State-of-the-Art Periorbital Distance Prediction and Disease Classification Using Periorbital Features

Periorbital distances are critical markers for diagnosing and monitoring a range of oculoplastic and craniofacial conditions. Manual measurement, however, is subjective and prone to intergrader variability. Automated methods have been developed but remain limited by standardized imaging requirements, small datasets, and a narrow focus on individual measurements. We developed a segmentation pipeline trained on a domain-specific dataset of healthy eyes and compared its performance against the Segment Anything Model (SAM) and the prior benchmark, PeriorbitAI. Segmentation accuracy was evaluated across multiple disease classes and imaging conditions. We further investigated the use of predicted periorbital distances as features for disease classification under in-distribution (ID) and out-of-distribution (OOD) settings, comparing shallow classifiers, CNNs, and fusion models. Our segmentation model achieved state-of-the-art accuracy across all datasets, with error rates within intergrader variability and superior performance relative to SAM and PeriorbitAI. In classification tasks, models trained on periorbital distances matched CNN performance on ID data (77--78\% accuracy) and substantially outperformed CNNs under OOD conditions (63--68\% accuracy vs. 14\%). Fusion models achieved the highest ID accuracy (80\%) but were sensitive to degraded CNN features under OOD shifts. Segmentation-derived periorbital distances provide robust, explainable features for disease classification and generalize better under domain shift than CNN image classifiers. These results establish a new benchmark for periorbital distance prediction and highlight the potential of anatomy-based AI pipelines for real-world deployment in oculoplastic and craniofacial care.

@article{nahass2025_2409.18769,
  title={ State-of-the-Art Periorbital Distance Prediction and Disease Classification Using Periorbital Features },
  author={ George R. Nahass and Sasha Hubschman and Jeffrey C. Peterson and Ghasem Yazdanpanah and Nicholas Tomaras and Madison Cheung and Alex Palacios and Kevin Heinze and Chad A. Purnell and Pete Setabutr and Ann Q. Tran and Darvin Yi },
  journal={arXiv preprint arXiv:2409.18769},
  year={ 2025 }
}