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February 11, 2026Open Access

A Systematic Review of Artificial Intelligence-Assisted Chest X-Ray Interpretation for Tuberculosis Detection in Lagos Island Primary Healthcare Centres,

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Authors

COChinwe OkonkwoTOTunde OlawaleNENgozi Eze

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Overview

This systematic review evaluates AI's diagnostic performance for TB detection in Lagos Island, suggesting improvements are necessary for effective implementation.

Key Points

  • This review aims to assess the effectiveness and challenges of using AI for chest X-ray interpretation in tuberculosis detection in Lagos Island.
  • Conducted a systematic search of electronic databases
  • Included studies on diagnostic accuracy and implementation of AI for TB
  • Performed independent selection, extraction, and quality assessment of studies
  • Identified limited eligible studies on AI-assisted CXR for TB detection
  • AI algorithms showed sensitivity for TB detection exceeding 85%
  • Integration of AI tools faced significant challenges due to infrastructure issues

Cite This Study

Okonkwo et al. (2010) studied this question.

synapsesocial.com/papers/698c1c33267fb587c655e81ahttps://doi.org/10.5281/zenodo.18529526
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A Systematic Review of Artificial Intelligence-Assisted Chest Radiography for Tuberculosis Case Detection in Primary Health Centres of Maputo Province, Mozambique2016
  2. 2Implementing AI-enabled chest X-ray for community-based integrated screening for tuberculosis, chronic respiratory diseases, and cardiovascular diseases in Nigeria2026
  3. 3Artificial Intelligence-Assisted chest X-ray for tuberculosis case finding in low- and Middle-Income countries: Implementation experiences and impact2026
  4. 4Diagnostic Accuracy of Artificial Intelligence-assisted Chest X-ray Interpretation Tools for Screening of Tuberculosis: A Systematic Review and Meta-analysis2025 · 1 citations
  5. 5Artificial Intelligence for Tuberculosis Screening and Detection: From Evidence to Policy and Implementation2026 · 1 citations