PT - JOURNAL ARTICLE AU - Yuwen Liu AU - Changsheng Xu AU - Shaopan Wang AU - Yuguang Chen AU - Xiang Lin AU - Shujia Guo AU - Zhaolin Liu AU - Yuqian Wang AU - Houjian Zhang AU - Yuli Guo AU - Caihong Huang AU - Huping Wu AU - Ying Li AU - Qian Chen AU - Jiaoyue Hu AU - Zhiming Luo AU - Zuguo Liu TI - Accurate detection and grading of pterygium through smartphone by a fusion training model AID - 10.1136/bjo-2022-322552 DP - 2023 Mar 01 TA - British Journal of Ophthalmology PG - bjo-2022-322552 4099 - http://bjo.bmj.com/content/early/2023/02/28/bjo-2022-322552.short 4100 - http://bjo.bmj.com/content/early/2023/02/28/bjo-2022-322552.full AB - Background/aims To improve the accuracy of pterygium screening and detection through smartphones, we established a fusion training model by blending a large number of slit-lamp image data with a small proportion of smartphone data.Method Two datasets were used, a slit-lamp image dataset containing 20 987 images and a smartphone-based image dataset containing 1094 images. The RFRC (Faster RCNN based on ResNet101) model for the detection model. The SRU-Net (U-Net based on SE-ResNeXt50) for the segmentation models. The open-cv algorithm measured the width, length and area of pterygium in the cornea.Results The detection model (trained by slit-lamp images) obtained the mean accuracy of 95.24%. The fusion segmentation model (trained by smartphone and slit-lamp images) achieved a microaverage F1 score of 0.8981, sensitivity of 0.8709, specificity of 0.9668 and area under the curve (AUC) of 0.9295. Compared with the same group of patients’ smartphone and slit-lamp images, the fusion model performance in smartphone-based images (F1 score of 0.9313, sensitivity of 0.9360, specificity of 0.9613, AUC of 0.9426, accuracy of 92.38%) is close to the model (trained by slit-lamp images) in slit-lamp images (F1 score of 0.9448, sensitivity of 0.9165, specificity of 0.9689, AUC of 0.9569 and accuracy of 94.29%).Conclusion Our fusion model method got high pterygium detection and grading accuracy in insufficient smartphone data, and its performance is comparable to experienced ophthalmologists and works well in different smartphone brands.Data are available on reasonable request. The data generated and/or analysed during the current study are available on reasonable request from the corresponding author ZuL (zuguoliu@xmu.edu.cn).