Computer Vision Technology in the Differential Diagnosis of Cushing’s Syndrome

Abstract

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 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 larger sample with healthy control subjects. Further studies might pursue a more advanced analysis and classification algorithm.

Key words

pituitary adenoma - glucocorticoids - metabolic features, obesity

^* Medicover Neuroendokrinologie, Prof. Stalla und Kollegen, Munich, Germany.

^† Institute of General Practice and Family Medicine, Ludwig-Maximilians-Universität, Munich, Germany

^‡ Department of Internal Medicine I, Klinikum Augsburg, Augsburg, Germany

^§ Rheumazentrum Erding, Erding, Germany

^** Zentrum für Endokrinologie und Stoffwechsel Nymphenburg, Munich, Germany

• References

• 1 Nieman LK, Biller BM, Findling JW. et al. The diagnosis of Cushing’s syndrome: An Endocrine Society Clinical Practice Guideline. J Clin Endocrinol Metab 2008; 93: 1526-1540
• 2 Biller BM, Grossman AB, Stewart PM. et al. Treatment of adrenocorticotropin-dependent Cushing’s syndrome: A consensus statement. J Clin Endocrinol Metab 2008; 93: 2454-2462
• 3 Pivonello R, De Leo M, Cozzolino A. et al. The Treatment of Cushing’s Disease. Endocr Rev 2015; 36: 385-486
• 4 Dekkers OM, Biermasz NR, Pereira AM. et al. Mortality in patients treated for Cushing’s disease is increased, compared with patients treated for nonfunctioning pituitary macroadenoma. J Clin Endocrinol Metab 2007; 92: 976-981
• 5 Berr CM, Stieg MR, Deutschbein T. et al. Persistence of myopathy in Cushing’s syndrome: evaluation of the German Cushing’s Registry. Eur J Endocrinol 2017; 176: 737-746
• 6 Psaras T, Milian M, Hattermann V. et al. Demographic factors and the presence of comorbidities do not promote early detection of Cushing’s disease and acromegaly. Exp Clin Endocrinol Diabetes 2011; 119: 21-25
• 7 Valassi E, Santos A, Yaneva M. et al. The European Registry on Cushing’s syndrome: 2-year experience. Baseline demographic and clinical characteristics. Eur J Endocrinol 2011; 165: 383-392
• 8 Alberti KG, Eckel RH, Grundy SM. et al. Harmonizing the metabolic syndrome: A joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 2009; 120: 1640-1645
• 9 Baid SK, Rubino D, Sinaii N. et al. Specificity of screening tests for Cushing’s syndrome in an overweight and obese population. J Clin Endocrinol Metab 2009; 94: 3857-3864
• 10 Ellis E, Chin PK, Hunt PJ. et al. Is late-night salivary cortisol a better screening test for possible cortisol excess than standard screening tests in obese patients with Type 2 diabetes?. N Z Med J 2012; 125: 47-58
• 11 Srikanthan K, Feyh A, Visweshwar H. et al. Systematic review of metabolic syndrome biomarkers: A panel for early detection, management, and risk stratification in the west virginian population. Int J Med Sci 2016; 13: 25-38
• 12 Schneider HJ, Dimopoulou C, Stalla GK. et al. Discriminatory value of signs and symptoms in Cushing’s syndrome revisited: What has changed in 30 years?. Clin Endocrinol (Oxf) 2013; 78: 153-154
• 13 Kosilek RP, Frohner R, Wurtz RP. et al. Diagnostic use of facial image analysis software in endocrine and genetic disorders: review, current results and future perspectives. Eur J Endocrinol 2015; 173: M39-44
• 14 Schneider HJ, Kosilek RP, Gunther M. et al. A novel approach to the detection of acromegaly: accuracy of diagnosis by automatic face classification. J Clin Endocrinol Metab 2011; 96: 2074-2080
• 15 Kosilek RP, Schopohl J, Grunke M. et al. Automatic face classification of Cushing’s syndrome in women – A novel screening approach. Exp Clin Endocrinol Diabetes 2013; 121: 561-564
• 16 Frohner R. Optimierungsstrategien für Gesichtsklassifikation bei der softwaregestützten Erkennung von Akromegalie [Dissertation]. Ludwig-Maximilians-Universität München 2016
• 17 Frohner R, Würtz R, Kosilek R. et al. Optimierung der Gesichtsklassifikation bei der Erkennung von Akromegalie. Journal für Klinische Endokrinologie und Stoffwechsel-Austrian Journal of Clinical Endocrinology and Metabolism 2013; 6: 20-24
• 18 Günther M, Würtz RP. Face detection and recognition using maximum likelihood classifiers on gabor graphs. Int J Pattern Recognit Artif Intell 2009; 23: 433-461
• 19 Huang GB, Learned-Miller EG. Labeled Faces in the Wild: Updates and New Reporting Procedures. Technical Report UM-CS-2014-003. University of Massachusetts; Amherst, MA, USA: 2014
• 20 Huang GB, Mattar M, Berg T. et al. Labeled Faces in the Wild: A database for studying face recognition in unconstrained environments. Technical Report 07-49. University of Massachusetts; Amherst, MA, USA: 2008
• 21 Colao A, Petersenn S, Newell-Price J. et al. A 12-month phase 3 study of pasireotide in Cushing’s disease. N Engl J Med 2012; 366: 914-924
• 22 Boscaro M, Barzon L, Fallo F. et al. Cushing’s syndrome. Lancet 2001; 357: 783-791
• 23 Newell-Price J, Trainer P, Besser M. et al. The diagnosis and differential diagnosis of Cushing’s syndrome and pseudo-Cushing’s states. Endocr Rev 1998; 19: 647-672
• 24 IEEE. 13th International Conference on Automatic Face & Gesture Recognition (FG 2018). In Xi’an China: Institute of Electrical and Electronics Engineers (IEEE); 2018
• 25 Treleaven P, Wells J. 3D Body Scanning and Healthcare Applications. Computer 2007; 40: 28-34
• 26 Toma AM, Zhurov A, Playle R. et al. Reproducibility of facial soft tissue landmarks on 3D laser-scanned facial images. Orthod Craniofac Res 2009; 12: 33-42
• 27 Wasfy JH, Rao SK, Essien UR. et al. Initial experience with endocrinology e-consults. Endocrine 2017; 55: 640-642
• 28 Guaraldi F, Salvatori R. Cushing syndrome: Maybe not so uncommon of an endocrine disease. J Am Board Fam Med 2012; 25: 199-208