Murphy Timothy I, Armitage James A, Abel Larry A, van Wijngaarden Peter, Douglass Amanda G
School of Medicine (Optometry), Faculty of Health, Deakin University, Geelong, VIC 3216, Australia.
Faculty of Health, School of Health Sciences, University of Canberra, Canberra, ACT 2601, Australia.
J Clin Med. 2025 Apr 28;14(9):3046. doi: 10.3390/jcm14093046.
This study investigated the visual search behaviour of optometrists and fellowship-trained ophthalmologists when screening for diabetic retinopathy in retinal photographs. Participants assessed and graded retinal photographs on a computer screen while a Gazepoint GP3 HD eye tracker recorded their eye movements. Areas of interest were derived from the raw data using Hidden Markov modelling. Fixation strings were extracted by matching raw fixation data to areas of interest and resolving ambiguities with graph search algorithms. Fixation strings were clustered using Affinity Propagation to determine search behaviours characteristic of the correct and incorrect response groups. A total of 23 participants (15 optometrists and 8 ophthalmologists) completed the grading task, with each assessing 20 images. Visual search behaviour differed between correct and incorrect responses, with data suggesting correct responses followed a visual search strategy incorporating the optic disc, macula, superior arcade, and inferior arcade as areas of interest. Data from incorrect responses suggest search behaviour driven by saliency or a search pattern unrelated to anatomical landmarks. Referable diabetic retinopathy was correctly identified in 86% of cases. Grader accuracy was 64.8% with good inter-grader agreement (α = 0.818). Our study suggests that a structured visual search strategy is correlated with higher accuracy when assessing retinal photographs for diabetic retinopathy. Referable diabetic retinopathy is detected at high rates; however, there is disagreement between clinicians when determining a precise severity grade.
本研究调查了验光师和接受过 fellowship 培训的眼科医生在筛查视网膜照片中的糖尿病视网膜病变时的视觉搜索行为。参与者在电脑屏幕上评估和分级视网膜照片，同时一台 Gazepoint GP3 HD 眼动追踪仪记录他们的眼动。使用隐马尔可夫模型从原始数据中得出感兴趣区域。通过将原始注视数据与感兴趣区域进行匹配，并使用图搜索算法解决模糊性问题，提取注视序列。使用亲和传播算法对注视序列进行聚类，以确定正确和错误反应组的特征性搜索行为。共有 23 名参与者（15 名验光师和 8 名眼科医生）完成了分级任务，每人评估 20 张图像。正确和错误反应的视觉搜索行为有所不同，数据表明正确反应遵循一种将视盘、黄斑、上方弓形区和下方弓形区作为感兴趣区域的视觉搜索策略。错误反应的数据表明搜索行为受显著性或与解剖标志无关的搜索模式驱动。在 86%的病例中正确识别出可转诊的糖尿病视网膜病变。分级者的准确率为 64.8%，分级者间一致性良好（α = 0.818）。我们的研究表明，在评估视网膜照片中的糖尿病视网膜病变时，结构化的视觉搜索策略与更高的准确率相关。可转诊的糖尿病视网膜病变检出率很高；然而，临床医生在确定精确的严重程度分级时存在分歧。
