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A research introduced at ESCMID International 2025 has demonstrated that an AI-powered lung ultrasound outperforms human consultants by 9% in diagnosing pulmonary tuberculosis (TB).
The ULTR-AI suite analyzes pictures from moveable, smartphone-connected ultrasound gadgets, providing a sputum-free, fast, and scalable different for TB detection. The outcomes exceed the World Well being Group (WHO) benchmarks for pulmonary tuberculosis analysis, marking a significant alternative for accessible and environment friendly TB triage.
Regardless of earlier world declines, TB charges rose by 4.6% from 2020 to 2023. Early screening and fast analysis are crucial elements of the WHO’s ‘Finish TB Technique,’ but many high-burden nations expertise substantial affected person dropouts on the diagnostic stage as a result of excessive price of chest X-ray tools and a scarcity of skilled radiologists.
“These challenges underscore the urgent need for more accessible diagnostic tools,” defined lead research creator, Dr. Véronique Suttels. The work is presently revealed on the preprint server SSRN.
“The ULTR-AI suite leverages deep learning algorithms to interpret lung ultrasound in real time, making the tool more accessible for TB triage, especially for minimally trained health care workers in rural areas. By reducing operator dependency and standardizing the test, this technology can help diagnose patients faster and more efficiently.”
The ULTR-AI suite contains three deep-learning fashions: ULTR-AI predicts TB straight from lung ultrasound pictures; ULTR-AI (indicators) detects ultrasound patterns as interpreted by human consultants; and ULTR-AI (max) makes use of the best threat rating from each fashions to optimize accuracy.
The research was performed at a tertiary city heart in Benin, West Africa. After exclusions, 504 sufferers have been included, with 192 (38%) confirmed to have pulmonary TB. Among the many research inhabitants, 15% have been HIV-positive and 13% had a historical past of TB.
A standardized 14-point lung ultrasound sliding scan protocol was carried out, with human consultants deciphering pictures based mostly on typical lung ultrasound findings. A single sputum molecular check (MTB Xpert Extremely) served because the reference commonplace.
ULTR-AI (max) demonstrated 93% sensitivity and 81% specificity (AUROC 0.93, 95% CI 0.92-0.95), exceeding WHO’s goal thresholds of 90% sensitivity and 70% specificity for non-sputum-based TB triage exams.
“Our model clearly detects human-recognizable lung ultrasound findings—like large consolidations and interstitial changes—but an end-to-end deep learning approach captures even subtler features beyond the human eye,” stated Dr. Suttels.
“Our hope is that this will help identify early pathological signs such as small sub-centimeter pleural lesions common in TB.”
“A key advantage of our AI models is the immediate turnaround time once they are integrated into an app,” added Dr. Suttels.
“This allows lung ultrasound to function as a true point-of-care test with good diagnostic performance at triage, providing instant results while the patient is still with the health care worker. Faster diagnosis could also improve linkage to care, reducing the risk of patients being lost to follow-up.”
Extra data:
Véronique Suttels et al, Lung Ultrasound for the Detection of Pulmonary Tuberculosis Utilizing Skilled- and AI-Guided Interpretation: A Potential Cohort Examine, SSRN (2025). DOI: 10.2139/ssrn.5174193
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European Society of Scientific Microbiology and Infectious Ailments
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AI-guided lung ultrasound marks an advance in tuberculosis analysis (2025, April 13)
retrieved 13 April 2025
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