Artificial Intelligence for Lacrimal Drainage Disorders: Advances in Diagnosis, Management, and Clinical Translation.

Purpose

Lacrimal drainage disorders (LDDs) are common in oculoplastic practice, but their diagnostic evaluation continues to depend on tests that are variably subjective, operator-dependent, and at times invasive. In recent years, artificial intelligence (AI), spanning conventional machine learning (ML), deep learning (DL), radiomics, and large language models (LLMs), has shown increasing potential to enhance screening, diagnostic classification, lesion localization, and decision support in LDDs. This narrative review summarizes the current evidence on AI applications in the lacrimal field, places these studies within the broader context of AI in oculoplastic practice, and highlights the principal barriers to clinical translation.

Methods

We conducted a narrative review of studies applying AI methods to lacrimal drainage disorders and related oculoplastic tasks, with emphasis on ML, DL, radiomics, image segmentation, and LLM-based approaches. Eligible evidence was synthesized according to major clinical use cases, including noninvasive screening, functional and anatomic diagnostic classification, obstruction localization, surgical planning, and patient-facing or clinician-facing decision support.

Results

Dedicated studies have demonstrated the feasibility of ML and DL models for classifying anatomic versus functional epiphora from lacrimal scintigraphy, screening for lacrimal duct obstruction from anterior segment optical coherence tomography (AS-OCT) tear meniscus images, noninvasively predicting dacryocystitis from ocular surface indicators using conventional ML and explainable deep stacked networks, automatically detecting and localizing obstruction on dacryocystography (DCG), and segmenting the bony nasolacrimal canal on cone-beam computed tomography (CBCT) using the nnU-Net v2 architecture. In parallel, LLMs such as ChatGPT and DeepSeek have been benchmarked on lacrimal knowledge, showing average but rapidly improving performance.

Conclusions

AI in LDDs remains at an early, largely single-center stage, but the trajectory is clear: image-based models already approach clinician-level accuracy for well-defined tasks, explainable tabular models make noninvasive screening plausible, and segmentation networks are becoming precise enough to support surgical planning. External validation, prospective evaluation, standardization of lacrimal imaging data sets, and careful integration with clinical workflows are the next steps needed to translate these proofs of concept into routine lacrimal practice.