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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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>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 F<jats:sub>1<\/jats:sub>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\u2019 smartphone and slit-lamp images, the fusion model performance in smartphone-based images (F<jats:sub>1<\/jats:sub>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 (F<jats:sub>1<\/jats:sub>score of 0.9448, sensitivity of 0.9165, specificity of 0.9689, AUC of 0.9569 and accuracy of 94.29%).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec>","DOI":"10.1136\/bjo-2022-322552","type":"journal-article","created":{"date-parts":[[2023,3,8]],"date-time":"2023-03-08T14:15:12Z","timestamp":1678284912000},"page":"336-342","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":22,"title":["Accurate detection and grading of pterygium through smartphone by a fusion training 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