest, FranceEvolucare Technologies, Le Pecq, FranceINSERM, LaTIM UMR 1101, Brest, FranceRHU EviRed, APHP, Université Paris Cité, Paris, FranceIntroduction An important obstacle in the fight against diabetic retinopathy (DR) is the use of a classification system based on old imaging techniques and insufficient data to accurately predict its evolution. New imaging techniques generate new valuable data, but we lack an adapted classification based on these data. The main objective of the Evaluation Intelligente de la Rétinopathie Diabétique, Intelligent evaluation of DR (EviRed) project is to develop and validate a system assisting the ophthalmologist in decision-making during DR follow-up by improving the prediction of its evolution.Methods and analysis A cohort of up to 5000 patients with diabetes will be recruited from 18 diabetology departments and 14 ophthalmology departments, in public or private hospitals in France and followed for an average of 2 years. Each year, systemic health data as well as ophthalmological data will be collected. Both eyes will be imaged by using different imaging modalities including widefield photography, optical coherence tomography (OCT) and OCT-angiography. The EviRed cohort will be divided into two groups: one group will be randomly selected in each stratum during the inclusion period to be representative of the general diabetic population. Their data will be used for validating the algorithms (validation cohort). The data for the remaining patients (training cohort) will be used to train the algorithms.Ethics and dissemination The study protocol was approved by the French South-West and Overseas Ethics Committee 4 on 28 August 2020 (CPP2020-07-060b/2020-A01725-34/20.06.16.41433). Prior to the start of the study, each patient will provide a written informed consent documenting his or her agreement to participate in the clinical trial. Results of this research will be disseminated in peer-reviewed publications and conference presentations. The database will also be available for further study or development that could benefit patients.Trial registration number NCT04624737https://bmjopen.bmj.com/content/14/4/e084574.full |
| spellingShingle | Eric Vicaut Ramin Tadayoni Pascale Massin Aude Couturier Sophie Bonnin Stéphanie Magazzeni Bruno Lay Alexandre Le Guilcher Gwenolé Quellec EviRed Investigators Artificial intelligence-based prediction of diabetic retinopathy evolution (EviRed): protocol for a prospective cohort BMJ Open |
| title | Artificial intelligence-based prediction of diabetic retinopathy evolution (EviRed): protocol for a prospective cohort |
| title_full | Artificial intelligence-based prediction of diabetic retinopathy evolution (EviRed): protocol for a prospective cohort |
| title_fullStr | Artificial intelligence-based prediction of diabetic retinopathy evolution (EviRed): protocol for a prospective cohort |
| title_full_unstemmed | Artificial intelligence-based prediction of diabetic retinopathy evolution (EviRed): protocol for a prospective cohort |
| title_short | Artificial intelligence-based prediction of diabetic retinopathy evolution (EviRed): protocol for a prospective cohort |
| title_sort | artificial intelligence based prediction of diabetic retinopathy evolution evired protocol for a prospective cohort |
| url | https://bmjopen.bmj.com/content/14/4/e084574.full |
| work_keys_str_mv | AT ericvicaut artificialintelligencebasedpredictionofdiabeticretinopathyevolutioneviredprotocolforaprospectivecohort AT ramintadayoni artificialintelligencebasedpredictionofdiabeticretinopathyevolutioneviredprotocolforaprospectivecohort AT pascalemassin artificialintelligencebasedpredictionofdiabeticretinopathyevolutioneviredprotocolforaprospectivecohort AT audecouturier artificialintelligencebasedpredictionofdiabeticretinopathyevolutioneviredprotocolforaprospectivecohort AT sophiebonnin artificialintelligencebasedpredictionofdiabeticretinopathyevolutioneviredprotocolforaprospectivecohort AT stephaniemagazzeni artificialintelligencebasedpredictionofdiabeticretinopathyevolutioneviredprotocolforaprospectivecohort AT brunolay artificialintelligencebasedpredictionofdiabeticretinopathyevolutioneviredprotocolforaprospectivecohort AT alexandreleguilcher artificialintelligencebasedpredictionofdiabeticretinopathyevolutioneviredprotocolforaprospectivecohort AT gwenolequellec artificialintelligencebasedpredictionofdiabeticretinopathyevolutioneviredprotocolforaprospectivecohort AT eviredinvestigators artificialintelligencebasedpredictionofdiabeticretinopathyevolutioneviredprotocolforaprospectivecohort |