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Published on: September 27, 2021
A deep learning-based method for evaluating the fitting states of orthokeratology lenses using fluorescein staining
Rong Lu1, Zhiqiang He1, Kai Niu1
1Key Laboratory of Universal Wireless Communications, Beijing University of Posts and Telecommunications, Beijing, 100876, China.
Purpose:
To develop and validate an automated deep learning-based tool for evaluating the fitting states of orthokeratology lenses using a dual-stream architecture based on ResNet-50 and a temporal attention module, compare its performance to the manual annotation results of experienced optometrists and assess the effectiveness of the methods used in the model.
Methods:
The dataset comprises 143 video recordings of fluorescein staining examinations conducted under slit-lamp microscopy. Five experienced optometrists independently evaluated these videos, classifying lens fitting states into three categories: loose-fitting, well-fitting, and tight-fitting. The consensus assessment of these optometrists was adopted as the gold standard. The ResNet-50 network was utilized to process intra-frame images and extract lens morphological features, while a temporal attention module was employed to analyze successive inter-frame images and capture lens movement features. To enhance the capture of spatial characteristics of lens fitting, an edge detection method was used as a form of data augmentation. After independent feature extraction, the outputs from the two branches were aggregated through concatenation, with the fused result being used for the final classification task.
Results:
By incorporating annotations from experienced optometrists, the proposed model demonstrates strong performance in classifying orthokeratology (Ortho-K) lens fitting states, achieving an overall accuracy of 92.3%, a macro sensitivity of 92.6%, and a macro specificity of 96.2%. The approach outperforms individual TimeSformer and ViViT models, with the integration of the proposed module into these models resulting in a significant enhancement in evaluation accuracy.
Conclusion:
The study demonstrated the effectiveness of the proposed model, which could automatically and accurately evaluate the fitting states of orthokeratology lenses. This approach offers a reliable and objective evaluation method, significantly aiding in the clinical assessment of orthokeratology lens fitting.
