Development of a computer-aided tool for the pattern recognition of facial features in diagnosing Turner syndrome: comparison of diagnostic accuracy with clinical workers

Abstract

Technologies applied for the recognition of facial features in diagnosing certain disorders seem to be promising in reducing the medical burden and improve the efficiency. This pilot study aimed to develop a computer-assisted tool for the pattern recognition of facial features for diagnosing Turner syndrome (TS). Photographs of 54 patients with TS and 158 female controls were collected from July 2016 to May 2017. Finally, photographs of 32 patients with TS and 96 age-matched controls were included in the study that were further divided equally into training and testing groups. The process of automatic classification consisted of image preprocessing, facial feature extraction, feature reduction and fusion, automatic classification, and result presentation. A total of 27 physicians and 21 medical students completed a web-based test including the same photographs used in computer testing. After training, the automatic facial classification system for diagnosing TS achieved a 68.8% sensitivity and 87.5% specificity (and a 67.6% average sensitivity and 87.9% average specificity after resampling), which was significantly higher than the average sensitivity (57.4%, P < 0.001) and specificity (75.4%, P < 0.001) of 48 participants, respectively. The accuracy of this system was satisfactory and better than the diagnosis by clinicians. However, the system necessitates further improvement for achieving a high diagnostic accuracy in clinical practice.

Automatic Facial Recognition of Williams-Beuren Syndrome Based on Deep Convolutional Neural Networks

“…Artificial intelligence has been integrated into routine clinical practice, especially for providing diagnostic support. As one of the dominant areas of artificial intelligence, computer-aided recognition of dysmorphic faces has progressed in recent years ( 23 ).…”

Facial Recognition Intensity in Disease Diagnosis Using Automatic Facial Recognition

“…A single disease was targeted in 16 studies, including 3 studies on Cornelia de Lange syndrome [2,20], 2 on Turner syndrome [21,22], 3 on Down syndrome [16,17], 1 on Angelman syndrome [2], 4 on acromegaly [23][24][25][26], 2 on Cushing's syndrome [27,28], and 1 study on fetal alcohol spectrum disorders (FASD) [29], as multiple diseases were detected in 4 studies [17,19]. Nine studies used photographs from public databases and web pages [2,25,27], and 11 studies obtained their photographs in local hospitals [20][21][22][23][24]. Ten studies described the demographic characteristics of their study population, reporting a percentage of males ranging from 0 to 66.2% [16,17,21,22,[24][25][26].…”

“…Nine studies used photographs from public databases and web pages [2,25,27], and 11 studies obtained their photographs in local hospitals [20][21][22][23][24]. Ten studies described the demographic characteristics of their study population, reporting a percentage of males ranging from 0 to 66.2% [16,17,21,22,[24][25][26]. The diagnostic criteria of the targeted diseases were reported in 12 studies and included analysis of gene mutation [2,20] and karyotype [16,17,21,22], success of previous treatment [23], experts' opinions [26], diagnostic tests [24,27,29].…”

What Are Important Ethical Implications of Using Facial Recognition Technology in Health Care?

“…9 Machine learning can also detect more subtle correlations between facial morphology and genetic disorders than clinicians. 4 It is thought that FRT can therefore eventually be used to assist in earlier detection and treatment of genetic disorders, 10,11 and computer applications (commonly known as apps) such as Face2Gene have been developed to assist clinicians in diagnosing genetic disorders. 12…”