Corneal opacity assessment using deep learning, multimodal fusion and explainable AI: A review

Corneal Opacity is a major cause of visual impairment and blindness, which represents a quantitative and qualitative loss of corneal transparency resulting from pathological, biological and structural alteration in the cornea. Accurate assessment of opacity in the field of image-based corneal opacity is essential for diagnosis, surgical planning, and outcome prediction driven by developments in high-resolution anterior segment imaging and computational analysis. The main objective of this review is to synthesize current advances in corneal opacity assessment using multimodal imaging and data-driven methodologies. A structured analysis of reviews is based on research published in major scientific databases, including Web of Science and Scopus, covering conventional techniques and advanced imaging modalities, such as anterior segment optical coherence tomography, Scheimpflug densitometry, ultrasound biomicroscopy, confocal microscopy, and emerging high-resolution optical methods. The literature demonstrates a clear trend towards the use of objective, automated, and depth-resolved imaging metrics derived from the most consistently validated imaging modality. Deep learning, quantitative image analysis, multimodal fusion, artificial intelligence, and explainable modeling frameworks have also been examined in recent developments. Despite significant progress in research activity observed over the past decade in overcoming challenges related to data heterogeneity, limited standardized datasets, and insufficient external validation continue to hinder clinical translation. Such development holds an informed diagnosis, provides appropriate treatment options, and develops an accurate prognosis; the accurate assessment of corneal opacity is essential. Integrating multimodal imaging with interpretable artificial intelligence holds substantial promise to support corneal opacity management.