ArticleScientific reports2023
Development and clinical validation of Swaasa AI platform for screening and prioritization of pulmonary TB.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. It is linked to trial NCT05989802 (Rapid Research in Diagnostics Development for Tuberculosis Network), which is not on this map. Cited by 21 papers, 3 of them syntheses that pooled it.
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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.
Rapid Research in Diagnostics Development for Tuberculosis Network (R2D2 Kids) and Assessing Diagnostics At Point-of-care for Tuberculosis in Children (ADAPT for Kids)
Who cites it
21 citing papers in PubMed, 3 syntheses or guidelines pooled it, 25 citations in OpenAlex.
- Cough biomarkers for diagnosis and monitoring of respiratory disease: a systematic review.European respiratory review : an official journal of the European Respiratory Society · 2026Pooled it
- Diagnostic accuracy of artificial intelligence for tuberculosis detection from cough sounds: a systematic review and meta-analysis.Frontiers in artificial intelligence · 2026Pooled it
- Audio-based digital biomarkers in diagnosing and managing respiratory diseases: a systematic review and bibliometric analysis.European respiratory review : an official journal of the European Respiratory Society · 2025Pooled it
- AI Based Cough Analysis for Pulmonary Tuberculosis Triage and Diagnosis: A Technical Review.npj biomedical innovations · 2026Review
- External validation of cough-based algorithms for pulmonary tuberculosis screening from the CODA TB DREAM challenge using cough data from Peru.Scientific reports · 2026Article
- A cough sound-based deep learning algorithm for accessible prompt detection of chronic obstructive pulmonary disease with smartphones.NPJ primary care respiratory medicine · 2026Article
- Tuberculosis Screening from Cough Audio: Baseline Models, Clinical Variables, and Uncertainty Quantification.Sensors (Basel, Switzerland) · 2026Article
- Artificial intelligence for screening drug resistance in tuberculosis.The Indian journal of medical research · 2025Article
- Mapping respiratory health digital interventions in South and Southeast Asia: a scoping review.Journal of global health · 2025Article
- A cross sectional feasibility study to evaluate the usability and efficacy of Swaasa AI platform for rapid respiratory health assessment.Scientific reports · 2025Article
- Tuberculosis Detection from Cough Recordings Using Bag-of-Words Classifiers.Sensors (Basel, Switzerland) · 2025Article
- Accelerating Cough-Based Algorithms for Pulmonary Tuberculosis Screening: Results From the CODA TB DREAM Challenge.Open forum infectious diseases · 2025Article
- Combating Antimicrobial Resistance: Role of Key Stakeholders with Focus on the Pharmaceutical Sector.Pharmaceutical medicine · 2025Review
- Gut microbiota and tuberculosis.iMeta · 2025Review
- Humanizing pulmonary care in the era of acoustic artificial intelligence: toward global health equity.Frontiers in medicine · 2025Article
- Artificial Intelligence: The new weapon to END TB.Lung India : official organ of Indian Chest Society · 2024Article
- A dataset of Solicited Cough Sound for Tuberculosis Triage Testing.Scientific data · 2024Article
- Solicited Cough Sound Analysis for Tuberculosis Triage Testing: The CODA TB DREAM Challenge Dataset.medRxiv : the preprint server for health sciences · 2024Article
- Cough Sounds in Screening and Diagnostics: A Scoping Review.The Laryngoscope · 2024Article
- Blazing the trail for innovative tuberculosis diagnostics.Infection · 2024Review
Corrections and comments
- Erratum issued
Authors and funding
14 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Acoustic signal analysis has been employed in various medical devices. However, studies involving cough sound analysis to screen the potential pulmonary tuberculosis (PTB) suspects are very few. The main objective of this cross-sectional validation study was to develop and validate the Swaasa AI platform to screen and prioritize at risk patients for PTB based on the signature cough sound as well as symptomatic information provided by the subjects. The voluntary cough sound data was collected at Andhra Medical College-India. An Algorithm based on multimodal convolutional neural network architecture and feedforward artificial neural network (tabular features) was built and validated on a total of 567 subjects, comprising 278 positive and 289 negative PTB cases. The output from these two models was combined to detect the likely presence (positive cases) of PTB. In the clinical validation phase, the AI-model was found to be 86.82% accurate in detecting the likely presence of PTB with 90.36% sensitivity and 84.67% specificity. The pilot testing of model was conducted at a peripheral health care centre, RHC Simhachalam-India on 65 presumptive PTB cases. Out of which, 15 subjects truly turned out to be PTB positive with a positive predictive value of 75%. The validation results obtained from the model are quite encouraging. This platform has the potential to fulfil the unmet need of a cost-effective PTB screening method. It works remotely, presents instantaneous results, and does not require a highly trained operator. Therefore, it could be implemented in various inaccessible, resource-poor parts of the world.
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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.