[No abstract available]
Deep learning (DL) neural networks have only recently been employed to interpret chest radiography (CXR) to screen and triage people for pulmonary tuberculosis (TB). No published studies have compared multiple DL systems and populations. We conducted a retrospective evaluation of three DL systems (CAD4TB, Lunit INSIGHT, and qXR) for detecting TB-associated abnormalities in chest radiographs from outpatients in Nepal and Cameroon. All 1196 individuals received a Xpert MTB/RIF assay and a CXR read by two groups of radiologists and the DL systems. Xpert was used as the reference standard.
(a) Objective(s): Contribute to the saving of lives, improvement in living conditions and well-being of populations in Cameroon who have fled as a result of the CAR crisis through: â€¢ Providing necessary registration and protection activities for evacuees; providing basic shelter, and NFI kits to the most vulnerable stranded migrants and returnees in transit sites before onward transportation â€¢ Assisting evacuees with health triage and referrals, as well as, pre-departure fitness to travel health checks and psychosocial
Since the beginning of 2014, Northeast Nigeria has witnessed an increase in violence conducted by the insurgency group Islamic State in West Africa (ISWA), leading to widespread displacement in the country with a spillover effect in neighboring countries including Niger, Chad, and Cameroon. The internal displacement situation in Cameroon poses many humanitarian challenges, both in terms of pressing humanitarian needs for IDPs, returnees and host families.
The increase of Islamic State in West Africa (ISWA) attacks on the Nigerian and Cameroonian territory since 2014 resulted in a displacement and refugee crisis. At the beginning of 2015, no system was in place to track and monitor displacement in Cameroon, resulting in a lack of clear information on the scope of the crisis and affected populations.