Diabetic Macular Edema Screened by Handheld Smartphone-based Retinal Camera and Artificial Intelligence

Abstract

Our aim was to assess the tomographic presence of diabetic macular edema in type 2 diabetes patients screened for diabetic retinopathy with color fundus photographs and an artificial intelligence algorithm. Color fundus photographs obtained with a low-cost smartphone-based handheld retinal camera were analyzed by the algorithm; patients with suspected macular lesions underwent ocular coherence tomography. A total of 366 patients were screened; diabetic macular edema was suspected in 34 and confirmed in 29 individuals, with average age 60.5 ± 10.9 years and glycated hemoglobin 9.8 ± 2.4%; use of insulin, statins, and aspirin were reported in 44.8%, 37.9%, and 34.5% of individuals, respectively; systemic blood hypertension, dyslipidemia, abdominal obesity, chronic kidney disease, and risk for diabetic foot ulcers were present in 100%, 58.6%, 62.1%, 48.3%, and 27.5% of individuals, respectively. Proliferative diabetic retinopathy was present in 31% of patients with macular edema; severity level was associated with albuminuria (p = 0.028). Eyes with macular edema had average central macular thickness 329.89 ± 80.98 m ; intraretinal cysts, sub retinal fluid, hyper-reflective foci, epiretinal membrane, and vitreomacular traction were found in 87.2%, 6.4%, 85.1%, 10.6%, and 6.4% of eyes, respectively. Diabetic retinopathy screening overwhelms health systems and is typically based on color fundus photographs, with high false-positive rates for the detection of diabetic macular edema. The present, semi-automated strategy comprising artificial intelligence algorithms integrated with smartphone-based retinal cameras could improve screening in low-resource settings with limited availability of ocular coherence tomography, allowing increased access rates and ultimately contributing to tackle preventable blindness.

The Prevalence and Risk Factors of Diabetic Retinopathy: Screening and Prophylaxis Project in 6 Provinces of China

“…Although the AI system is less sensitive than optical coherence tomography in screening for diabetic macular edema, studies have shown that the AI system has high sensitivity and specificity for DR detection including diabetic macular edema. 50 …”

“…Although the AI system is less sensitive than optical coherence tomography in screening for diabetic macular edema, studies have shown that the AI system has high sensitivity and specificity for DR detection including diabetic macular edema. 50 Some limitations of this study should be mentioned. First, this study was a cross-sectional investigation, which does not allow conclusions on risk factors, but allows conclusions on associated risk factors for DR. Second, as a hospitalbased study, the patients recruited in the study may not be representative of the overall population with diabetes.…”

Getting smart with coagulation

“…Their possible usage for monitoring anticoagulant therapy should therefore be seen as another important opportunity, which thus extends their usage for chronic disease monitoring (e.g., for measuring hemoglobin in patients with anemia, glycosylated hemoglobin in those with diabetes, and so forth). 20‐24 Nonetheless, some important aspects still need to be defined before they will be ready for prime time in VKAs monitoring (e.g., is self‐adjustment of VKA therapy reliable using the device? How does one check the proper function of the device?…”

Artificial intelligence for diabetic retinopathy screening: beyond diagnostic accuracy

“…Although the authors have defined referable DR as DR equal to or worse than moderate non proliferative DR ( 6 ), diabetic macular edema (DME) is more common than proliferative DR and corresponds to the predominant cause of moderate or severe vision loss in patients with diabetes ( 7 ). Even though it is challenging to identify DME in bi-dimensional color fundus photographs, a valid alternative could be a semi-automated strategy combined with a higher sensitivity ( 8 ), with or without telemedicine evaluation by specialists ( 9 ); a two-tiered evaluation involving the more expensive optical coherence tomography (OCT) machines could be advised only for those with suspected macular edema ( 8 ). Certainly, the scarcity of ophthalmic medical resources mentioned by the authors has to be taken into account in evaluating the trade-off between sensitivity and specificity ( 6 ); since referrals incur costs for the patient and the health-care system ( 10 ), and since cost considerations are essential for the sustainability screening programmes, a formal economic evaluation would be warranted for the ideal calibrations of such operating points ( 9 ).…”

“…Along with AI, which has been reported to allow higher referral adherence from point-of-care screening recommendations ( 10 ), other tools have been proposed to achieve higher coverage and adherence rates, even in settings where eyecare is readily available, such as telemedicine ( 12 ). Telemedicine protocols, besides having been proven cost-effective for this use case ( 13 ), are all the more remarkable in China considering its dimensions, the fact that the majority of patients at high risk live in rural areas ( 14 ), and the possibility of employing mobile units and portable retinal cameras ( 8 ). Furthermore, raising awareness about DR among patients and local health-care workers is essential for increased attendance and the overall success of screening initiatives ( 9 ).…”