Mobile Diagnosis of Congenital Genetic Conditions: A Model for Screening and Surveillance in Low-Resource Settings

SUMMARY Congenital anomalies represent an increasing burden of disease worldwide, accounting for millions of birth defect-related disabilities with a disproportionate impact on Low to Middle Income Countries (LIMCs). Many harbor genetic etiologies, for which no confirmatory diagnosis can be made due to the dearth of diagnostic

technologies in most LMICs. The inability to rapidly and accurately diagnose individuals that harbor a genetic syndrome increases the risk of mortality and morbidity (as a number of manageable congenital anomalies may be hidden, such as congenital heart defects or hearing infections) and prevents the accurate determination of

prevalence rates, critical for public health surveillance and intervention programs. The first part (R21) of this project addresses these gaps using two synergistic mobile health intervention tools to screen for syndromic conditions and specifically demonstrate that a specific diagnostic of Down syndrome

(expandable to all aneuploidies and those diseases resulting from copy number variants, point mutations and insertions/deletions) can be performed with minimal resources, in the Democratic Republic of the Congo (DRC). Aim 1 will be to train and validate AI-guided smartphone-based technology to screen for syndromic

conditions, while Aim 2 will create low-cost, rapid initial genetic diagnostic capacity in the DRC. In the expansion part of the proposal (R33), we will test whether the implementation of a registry measuring health outcomes can be used as a scalable model for future newborn screening and health surveillance in a

low-resource setting. To this effect, Aim 3 will build infrastructure for birth defects detection, genetic confirmation, competence building, and practice and outcomes surveillance in low-resource conditions with two parallel sub-aims: Aim 3a will assess the feasibility of the diagnostic capacity on a large

population sample and provide a tool to measure specific health outcomes, while Aim 3b will establish a small-scale and functional database/registry of morphological, genetic, and health outcomes data. In limited resources settings, comprehensive systems to detect, refer, treat and surveil individuals with

congenital anomalies are non-existent. Our innovative technologies will address this gap, build local capacity of diagnostic screening and of a data registry, allowing for early diagnosis and condition-specific care, likely to lower morbidity and mortality of children with non-communicable syndromic conditions.