Artificial intelligence and diagnosis and management of tuberculosis disease in children
Artificial intelligence enhances the diagnosis and treatment prediction of PdTB in resource-constrained settings. The integration of artificial intelligence with existing diagnostic tools like GeneXpert and telemedicine strategies can significantly improve the efficiency of screening processes. Future research efforts should prioritize the expansion of pediatric datasets and the evaluation of multimodal AI-PdTB approaches.
Authors
- Declan Ikechukwu Emegano (ORCID: https://orcid.org/0000-0003-0258-9624)
- Emeje Paul Isaac
- Oyibo Saidu Silas
- İlker Özsahin
- Dilber Uzun Ozsahin
- Berna Uzun
- Chinonye Lynda Emegano
Institutions
- Federal University Lokoja (NG)
- University of Sharjah (AE)
- Medical Diagnostic Laboratories (United States) (US)
- Primary HealthCare (MT)
- Federal Medical Centre (NG)
- Near East University (CY)
- Saveetha University (IN)
Publication Details
- Journal
- Current Opinion in Pediatrics
- Published
- 2026-02-09
- DOI
- https://doi.org/10.1097/mop.0000000000001551
- Primary Topic
- Tuberculosis Research and Epidemiology
- Type
- article
- Field-Weighted Citation Impact
- 0.00