This publications portal is a repository of all IOM migration health publications from 2006 to present where IOM was a primary contributor.
Publications include peer-reviewed scientific papers, technical reports, training guides/manuals, policy briefs/discussion papers, factsheets, newsletters, research reviews, conference and poster presentations. These are categorized by topic, author, country/region covered as well as by year, language, and type of publication. The map reflects the countries covered by the publications.
To browse or search: simply use the filter options on the left-hand side. Alternatively, you can enter keyword/s in the search box. Selecting a specific publication will lead to a ‘download’ link or link to the website where the document is housed. Here is the step-by-step guide for your reference.
A new resource on artificial intelligence powered computer automated detection software products for tuberculosis programmes and implementers
Author/s: Zhi Zhen Qin, Tasneem Naheyan, Morten Ruhwald, Claudia M. Denkinger, Sifrash Gelaw, Madlen Nash, Jacob Creswell, Sandra Vivian Kik
Recently, the number of artificial intelligence-powered computer-aided detection (CAD) products that detect tuberculosis (TB)-related abnormalities from chest X-rays (CXR) available on the market has increased. Although CXR is a relatively effective and inexpensive method for TB screening and triaging, a shortage of skilled radiologists in many high TB-burden countries limits its use. CAD technology offers a solution to this problem. Before adopting a CAD product,…Read more
The performance and yield of tuberculosis testing algorithms using microscopy, chest x-ray, and Xpert MTB/RIF
Author/s: Jacob Creswell, Zhi Zhen Qin, Rajedra Gurung, Bikash Lamichhane, Deepak Kumar Yadav, Mohan Kumar Prasai, Nirmala Bista, Lal Mani Adhikari, Bishwa Rai, Santat Sudrungrot
Using artificial intelligence to read chest radiographs for tuberculosis detection: A multi-site evaluation of the diagnostic accuracy of three deep learning systems
Author/s: Zhi Zhen Qin, Melissa S. Sander, Bishwa Rai, Collins N. Titahong, Santat Sudrungrot, Sylvain N. Laah, Lal Mani Adhikari, E. Jane Carter, Lekha Puri, Andrew J. Codlin, & Jacob Creswell
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 more