Artificial intelligence in oculoplasty: Current applications and future perspectives

Table 1 Summary of recent artificial intelligence applications in oculoplastic surgery

Domain	Clinical task	Data modality	AI technique	Clinical relevance/limitations
Eyelid disorders	Ptosis grading, eyelid morphometry	Clinical photographs	CNN, U-Net	Objective measurement; limited by retrospective, single-centre datasets
Orbital pathologies	Tumor classification, TED assessment	CT, MRI	Deep learning segmentation, radiomics	Improved surgical planning; limited external validation
TED	Muscle/fat volume quantification	CT, MRI, facial images	U-Net, CNN	Severity grading; heterogeneous imaging protocols
Lacrimal disorders	Nasolacrimal duct obstruction detection, dacryocystorhinostomy planning	Dacryocystography, endoscopy	CNN, image classification	Intraoperative guidance; small sample sizes
Aesthetic and reconstructive	Outcome prediction, facial symmetry	Facial photographs, three-dimensional imaging	Generative AI, morphometric models	Patient counselling; risk of bias and overfitting

AI: Artificial intelligence; CNN: Convolutional neural network; CT: Computed tomography; MRI: Magnetic resonance imaging; TED: Thyroid eye disease.