ArticleScientific data2025
An Integrated Mycobacterial CT Imaging Dataset with Multispecies Information.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Revealing hidden material composition via physics-induced deep learning for precision clinical diagnosis.Patterns (New York, N.Y.) · 2026Article
- Whole-Lung CT Radiomics-Based Machine Learning Classification of Nontuberculous Mycobacterial Lung Disease Across Geographically Distinct Cohorts.medRxiv : the preprint server for health sciences · 2026Article
- Radiomics dataset from chest CT of clinically healthy adults.Data in brief · 2026Article
- Rapid Triage of Smear-Positive Tuberculosis Suspects with a Commercial DNA Microarray: A Real-World Experience from 1100 Sputum Specimens.Infection and drug resistance · 2026Article
- Harnessing the gut microbiome to combat tuberculosis: a technological and clinical review.Frontiers in cellular and infection microbiology · 2026Review
- Thoracic magnetic resonance imaging in non-tuberculous mycobacterial pulmonary disease: Characteristics and potential implementation.PloS one · 2025Article
- Diagnosis of nontuberculous mycobacterial infections using genomics and artificial intelligence-machine learning approaches: scope, progress and challenges.Frontiers in microbiology · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
The increasing global incidence of nontuberculous mycobacterial (NTM) pulmonary disease highlights the need for rapid diagnostic methods to guide timely treatment and prevent antibiotic misuse. While bacterial culture remains the gold standard for diagnosis, its extended turnaround time compromises clinical decision-making. Computed tomography (CT), with its high sensitivity for lung lesions and rapid imaging capabilities, has emerged as a critical diagnostic tool. AI-assisted CT interpretation shows particular promise for improving NTM detection, yet progress has been hindered by limited datasets due to disease rarity. We address this gap by introducing the first comprehensive CT dataset combining 430 NTM and 871 tuberculosis cases, supplemented with clinical parameters including demographics, symptoms, and mycobacterial species data. This resource aims to catalyze AI algorithm development for differential diagnosis, ultimately enhancing precision in NTM management through advanced machine learning applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.