PeriorbitAI: Artificial intelligence automation of eyelid and periorbital measurements.

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Abstract

To develop a deep learning semantic segmentation network to automate the assessment of eight periorbital measurements.
Development and validation of an AI segmentation algorithm METHODS: A total of 418 photographs of periorbital areas were used to train a deep learning semantic segmentation model to segment iris, aperture, and brow areas. This data was used to develop a post-processing algorithm which measured margin reflex distance(MRD) 1 and 2, medial canthal height(MCH), lateral canthal height(LCH), medial brow height(MBH), lateral brow height(LBH), medial intercanthal distance(MID), and lateral intercanthal distance(LID). The algorithm validity was evaluated on a prospective hold-out test set against three graders. The main outcome measures were dice coefficient, mean absolute difference, intraclass correlation coefficient, and Bland Altman analysis. A smart-phone video was also segmented and evaluated as proof of concept.
The AI algorithm performed in close agreement with all human graders, with a mean absolute difference of 0.5 mm for MRD1, MRD2, LCH, and MCH. The mean absolute difference between graders is approximately 1.5-2 mm for LBH and MBH and approximately 2-4 mm for MID and LID. The 95% confidence intervals for all graders overlapped in most cases demonstrating that the algorithm performs similarly to human graders. The segmentation of a smartphone video demonstrated that MRD1 can be dynamically measured.
We present, to the best of our knowledge, the first open sourced, artificial intelligence system capable of automating static and dynamic periorbital measurements. A fully automated tool stands to transform the delivery of clinical care and quantification of surgical outcomes.
Copyright © 2021. Published by Elsevier Inc.