Maschinelles Sehen als Hilfsmittel in der Differentialdiagnostik des Cushing-Syndroms

Popp, Kathrin (2018): Maschinelles Sehen als Hilfsmittel in der Differentialdiagnostik des Cushing-Syndroms. Dissertation, LMU München: Medizinische Fakultät

Abstract

Objective: Cushing's syndrome is a rare disease characterized by clinical features that show morphological similarity with the metabolic syndrome. Distinguishing these diseases is a challenge in clinical practice. 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 age and body-mass-index. Methods: 82 patients (22 male, 60 female) and 98 control subjects (32 male, 66 female) matched by age, gender and body-mass-index were included. 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-one 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 higher for 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 sample with healthy control subjects. Further studies might pursue a more advanced analysis and classification algorithm.

• Dokumententyp: Dissertationen (Dissertation, LMU München)
• Themengebiete: 600 Technik, Medizin, angewandte Wissenschaften ; 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit
• Fakultäten: Medizinische Fakultät
• Sprache der Hochschulschrift: Deutsch
• Datum der mündlichen Prüfung: 8. Februar 2018
• 1. Bericht­erstatter:in: Schneider, Harald Jörn