ArticleFrontiers in medicine2026
Deep learning-based differentiation of non-tuberculous mycobacterial lung disease and pulmonary tuberculosis using chest CT.
Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Differentiating non-tuberculous mycobacterial lung disease (NTM-LD) from pulmonary tuberculosis (PTB) remains a significant clinical challenge owing to their overlapping clinical and imaging features, despite markedly different therapeutic strategies. Methods: A total of 409 patients with microbiologically confirmed diagnoses were retrospectively enrolled and randomly divided into a training set ( Results: The proposed 3D ResNeXt model achieved the highest performance, with an AUC of 0.89 and accuracy of 0.89 on the training set, and an AUC of 0.83 and accuracy of 0.84 on the independent test set. DeLong's test confirmed statistically significant superiority over all six comparator architectures on the test set (all Conclusion: The 3D ResNeXt model demonstrated superior and interpretable performance in differentiating NTM-LD from PTB on chest CT. It holds promise as a valuable clinical decision-support tool, although prospective multicenter validation is warranted.
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