Valoración del uso de retinografía como método de diagnóstico precoz de glaucoma crónico en atención primaria: validación para el cribado en población con factores de riesgo para glaucoma de ángulo abierto

González, Soraya Sánchez; Lozano, José Calvo; González, Jéssica Sánchez; González, Miguel Pedregal; Castillo, Manuel Cornejo; Fernández, Eduardo; Barral, Francisco Javier; Espinosa, José Ramón Pérez

doi:10.1016/j.aprim.2016.10.008

Cited by 4 publications

References 18 publications

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Artificial Intelligence for the Detection of Diabetic Retinopathy in Primary Care: Protocol for Algorithm Development

Vidal-Alaball¹,

Fibla²,

Zapata³

et al. 2019

JMIR Res Protoc

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Background Diabetic retinopathy (DR) is one of the most important causes of blindness worldwide, especially in developed countries. In diabetic patients, periodic examination of the back of the eye using a nonmydriatic camera has been widely demonstrated to be an effective system to control and prevent the onset of DR. Convolutional neural networks have been used to detect DR, achieving very high sensitivities and specificities. Objective The objective of this is paper was to develop an artificial intelligence (AI) algorithm for the detection of signs of DR in diabetic patients and to scientifically validate the algorithm to be used as a screening tool in primary care. Methods Under this project, 2 studies will be conducted in a concomitant way: (1) Development of an algorithm with AI to detect signs of DR in patients with diabetes and (2) A prospective study comparing the diagnostic capacity of the AI algorithm with respect to the actual system of family physicians evaluating the images. The standard reference to compare with will be a blinded double reading conducted by retina specialists. For the development of the AI algorithm, different iterations and workouts will be performed on the same set of data. Before starting each new workout, the strategy of dividing the set date into 2 groups will be used randomly. A group with 80% of the images will be used during the training (training dataset), and the remaining 20% images will be used to validate the results (validation dataset) of each cycle (epoch). During the prospective study, true-positive, true-negative, false-positive, and false-negative values will be calculated again. From here, we will obtain the resulting confusion matrix and other indicators to measure the performance of the algorithm. Results Cession of the images began at the end of 2018. The development of the AI algorithm is calculated to last about 3 to 4 months. Inclusion of patients in the cohort will start in early 2019 and is expected to last 3 to 4 months. Preliminary results are expected to be published by the end of 2019. Conclusions The study will allow the development of an algorithm based on AI that can demonstrate an equal or superior performance, and that constitutes a complement or an alternative, to the current screening of DR in diabetic patients. International Registered Report Identifier (IRRID) PRR1-10.2196/12539

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Artificial Intelligence for the Detection of Diabetic Retinopathy in Primary Care: Protocol for Algorithm Development

Vidal-Alaball¹,

Fibla²,

Zapata³

et al. 2019

JMIR Res Protoc

View full text Add to dashboard Cite

show abstract

Artificial Intelligence for the Detection of Diabetic Retinopathy in Primary Care: Protocol for Algorithm Development (Preprint)

Vidal-Alaball¹,

Fibla²,

Zapata³

et al. 2018

Preprint

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BACKGROUND Diabetic retinopathy (DR) is one of the most important causes of blindness worldwide, especially in developed countries. In diabetic patients, periodic examination of the back of the eye using a nonmydriatic camera has been widely demonstrated to be an effective system to control and prevent the onset of DR. Convolutional neural networks have been used to detect DR, achieving very high sensitivities and specificities. OBJECTIVE The objective of this is paper was to develop an artificial intelligence (AI) algorithm for the detection of signs of DR in diabetic patients and to scientifically validate the algorithm to be used as a screening tool in primary care. METHODS Under this project, 2 studies will be conducted in a concomitant way: (1) Development of an algorithm with AI to detect signs of DR in patients with diabetes and (2) A prospective study comparing the diagnostic capacity of the AI algorithm with respect to the actual system of family physicians evaluating the images. The standard reference to compare with will be a blinded double reading conducted by retina specialists. For the development of the AI algorithm, different iterations and workouts will be performed on the same set of data. Before starting each new workout, the strategy of dividing the set date into 2 groups will be used randomly. A group with 80% of the images will be used during the training (training dataset), and the remaining 20% images will be used to validate the results (validation dataset) of each cycle (epoch). During the prospective study, true-positive, true-negative, false-positive, and false-negative values will be calculated again. From here, we will obtain the resulting confusion matrix and other indicators to measure the performance of the algorithm. RESULTS Cession of the images began at the end of 2018. The development of the AI algorithm is calculated to last about 3 to 4 months. Inclusion of patients in the cohort will start in early 2019 and is expected to last 3 to 4 months. Preliminary results are expected to be published by the end of 2019. CONCLUSIONS The study will allow the development of an algorithm based on AI that can demonstrate an equal or superior performance, and that constitutes a complement or an alternative, to the current screening of DR in diabetic patients. INTERNATIONAL REGISTERED REPOR PRR1-10.2196/12539

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Retinografía y la tomografía de coherencia óptica para el diagnóstico del glaucoma mediante la evaluación de la sensibilidad y especificidad: revisión sistemática

Cavanela,

López-Alegría

2023

Revista Mexicana De Oftalmología

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Valoración del uso de retinografía como método de diagnóstico precoz de glaucoma crónico en atención primaria: validación para el cribado en población con factores de riesgo para glaucoma de ángulo abierto

Cited by 4 publications

References 18 publications

Artificial Intelligence for the Detection of Diabetic Retinopathy in Primary Care: Protocol for Algorithm Development

Artificial Intelligence for the Detection of Diabetic Retinopathy in Primary Care: Protocol for Algorithm Development

Artificial Intelligence for the Detection of Diabetic Retinopathy in Primary Care: Protocol for Algorithm Development (Preprint)

Retinografía y la tomografía de coherencia óptica para el diagnóstico del glaucoma mediante la evaluación de la sensibilidad y especificidad: revisión sistemática

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