Inference of Visual Acuity and Postoperative Outcomes from Routine Biometry Images Using Pretrained Embedding Architectures and Machine Learning Techniques: A feasibility Study (Preprint)

BACKGROUND As the world’s most frequent surgical procedure, cataract surgery is essential for restoring vision and quality of life. However, routine diagnostic data remains underutilized e.g. for predicting functional outcomes, which is vital for managing patient expectations. This study uses deep learning to leverage such data, providing an objective basis for personalized surgical counseling. OBJECTIVE To evaluate the feasibility of a deep learning framework for objectively inferring current visual acuity (VA) and forecasting postoperative VA improvement by leveraging latent anatomical data from routine biometry imaging. METHODS A machine learning pipeline was developed using the PyCaret library to process high-dimensional embeddings. Features were extracted from standardized image types using three state-of-the-art architectures for comparison: OpenAI’s CLIP (ViT-B/32), Meta’s DinoV2 (ViT-S/14), and the YOLO11n-cls classification backbone. Extracted features were concatenated and classified into discrete VA and logMAR gain categories. The study analyzed images from the ZEISS IOLMaster 700 (anterior segment OCT, frontal eye photographs, and foveal fixation OCT) across two cohorts: a large-scale same-day inference dataset consisting of 15,074 eyes and a longitudinal postoperative dataset of 650 eyes. RESULTS The YOLO11n-cls architecture consistently yielded the most clinically relevant feature space. For same-day VA inference, a YOLO-based Logistic Regression model achieved a macro-averaged AUC of 0.80, peaking at 0.85 for high-acuity cases. In the postoperative task, a YOLO-XGBoost configuration achieved an AUC of 0.84 for predicting significant visual gains (≥0.4 logMAR improvement). CONCLUSIONS Routine biometry images contain a robust biological signal for visual potential. YOLO-based architectures demonstrate superior efficacy in feature extraction for standardized ophthalmic imaging, providing a viable pathway for objective surgical counseling and preoperative decision support. The combination with ensemble learning techniques seems to be a good way of extracting clinically relevant features.