Neural network-based automated proptosis measurement using computed tomography images for patients with thyroid-associated orbitopathy

The purpose of this study was to evaluate the clinical feasibility and reliability of a neural network (NN)-based automated proptosis measurement system using computed tomography (CT) images. An automated proptosis measurement system was developed using the CT images of 200 eyes from 100 patients diagnosed with thyroid-associated orbitopathy. We compared the proptosis value obtained from the proposed automated system with the values obtained from the Hertel exophthalmometer and manual measurements of CT slices. The average measurement values were 17.77 ± 2.47 mm with the Hertel exophthalmometer, 18.87 ± 2.68 mm with the manual measurement of CT slices, and 19.30 ± 2.76 mm with the proposed automated system. There was no significant difference in the proptosis values measured using the manual and automated NN-based methods (p = 0.241). The values obtained from manual measurement and automated measurement using CT images showed excellent agreement with an intraclass correlation coefficient of 0.95. Based on the Bland-Altman plots, the 95% limits of agreement between manual CT and NN-based measurements were much smaller than those between Hertel exophthalmometer measurement and manual and NN-based measurements. In conclusion, automated NN-based measurements could provide a straightforward and efficient method for measuring proptosis using CT images.