标题： FundaQ-8：一种临床启发的自动视网膜图像质量评分框架 显示英文标题

标题： FundaQ-8: A Clinically-Inspired Scoring Framework for Automated Fundus Image Quality Assessment

摘要： 自动视网膜图像质量评估（FIQA）由于图像采集的差异和主观专家评估而仍然是一个挑战。 我们引入了FundaQ-8，一种新的经过专家验证的框架，用于使用八个关键参数系统地评估视网膜图像质量，包括视野覆盖范围、解剖可见性、光照和图像伪影。 使用FundaQ-8作为结构化评分参考，我们开发了一个基于ResNet18的回归模型，以预测0到1范围内的连续质量分数。 该模型在来自真实临床来源和Kaggle数据集的1800张视网膜图像上进行训练，使用迁移学习、均方误差优化和标准化预处理。 与EyeQ数据集的验证和统计分析确认了该框架的可靠性和临床可解释性。 将FundaQ-8纳入用于糖尿病视网膜病变分级的深度学习模型中，也提高了诊断的鲁棒性，突显了在实际筛查应用中质量感知训练的价值。 显示英文摘要

摘要： Automated fundus image quality assessment (FIQA) remains a challenge due to variations in image acquisition and subjective expert evaluations. We introduce FundaQ-8, a novel expert-validated framework for systematically assessing fundus image quality using eight critical parameters, including field coverage, anatomical visibility, illumination, and image artifacts. Using FundaQ-8 as a structured scoring reference, we develop a ResNet18-based regression model to predict continuous quality scores in the 0 to 1 range. The model is trained on 1800 fundus images from real-world clinical sources and Kaggle datasets, using transfer learning, mean squared error optimization, and standardized preprocessing. Validation against the EyeQ dataset and statistical analyses confirm the framework's reliability and clinical interpretability. Incorporating FundaQ-8 into deep learning models for diabetic retinopathy grading also improves diagnostic robustness, highlighting the value of quality-aware training in real-world screening applications.