ABSTRACT

Background

Screening for diabetic eye disease (DED) and general diabetes care is often separate, which leads to delays and low adherence to DED screening recommendations. Thus, we assessed the feasibility, achieved image quality, and possible barriers of telemedical DED screening in a point-of-care general practice setting and the accuracy of an automated algorithm for detection of DED.

Methods

Patients with diabetes were recruited at general practices. Retinal images were acquired using a non-mydriatic camera (CenterVue, Italy) by medical assistants. Images were quality assessed and double graded by two graders. All images were also graded automatically using a commercially available artificial intelligence (AI) algorithm (EyeArt version 2.1.0, Eyenuk Inc.).

Results

A total of 75 patients (147 eyes; mean age 69 years, 96% type 2 diabetes) were included. Most of the patients (51; 68%) preferred DED screening at the general practice, but only twenty-four (32%) were willing to pay for this service. Images of 63 patients (84%) were determined to be evaluable, and DED was diagnosed in 6 patients (8.0%). The algorithm’s positive/negative predictive values (95% confidence interval) were 0.80 (0.28–0.99)/1.00 (0.92–1.00) and 0.75 (0.19–0.99)/0.98 (0.88–1.00) for detection of any DED and referral-warranted DED, respectively.

Overall, the number of referrals was 18 (24%) for manual telemedical assessment and 31 (41%) for the artificial intelligence (AI) algorithm, resulting in a relative increase of referrals by 72% when using AI.

Conclusions

Our study shows that achieved overall image quality in a telemedical GP-based DED screening was sufficient and that it would be accepted by medical assistants and patients in most cases. However, good image quality and integration into existing workflow remain challenging. Based on these findings, a larger-scale implementation study is warranted.

中文翻译：

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初级保健环境中的远程医疗糖尿病视网膜病变筛查：视网膜照片的质量和自动图像分析的准确性

摘要

背景

糖尿病眼病 (DED) 筛查和一般糖尿病护理通常是分开的，这会导致延迟和对 DED 筛查建议的依从性低。因此，我们评估了在护理点一般实践环境中远程医疗 DED 筛查的可行性、实现的图像质量和可能的障碍，以及用于检测 DED 的自动算法的准确性。

方法

在全科诊所招募糖尿病患者。视网膜图像由医疗助理使用非散瞳相机（CenterVue，意大利）获取。图像由两名评分者进行质量评估和双重评分。所有图像还使用市售的人工智能 (AI) 算法（EyeArt 版本 2.1.0，Eyenuk Inc.）自动分级。

结果

共纳入 75 名患者（147 只眼；平均年龄 69 岁，96% 为 2 型糖尿病）。大多数患者 (51; 68%) 在全科诊所首选 DED 筛查，但只有 24 位 (32%) 愿意为这项服务付费。63 名患者 (84%) 的图像被确定为可评估的，6 名患者 (8.0%) 被诊断为 DED。该算法的阳性/阴性预测值（95% 置信区间）分别为 0.80（0.28–0.99）/1.00（0.92–1.00）和 0.75（0.19–0.99）/0.98（0.88–1.00），用于检测任何 DED 和转诊保证DED，分别。

总体而言，人工远程医疗评估的转诊数量为 18 (24%)，人工智能 (AI) 算法的转诊数量为 31 (41%)，导致使用 AI 时转诊的相对增加 72%。

结论

我们的研究表明，在基于 GP 的远程医疗 DED 筛查中实现的整体图像质量就足够了，并且在大多数情况下它会被医疗助理和患者接受。然而，良好的图像质量和与现有工作流程的集成仍然具有挑战性。基于这些发现，需要进行更大规模的实施研究。