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.

Toward a Diagnostic Score in Cushing's Syndrome

“…The overall correct classification rates were 46 and 81% for male patients and controls, and 57 and 65% for female patients and controls, respectively. This moderate diagnostic accuracy is probably related to the clinical characteristics of the control group (“rule-out CS patients”) who have shown some signs of CS, whereas discrimination form a normal control group would have been more efficient (52).…”

Classification of Facial Images to Assist in the Diagnosis of Autism Spectrum Disorder

“…The best result was achieved by the SVM classifier with 86.2% accuracy. Considering that the facial differences from individuals with ASD and TD appear to be subtle, this is a promising result compared to the 62.8% achieved in 24 for Cushing's syndrome. It indicates that it is possible to use anthropometric measures to assist in ASD diagnosis, even when a small sample size is available (41 ASD and 69 TD instances in this study) using traditional machine learning algorithms.…”

“…For syndromes where the facial dismorphism pattern is less evident, however, the accuracy may be lower. For example, in Cushing's syndrome, the achieved accuracy was of 62.8% 24 .…”

Current applications of artificial intelligence combined with urine detection in disease diagnosis and treatment

“…The core technologies of AI include computer vision, machine learning, natural language processing, robotics, and speech recognition (25,26). AI technology applications in the medical field are mainly computer vision and machine learning (27)(28)(29)(30)(31)(32)(33)(34). Computer vision aims to replace the visual organ as the input means through the imaging system, then analyzes and processes the image through the computer.…”

“…Computer vision aims to replace the visual organ as the input means through the imaging system, then analyzes and processes the image through the computer. Therefore, computer vision is widely used in medical imaging to improve recognition and analysis ability to help predict and diagnose the disease (27)(28)(29)(30)(31)(32). AI has been widely used in the image-based diagnosis and has shown strong perandom forestsormance.…”