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Popp, Kathrin Hannah; Kosilek, Robert Philipp; Frohner, Richard; Stalla, Guenther Karl; Athanasoulia-Kaspar, Anastasiap; Berr, Christina M.; Zopp, Stephanie; Reincke, Martin; Witt, Matthias; Wuertz, Rolf P.; Deutschbein, Timo; Quinkler, Marcus; Schneider, Harald Jörn (2019): Computer Vision Technology in the Differential Diagnosis of Cushing's Syndrome. In: Experimental and Clinical Endocrinology & Diabetes, Vol. 127, No. 10: pp. 685-690
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Objective Cushing's syndrome is a rare disease characterized by clinical features that show morphological similarity with the metabolic syndrome. Distinguishing these diseases in clinical practice is challenging. We have previously shown that Computer vision technology can be a potentially useful diagnostic tool in Cushing's syndrome. In this follow-up study, we addressed the described problem by increasing the sample size and including controls matched by body mass index. Methods We enrolled 82 patients (22 male, 60 female) and 98 control subjects (32 male, 66 female) matched by age, gender and body-mass-index. The control group consisted of patients with initially suspected, but biochemically excluded Cushing's syndrome. Standardized frontal and profile facial digital photographs were acquired. The images were analyzed using specialized computer vision and classification software. A grid of nodes was semi-automatically placed on disease-relevant facial structures for analysis of texture and geometry. Classification accuracy was calculated using a leave-one-out cross-validation procedure with a maximum likelihood classifier. Results The overall correct classification rates were 10/22 (45.5 %)for male patients and 26/32 (81.3 %) for male controls, and 34/60 (56.7%) for female patients and 43/66 (65.2 %) for female controls. In subgroup analyses, correct classification rates were higherfor iatrogenic than for endogenous Cushing's syndrome. Conclusion Regarding the advanced problem of detecting Cushing's syndrome within a study sample matched by body mass index, we found moderate classification accuracy by facial image analysis. Classification accuracy is most likely higher in a larger sample with healthy control subjects. Further studies might pursue a more advanced analysis and classification algorithm.