Annette Uwineza

TL;DR: An algorithm for pooling subsamples based on the geometry of a hypercube that, at low prevalence, accurately identifies individuals infected with SARS-CoV-2 in a small number of tests and few rounds of testing is proposed.

TL;DR: It is demonstrated how facial analysis technology can assist clinicians in making accurate 22q11.2 DS diagnoses and will assist in earlier detection and in increasing recognition of 22q12.2 deletion syndrome throughout the world.

TL;DR: Evaluation using a digital facial analysis technology of a larger diverse cohort of newborns to adults was able to diagnose Down syndrome with a sensitivity of 0.961, specificity of0.924, and accuracy of 1.065, demonstrating the accuracy and promise of digital facialAnalysis technology in the diagnosis of Down syndrome internationally.

TL;DR: In this paper, individuals from diverse populations with NOONAN syndrome were evaluated clinically and by facial analysis technology, and the most common clinical characteristics found in all population groups included widely spaced eyes and low-set ears in 80% or greater of participants, short stature in more than 70%, and pulmonary stenosis in roughly half of study individuals.

TL;DR: This study looked at individuals from diverse populations with both clinical and molecularly confirmed diagnoses of CdLS by facial analysis technology and found that sensitivity was equal or greater than 95% for all groups.