Testing a Deep Learning Algorithm for Detection of Diabetic Retinopathy in a Spanish Diabetic Population and with MESSIDOR Database
Background: The aim of the present study was to test our deep learning algorithm (DLA) by reading the retinographies. Methods: We tested our DLA built on convolutional neural networks in 14,186 retinographies from our population and 1200 images extracted from MESSIDOR. The retinal images were graded...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-07-01
|
| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/11/8/1385 |
| _version_ | 1797524163720642560 |
|---|---|
| author | Marc Baget-Bernaldiz Romero-Aroca Pedro Esther Santos-Blanco Raul Navarro-Gil Aida Valls Antonio Moreno Hatem A. Rashwan Domenec Puig |
| author_facet | Marc Baget-Bernaldiz Romero-Aroca Pedro Esther Santos-Blanco Raul Navarro-Gil Aida Valls Antonio Moreno Hatem A. Rashwan Domenec Puig |
| author_sort | Marc Baget-Bernaldiz |
| collection | DOAJ |
| description | Background: The aim of the present study was to test our deep learning algorithm (DLA) by reading the retinographies. Methods: We tested our DLA built on convolutional neural networks in 14,186 retinographies from our population and 1200 images extracted from MESSIDOR. The retinal images were graded both by the DLA and independently by four retina specialists. Results of the DLA were compared according to accuracy (ACC), sensitivity (S), specificity (SP), positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC), distinguishing between identification of any type of DR (any DR) and referable DR (RDR). Results: The results of testing the DLA for identifying any DR in our population were: ACC = 99.75, S = 97.92, SP = 99.91, PPV = 98.92, NPV = 99.82, and AUC = 0.983. When detecting RDR, the results were: ACC = 99.66, S = 96.7, SP = 99.92, PPV = 99.07, NPV = 99.71, and AUC = 0.988. The results of testing the DLA for identifying any DR with MESSIDOR were: ACC = 94.79, S = 97.32, SP = 94.57, PPV = 60.93, NPV = 99.75, and AUC = 0.959. When detecting RDR, the results were: ACC = 98.78, S = 94.64, SP = 99.14, PPV = 90.54, NPV = 99.53, and AUC = 0.968. Conclusions: Our DLA performed well, both in detecting any DR and in classifying those eyes with RDR in a sample of retinographies of type 2 DM patients in our population and the MESSIDOR database. |
| first_indexed | 2024-03-10T08:53:29Z |
| format | Article |
| id | doaj.art-4e1cd713bdf34b6aaaaa576caceed332 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-10T08:53:29Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-4e1cd713bdf34b6aaaaa576caceed3322023-11-22T07:19:45ZengMDPI AGDiagnostics2075-44182021-07-01118138510.3390/diagnostics11081385Testing a Deep Learning Algorithm for Detection of Diabetic Retinopathy in a Spanish Diabetic Population and with MESSIDOR DatabaseMarc Baget-Bernaldiz0Romero-Aroca Pedro1Esther Santos-Blanco2Raul Navarro-Gil3Aida Valls4Antonio Moreno5Hatem A. Rashwan6Domenec Puig7Ophthalmology Service, Hospital Universitat Sant Joan, Institut de Investigació Sanitària Pere Virgili [IISPV], Universitat Rovira & Virgili, 43204 Reus, SpainOphthalmology Service, Hospital Universitat Sant Joan, Institut de Investigació Sanitària Pere Virgili [IISPV], Universitat Rovira & Virgili, 43204 Reus, SpainOphthalmology Service, Hospital Universitat Sant Joan, Institut de Investigació Sanitària Pere Virgili [IISPV], Universitat Rovira & Virgili, 43204 Reus, SpainOphthalmology Service, Hospital Universitat Sant Joan, Institut de Investigació Sanitària Pere Virgili [IISPV], Universitat Rovira & Virgili, 43204 Reus, SpainDepartment of Computer Engineering and Mathematics, Universitat Rovira & Virgili, 43204 Reus, SpainDepartment of Computer Engineering and Mathematics, Universitat Rovira & Virgili, 43204 Reus, SpainDepartment of Computer Engineering and Mathematics, Universitat Rovira & Virgili, 43204 Reus, SpainDepartment of Computer Engineering and Mathematics, Universitat Rovira & Virgili, 43204 Reus, SpainBackground: The aim of the