Chandrasekaran AC, Fu Z, Kraniski R, et al. - This study was attempted to investigate the hypothesis that computer vision used to dual-energy computed tomography (DECT) finger images is a useful approach for precise and accurate calcinosis cutis (CC) quantification in systemic sclerosis (SSc) patients. Researchers obtained 40 de-identified 2D DECT images from SSc patients with clinically evident finger CC lesions and divided them into training (N = 30 with image rotation × 3 to expand the set to N = 120) and test sets (N = 10). They further developed and tested an expert musculoskeletal radiologist confirmed accurate manual segmentation (subtraction) of the phalanges for each image as a gold standard, and a U-Net Convolutional Neural Network (CNN) computer vision model for segmentation of healthy phalanges. This study’s findings indicate that CNN quantification has a high degree of association with expert radiologist measurement of finger CC area measurements. Further study will include segmentation of 3-dimensional images for volumetric and density quantification, as well as validation in larger, independent cohorts.