Two Handheld Retinograph Devices Evaluated by Ophthalmologists and an Artificial Intelligence Algorithm

Telemedicine in diabetic retinopathy (RD) screening is effective but does not reach the entire diabetes population. The use of portable cameras and artificial intelligence (AI) can help in screening diabetes. We evaluated the ability of two handheld cameras, one based on a smartphone and the other o...

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Bibliographic Details
Published in:Journal of clinical medicine 2024-11, Vol.13 (22), p.6935
Main Authors: Romero-Aroca, Pedro, Fontoba-Poveda, Benilde, Garcia-Curto, Eugeni, Valls, Aida, Cristiano, Julián, Llagostera-Serra, Monica, Morente-Lorenzo, Cristian, Mendez-Marín, Isabel, Baget-Bernaldiz, Marc
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Antidiabetics
Archives & records
Artificial intelligence
Beliefs, opinions and attitudes
Cameras
Diabetes
Diabetic retinopathy
Diagnosis
Electronic health records
Equipment and supplies
Ethics
Health aspects
Medical records
Ophthalmologists
Patients
Smartphones
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Summary:Telemedicine in diabetic retinopathy (RD) screening is effective but does not reach the entire diabetes population. The use of portable cameras and artificial intelligence (AI) can help in screening diabetes. We evaluated the ability of two handheld cameras, one based on a smartphone and the other on a smartscope, to obtain images for comparison with OCT. Evaluation was carried out in two stages: the first by two retina specialists and the second using an artificial intelligence algorithm that we developed. The retina specialists reported that the smartphone images required mydriasis in all cases, compared to 73.05% of the smartscope images and 71.11% of the OCT images. Images were ungradable in 27.98% of the retinographs with the smartphone and in 7.98% with the smartscope. The detection of any DR using the AI algorithm showed that the smartphone obtained lower recall values (0.89) and F1 scores (0.89) than the smartscope, with 0.99. Low results were also obtained using the smartphone to detect mild DR (146 retinographs), compared to using the smartscope (218 retinographs). we consider that the use of handheld devices together with AI algorithms for reading retinographs can be useful for DR screening, although the ease of image acquisition through small pupils with these devices needs to be improved.
ISSN:2077-0383
2077-0383
DOI:10.3390/jcm13226935