Simultaneous screening and classification of diabetic retinopathy and age-related macular degeneration based on fundus photos-a prospective analysis of the RetCAD system

TY - JOUR

T1 - Simultaneous screening and classification of diabetic retinopathy and age-related macular degeneration based on fundus photos-a prospective analysis of the RetCAD system

AU - Skevas, Christos

AU - Weindler, Hanah

AU - Levering, Max

AU - Engelberts, Jonne

AU - van Grinsven, Mark

AU - Katz, Toam

N1 - International Journal of Ophthalmology Press.

PY - 2022

Y1 - 2022

N2 - AIM: To assess the accuracy of an artificial intelligence (AI) based software (RetCAD, Thirona, The Netherlands) to identify and grade age-related macular degeneration (AMD) and diabetic retinopathy (DR) simultaneously based on fundus photos.METHODS: This prospective study included 1245 eyes of 630 patients attending an ophthalmology day-care clinic. Fundus photos were acquired and parallel graded by the RetCAD AI software and by an expert reference examiner for image quality, and staging of AMD and DR. Adjudication was provided by a second expert examiner in case of disagreement between the AI software and the reference examiner. Statistical analysis was performed on eye-level and on patient-level, by summarizing the individual image level-gradings into and eye-level or patient-level score, respectively. The performance of the RetCAD system was measured using receiver operating characteristics (ROC) analysis and sensitivity and specificity for both AMD and DR were reported.RESULTS: The RetCAD achieved an area under the ROC (Az) of 0.926 with a sensitivity of 84.6% at a specificity of 84.0% for image quality. On image level, the RetCAD software achieved Az values of 0.964 and 0.961 with sensitivity/specificity pairs of 98.2%/79.1% and 83.9%/93.3% for AMD and DR, respectively. On patient level, the RetCAD software achieved Az values of 0.960 and 0.948 with sensitivity/specificity pairs of 97.3%/73.3% and 80.0%/90.1% for AMD and DR, respectively. After adjudication by the second expert examiner sensitivity/specificity increases on patient-level to 98.6%/78.3% and 100.0%/92.3% for AMD and DR, respectively.CONCLUSION: The RetCAD offers very good sensitivity and specificity compared to manual grading by experts and is in line with that obtained by similar automated grading systems. The RetCAD AI software enables simultaneous grading of both AMD and DR based on the same fundus photos. Its sensitivity may be adjusted according to the desired acceptable sensitivity and specificity. Its simplicity cloud base integration allows cost-effective screening where routine expert evaluation may be limited.

AB - AIM: To assess the accuracy of an artificial intelligence (AI) based software (RetCAD, Thirona, The Netherlands) to identify and grade age-related macular degeneration (AMD) and diabetic retinopathy (DR) simultaneously based on fundus photos.METHODS: This prospective study included 1245 eyes of 630 patients attending an ophthalmology day-care clinic. Fundus photos were acquired and parallel graded by the RetCAD AI software and by an expert reference examiner for image quality, and staging of AMD and DR. Adjudication was provided by a second expert examiner in case of disagreement between the AI software and the reference examiner. Statistical analysis was performed on eye-level and on patient-level, by summarizing the individual image level-gradings into and eye-level or patient-level score, respectively. The performance of the RetCAD system was measured using receiver operating characteristics (ROC) analysis and sensitivity and specificity for both AMD and DR were reported.RESULTS: The RetCAD achieved an area under the ROC (Az) of 0.926 with a sensitivity of 84.6% at a specificity of 84.0% for image quality. On image level, the RetCAD software achieved Az values of 0.964 and 0.961 with sensitivity/specificity pairs of 98.2%/79.1% and 83.9%/93.3% for AMD and DR, respectively. On patient level, the RetCAD software achieved Az values of 0.960 and 0.948 with sensitivity/specificity pairs of 97.3%/73.3% and 80.0%/90.1% for AMD and DR, respectively. After adjudication by the second expert examiner sensitivity/specificity increases on patient-level to 98.6%/78.3% and 100.0%/92.3% for AMD and DR, respectively.CONCLUSION: The RetCAD offers very good sensitivity and specificity compared to manual grading by experts and is in line with that obtained by similar automated grading systems. The RetCAD AI software enables simultaneous grading of both AMD and DR based on the same fundus photos. Its sensitivity may be adjusted according to the desired acceptable sensitivity and specificity. Its simplicity cloud base integration allows cost-effective screening where routine expert evaluation may be limited.

U2 - 10.18240/ijo.2022.12.14

DO - 10.18240/ijo.2022.12.14

M3 - SCORING: Journal article

C2 - 36536981

VL - 15

SP - 1985

EP - 1993

JO - INT J OPHTHALMOL-CHI

JF - INT J OPHTHALMOL-CHI

SN - 2222-3959

IS - 12

ER -