A machine learning engineer details the development of a pediatric tuberculosis screening tool using approximately 2,000 chest X-rays. The project focused on overcoming challenges such as class imbalance, with only 9.8% of cases being positive, and significant data heterogeneity due to varying patient ages and imaging centers. The goal was to create a robust preprocessing pipeline and a reliable modeling approach capable of accurately triaging X-rays for children needing further confirmatory testing. AI
IMPACT This case study highlights methods for building robust AI models in resource-limited medical settings, potentially improving diagnostic accuracy for diseases like pediatric TB.
RANK_REASON Technical case study detailing the engineering behind an AI model for medical imaging. [lever_c_demoted from research: ic=1 ai=1.0]
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