present study was to test our deep learning algorithm (DLA) by reading the retinographies. Methods: We tested our DLA built on convolutional neural networks in 14,186 retinographies from our population and 1200 images extracted from MESSIDOR. The retinal images were graded both by the DLA and independently by four retina specialists. Results of the DLA were compared according to accuracy (ACC), sensitivity (S), specificity (SP), positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC), distinguishing between identification of any type of DR (any DR) and referable DR (RDR). Results: The results of testing the DLA for identifying any DR in our population were: ACC = 99.75, S = 97.92, SP = 99.91, PPV = 98.92, NPV = 99.82, and AUC = 0.983. When detecting RDR, the results were: ACC = 99.66, S = 96.7, SP = 99.92, PPV = 99.07, NPV = 99.71, and AUC = 0.988. The results of testing the DLA for identifying any DR with MESSIDOR were: ACC = 94.79, S = 97.32, SP = 94.57, PPV = 60.93, NPV = 99.75, and AUC = 0.959. When detecting RDR, the results were: ACC = 98.78, S = 94.64, SP = 99.14, PPV = 90.54, NPV = 99.53, and AUC = 0.968. Conclusions: Our DLA performed well, both in detecting any DR and in classifying those eyes with RDR in a sample of retinographies of type 2 DM patients in our population and the MESSIDOR database.https://www.mdpi.com/2075-4418/11/8/1385diabetic retinopathydeep learning algorithmconvolutional neural networksdiabetic retinopathy screening |
| spellingShingle | Marc Baget-Bernaldiz Romero-Aroca Pedro Esther Santos-Blanco Raul Navarro-Gil Aida Valls Antonio Moreno Hatem A. Rashwan Domenec Puig Testing a Deep Learning Algorithm for Detection of Diabetic Retinopathy in a Spanish Diabetic Population and with MESSIDOR Database Diagnostics diabetic retinopathy deep learning algorithm convolutional neural networks diabetic retinopathy screening |
| title | Testing a Deep Learning Algorithm for Detection of Diabetic Retinopathy in a Spanish Diabetic Population and with MESSIDOR Database |
| title_full | Testing a Deep Learning Algorithm for Detection of Diabetic Retinopathy in a Spanish Diabetic Population and with MESSIDOR Database |
| title_fullStr | Testing a Deep Learning Algorithm for Detection of Diabetic Retinopathy in a Spanish Diabetic Population and with MESSIDOR Database |
| title_full_unstemmed | Testing a Deep Learning Algorithm for Detection of Diabetic Retinopathy in a Spanish Diabetic Population and with MESSIDOR Database |
| title_short | Testing a Deep Learning Algorithm for Detection of Diabetic Retinopathy in a Spanish Diabetic Population and with MESSIDOR Database |
| title_sort | testing a deep learning algorithm for detection of diabetic retinopathy in a spanish diabetic population and with messidor database |
| topic | diabetic retinopathy deep learning algorithm convolutional neural networks diabetic retinopathy screening |
| url | https://www.mdpi.com/2075-4418/11/8/1385 |
| work_keys_str_mv | AT marcbagetbernaldiz testingadeeplearningalgorithmfordetectionofdiabeticretinopathyinaspanishdiabeticpopulationandwithmessidordatabase AT romeroarocapedro testingadeeplearningalgorithmfordetectionofdiabeticretinopathyinaspanishdiabeticpopulationandwithmessidordatabase AT esthersantosblanco testingadeeplearningalgorithmfordetectionofdiabeticretinopathyinaspanishdiabeticpopulationandwithmessidordatabase AT raulnavarrogil testingadeeplearningalgorithmfordetectionofdiabeticretinopathyinaspanishdiabeticpopulationandwithmessidordatabase AT aidavalls testingadeeplearningalgorithmfordetectionofdiabeticretinopathyinaspanishdiabeticpopulationandwithmessidordatabase AT antoniomoreno testingadeeplearningalgorithmfordetectionofdiabeticretinopathyinaspanishdiabeticpopulationandwithmessidordatabase AT hatemarashwan testingadeeplearningalgorithmfordetectionofdiabeticretinopathyinaspanishdiabeticpopulationandwithmessidordatabase AT domenecpuig testingadeeplearningalgorithmfordetectionofdiabeticretinopathyinaspanishdiabeticpopulationandwithmessidordatabase |