Diabetic retinopathy screening in urban primary care setting with a handheld smartphone-based retinal camera

Abstract

Aims To evaluate diabetic retinopathy (DR) screening with a portable handheld smartphone-based retinal camera and telemedicine in an urban primary healthcare setting and to evaluate the learning curve for image acquisition, performed by healthcare personnel without previous experience in retinal imaging. Methods This was a prospective study that enrolled patients with type 2 diabetes mellitus (T2DM) followed at a primary healthcare unit in São Paulo, Brazil. After a brief training in image acquisition, there was further continuous feedback given by a retina specialist during the remote image reading process. Each patient underwent two fundus and one anterior ocular segment images per eye, after mydriasis. Patients were classified according to the need of referral. Results A total of 627 adult individuals with T2DM underwent retinal evaluation. The population was composed by 63.2% female individuals, age median of 66 years, diabetes duration 10.7 ± 8.2 years and HbA1c 7.7 ± 1.9% (61 + 20.8 mmol/mol). The most prevalent associated comorbidities were arterial hypertension (80.3%) and dyslipidemia (50.2%). Referral decision was possible in 81.2% patients. Most patients had absent or non-referable DR; the main ocular media opacity detected was cataract. After the 7th day of image acquisition, the daily rate of patients whose images allowed clinical decision was maintained above 80%. A higher HbA1c was associated with referable DR. Conclusions A low-cost DR screening strategy with a handheld device and telemedicine is feasible and has the potential to increase coverage of DR screening in underserved areas; the possibility of mobile units is relevant for DR screening in the context of COVID-19 pandemic. This article belongs to the topical collection Eye Complications of Diabetes, managed by Giuseppe Querques.

A Classification Tree Model with Optical Coherence Tomography Angiography Variables to Screen Early-Stage Diabetic Retinopathy in Diabetic Patients

“…Deng [ 32 ] integrated a quantitative device called a handheld electroretinogram with the classification tree model and found good accuracy. Moreover, handheld smartphone-based retinal cameras were applied in urban primary healthcare settings and the accuracy maintained above 80% [ 33 , 34 ]. Studies on DR screening using advanced quantitative devices are dramatically increasing, but most of them are awaiting further improvements.…”

Learning curve evaluation upskilling retinal imaging using smartphones

“…However, the results by Adam et al were based on only two medical students and one ophthalmology resident. Queiroz et al documented the rate of patients whose smartphone-based fundus images allowed clinical decision on daily basis over a 16 days period after an initial 4-h-training, however did not report any additional parameters, nor did perform any statistical analysis 41 . Still, their study supports that SBFI can be feasible for a low-cost diabetic retinopathy screening.…”

“…Evidence from several studies suggests non-expert examiners can learn and employ SBFI with sufficient results 22 – 29 . Queiroz et al evaluated the learning curve of nurses carrying out SBFI and concluded that 80% of the acquired images were usable for clinical decisions 41 . However, other studies reported inability to detect diabetic retinopathy employing SBFI by non-expert examiners 30 .…”

Smartphone Ophthalmoscopy: is there a place for it?

“…This specially aids in decentralising multi-speciality health care in developing countries. [21][22][23][24][25][26][27][28][29][30][31]…”