Facial analysis technology for the detection of Down syndrome in the Democratic Republic of the Congo

Abstract

Down syndrome is one of the most common chromosomal anomalies affecting the world's population, with an estimated frequency of 1 in 700 live births. Despite its relatively high prevalence, diagnostic rates based on clinical features have remained under 70% for most of the developed world and even lower in countries with limited resources. While genetic and cytogenetic confirmation greatly increases the diagnostic rate, such resources are often non-existent in many low- and middle-income countries, particularly in Sub-Saharan Africa. To address the needs of countries with limited resources, the implementation of mobile, user-friendly and affordable technologies that aid in diagnosis would greatly increase the odds of success for a child born with a genetic condition. Given that the Democratic Republic of the Congo is estimated to have one of the highest rates of birth defects in the world, our team sought to determine if smartphone-based facial analysis technology could accurately detect Down syndrome in individuals of Congolese descent. Prior to technology training, we confirmed the presence of trisomy 21 using low-cost genomic applications that do not need advanced expertise to utilize and are available in many low-resourced countries. Our software technology trained on 132 Congolese subjects had a significantly improved performance (91.67% accuracy, 95.45% sensitivity, 87.88% specificity) when compared to previous technology trained on individuals who are not of Congolese origin (p < 5%). In addition, we provide the list of most discriminative facial features of Down syndrome and their ranges in the Congolese population. Collectively, our technology provides low-cost and accurate diagnosis of Down syndrome in the local population.

Keywords: Congo; DRC; Down syndrome; Facial analysis; Machine learning; Screening.

Fig. 1:

(a) Landmarks and metrics used by the facial analysis technology to quantify facial phenotypes. Blue lines represent distances. Horizontal distances are normalized with respect to the distance between the lateral canthi (H), and vertical distances are normalized to the distance between the lateral canthi and the oral commisures (V). Dashed green lines represent angles centered at the landmarks with green circles. Appearance features are calculated at different resolutions around each of the 33 facial landmarks. (b) Metris that are significantly different beteween patients with Down syndrome and normative subjecs in the population of the DRC. Blue lines and landmarks in red represent the distance and appearance metrics, respectively, used by our classifier to identify Down syndrome in the population of the DRC (presented in Tables 2 and 3). The other metrics that are significantly different between patients with Down syndrome and healthy subjects and that are presented in Table 4 are depicted in yellow.

Fig. 2:

Distribution of the ratios of segmental duplications of chromosome 11 versus chromosome 21 for negative controls, independent positive controls and cases of Down syndrome identified in the DRC. **** indicates a p value < 0.001 as measured by the t-test.