Background Diabetic retinopathy (DR) is a leading cause of blindness in adults worldwide. Artificial intelligence (AI) with autonomous deep learning algorithms has been increasingly used in retinal image analysis, particularly for the screening of referrable DR. An established treatment for proliferative DR is panretinal or focal laser photocoagulation. Training autonomous models to discern laser patterns can be important in disease management and follow-up. Methods A deep learning model was trained for laser treatment detection using the EyePACs dataset. Data was randomly assigned, by participant, into development (n=18 945) and validation (n=2105) sets. Analysis was conducted at the single image, eye, and patient levels. The model was then used to filter input for three independent AI models for retinal indications; changes in model efficacy were measured using area under the receiver operating characteristic curve (AUC) and mean absolute error (MAE). Results On the task of laser photocoagulation detection: AUCs of 0.981, 0.95, and 0.979 were achieved at the patient, image, and eye levels, respectively. When analysing independent models, efficacy was shown to improve across the board after filtering. Diabetic macular oedema detection on images with artefacts was AUC 0.932 vs AUC 0.955 on those without. Participant sex detection on images with artefacts was AUC 0.872 vs AUC 0.922 on those without. Participant age detection on images with artefacts was MAE 5.33 vs MAE 3.81 on those without. Conclusion The proposed model for laser treatment detection achieved high performance on all analysis metrics and has been demonstrated to positively affect the efficacy of different AI models, suggesting that laser detection can generally improve AI-powered applications for fundus images. Data may be obtained from a third party and are not publicly available. Deidentified data used in this study are not publicly available at present. Parties interested in data access should contact Jorge Cuadros (jcuadros@eyepacs.com) for queries related to EyePACS.

中文翻译：

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使用深度学习对眼底图像中的激光光凝进行自主筛选


背景 糖尿病视网膜病变 (DR) 是全世界成年人失明的主要原因。具有自主深度学习算法的人工智能 (AI) 已越来越多地应用于视网膜图像分析，特别是用于筛选可参考的 DR。增殖性 DR 的既定治疗方法是全视网膜或局部激光光凝术。训练自主模型来识别激光模式对于疾病管理和随访非常重要。方法 使用 EyePACs 数据集训练深度学习模型以进行激光治疗检测。数据由参与者随机分配到开发组 (n=18 945) 和验证组 (n=2105)。在单图像、眼睛和患者层面进行分析。然后，该模型被用来过滤三个独立的人工智能模型的输入，用于视网膜指示；使用受试者工作特征曲线下面积（AUC）和平均绝对误差（MAE）来测量模型功效的变化。结果在激光光凝检测任务中：患者、图像和眼睛水平的 AUC 分别为 0.981、0.95 和 0.979。在分析独立模型时，过滤后功效显示全面提高。含有伪影的图像上的糖尿病黄斑水肿检测结果为 AUC 0.932，而没有伪影的图像上的糖尿病黄斑水肿检测结果为 AUC 0.955。带有人工制品的图像上的参与者性别检测为 AUC 0.872，而没有人工制品的图像上的参与者性别检测为 AUC 0.922。带有人工制品的图像上的参与者年龄检测为 MAE 5.33，而没有人工制品的图像上的参与者年龄检测为 MAE 3.81。 结论 所提出的激光治疗检测模型在所有分析指标上都取得了高性能，并且已被证明对不同人工智能模型的功效产生积极影响，这表明激光检测通常可以改善人工智能驱动的眼底图像应用。数据可能从第三方获得，并且不公开。本研究中使用的去识别化数据目前尚未公开。对数据访问感兴趣的各方应联系 Jorge Cuadros (jcuadros@eyepacs.com) 询问与 EyePACS 相关的问题。