A deep learning framework using convolutional neural networks (CNNs) was reported to quantify Mycobacterium tuberculosis antibiotic resistance and predict patient responses to treatment. The approach targets an operational gap where resistance phenotyping and patient-level prediction can be slow or resource-intensive. If validated, the model could help clinicians select regimens sooner by translating microbiology readouts into diagnostic-grade resistance estimates tied to likely therapeutic outcomes.