Aplicación o artificial Inteligencie modelos in mhest x-ray Interpretation for pulmonary tuberculosis diagnosis
DOI:
https://doi.org/10.71112/5vy5w189Keywords:
Pulmonary tuberculosis, Artificial inteligente, Deep Lear Ning, Machine Lear Ning, Scoping reviewAbstract
Pulmonary tuberculosis (PTB) remains a major cause of infectious mordidita and mortality worldwide. Artificial intelligence (AI), particularly through deep learning techniques, has emerged as a promising tool for automated diagnosis using chest X-ray imaging. This scoping review aimed to map and characterize the available scientific evidence on AI models applied to PTB diagnosis. A systematic search was conducted across six international databases following the Joanna Briggs Institute methodology and PRISMA-ScR guidelines. Twenty studies published between 2020 and 2025 were included, predominantly employing convolutional neural networks (CNN), hybrid architectures with transformers, and contextual segmentation models. Reported accuracies ranged from 96% to 99.9%. AI demonstrated diagnostic performance superior to human interpretation under controlled conditions, although challenges remain regarding standardization, clinical validation, and geographic representativeness.
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