Incomplete Multimodal Learning for Visual Acuity Prediction After Cataract Surgery Using Masked Self-Attention

As the primary treatment option for cataracts, it is estimated that millions of cataract surgeries are performed each year globally. Predicting the Best Corrected Visual Acuity (BCVA) in cataract patients is crucial before surgeries to avoid medical disputes. However, accurate prediction remains a challenge in clinical practice. Traditional methods based on patient characteristics and surgical parameters have limited accuracy and often underestimate postoperative visual acuity. In this paper, we propose a novel framework for predicting visual acuity after cataract surgery using masked self-attention. Especially different from existing methods, which are based on monomodal data, our proposed method takes preoperative images and patient demographic data as input to leverage multimodal information. Furthermore, we expand our method to a more complex and challenging clinical scenario, i.e., the incomplete multimodal data. Firstly, we apply efficient Transformers to extract modality-specific features. Then, an attentional fusion network is utilized to fuse the multimodal information. To address the modality-missing problem, an attention mask mechanism is proposed to improve the robustness. We evaluate our method on a collected dataset of 1960 patients who underwent cataract surgery and compare its performance with other state-of-the-art approaches. The results show that our proposed method outperforms other methods and achieves a mean absolute error of 0.122 logMAR. The percentages of the prediction errors within +/- 0.10 logMAR are 94.3%. Besides, extensive experiments are conducted to investigate the effectiveness of each component in predicting visual acuity. Codes will be available at https://github.com/liyiersan/MSA.