Aims. The lack of primary ophthalmologists in China results in the inability of basic-level hospitals to diagnose pterygium patients. To solve this problem, an intelligent-assisted lightweight pterygium diagnosis model based on anterior segment images is proposed in this study. Methods. Pterygium is a common and frequently occurring disease in ophthalmology, and fibrous tissue hyperplasia is both a diagnostic biomarker and a surgical biomarker. The model diagnosed pterygium based on biomarkers of pterygium. First, a total of 436 anterior segment images were collected; then, two intelligent-assisted lightweight pterygium diagnosis models (MobileNet 1 and MobileNet 2) based on raw data and augmented data were trained via transfer learning. The results of the lightweight models were compared with the clinical results. The classic models (AlexNet, VGG16 and ResNet18) were also used for training and testing, and their results were compared with the lightweight models. A total of 188 anterior segment images were used for testing. Sensitivity, specificity, F1-score, accuracy, kappa, area under the concentration-time curve (AUC), 95% CI, size, and parameters are the evaluation indicators in this study. Results. There are 188 anterior segment images that were used for testing the five intelligent-assisted pterygium diagnosis models. The overall evaluation index for the MobileNet2 model was the best. The sensitivity, specificity, F1-score, and AUC of the MobileNet2 model for the normal anterior segment image diagnosis were 96.72%, 98.43%, 96.72%, and 0976, respectively; for the pterygium observation period anterior segment image diagnosis, the sensitivity, specificity, F1-score, and AUC were 83.7%, 90.48%, 82.54%, and 0.872, respectively; for the surgery period anterior segment image diagnosis, the sensitivity, specificity, F1-score, and AUC were 84.62%, 93.50%, 85.94%, and 0.891, respectively. The kappa value of the MobileNet2 model was 77.64%, the accuracy was 85.11%, the model size was 13.5 M, and the parameter size was 4.2 M. Conclusion. This study used deep learning methods to propose a three-category intelligent lightweight-assisted pterygium diagnosis model. The developed model can be used to screen patients for pterygium problems initially, provide reasonable suggestions, and provide timely referrals. It can help primary doctors improve pterygium diagnoses, confer social benefits, and lay the foundation for future models to be embedded in mobile devices.

中文翻译：

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基于眼前节图像的轻量级翼状胬肉智能诊断模型研究

目标。我国缺乏初级眼科医生，导致基层医院无法诊断翼状胬肉患者。针对这一问题，本研究提出了一种基于眼前节图像的智能辅助轻量型翼状胬肉诊断模型。方法. 翼状胬肉是眼科常见病和多发病，纤维组织增生既是诊断生物标志物，也是手术生物标志物。该模型根据翼状胬肉的生物标志物诊断翼状胬肉。首先，共收集到 436 幅眼前节图像；然后，通过迁移学习训练了两个基于原始数据和增强数据的智能辅助轻量级翼状胬肉诊断模型（MobileNet 1 和 MobileNet 2）。将轻量级模型的结果与临床结果进行比较。经典模型（AlexNet、VGG16 和 ResNet18）也被用于训练和测试，并将它们的结果与轻量级模型进行了比较。共有 188 幅眼前节图像用于测试。灵敏度、特异性、F1 评分、准确度、kappa、浓度-时间曲线下面积 (AUC)、结果. 共有188张眼前节图像用于测试五种智能辅助翼状胬肉诊断模型。MobileNet2 模型的整体评价指标最好。MobileNet2模型对正常眼前节图像诊断的敏感性、特异性、F1-score和AUC分别为96.72%、98.43%、96.72%和0976；翼状胬肉观察期眼前节影像诊断的敏感性、特异性、F1评分和AUC分别为83.7%、90.48%、82.54%和0.872；手术期眼前节影像诊断的敏感性、特异性、F1评分和AUC分别为84.62%、93.50%、85.94%和0.891。MobileNet2模型的kappa值为77.64%，准确率为85.11%，模型大小为13.5 M，参数大小为4.2 M。结论。本研究采用深度学习方法，提出三类智能轻量级翼状胬肉诊断模型。所开发的模型可用于初步筛查患者翼状胬肉问题，提供合理建议，及时转诊。它可以帮助基层医生提高翼状胬肉诊断，赋予社会效益，并为未来模型嵌入移动设备奠定基础。