2025-06-10 スイス連邦工科大学ローザンヌ校(EPFL)
Dr. Véronique Suttels performs a smartphone-connected ultrasound on a symptomatic Beninese patient – 2025 EPFL/Véronique Suttels – CC-BY-SA 4.0
<関連情報>
- https://actu.epfl.ch/news/ai-making-it-easier-to-diagnose-tuberculosis-in-su/
- https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5174193
専門家とAIによる解釈を用いた肺結核検出のための肺超音波検査: 前向きコホート研究 Lung Ultrasound for the Detection of Pulmonary Tuberculosis Using Expert- and AI-Guided Interpretation: A Prospective Cohort Study
Véronique Suttels,Trevor Brokowski,…
Social Science Research Network Posted: 18 Mar 2025
Abstract
Background: Point-of-care lung ultrasound (LUS) is a promising tool for portable sputum-free tuberculosis (TB) triage. We investigate the diagnostic performance of LUS to detect TB using expert and artificial intelligence (AI) guided interpretation. We introduce ULTR-AI (Ultrasound-led TB recognition using AI), a suite of deep learning (DL) models designed to automate TB prediction from LUS images.
Methods: In this prospective cohort study in a tertiary center in urban Benin, a standardized 14-point sliding scan LUS protocol was performed for symptomatic patients by a trained operator. LUS images were reviewed by two blinded and independent readers. Same-day single sputum Xpert MTB/RIF Ultra® was the microbiological reference standard. The suite comprises three AI models, ULTR-AI, ULTR-AI[signs] and ULTR-AI[max]. ULTR-AI predicts TB directly from images using DL, ULTR-AI[signs] first generates human-recognizable pathological signs before TB risk prediction in a machine learning (ML) model. Finally, ULTR-AI[max] takes the maximal TB risk score predicted by these two models.
Findings: Out of 760 patients screened, 504 were analyzed. Median age was 40 (IQR 30-52), 196 (39%) were female, 78 (15%) were people with HIV and 66 (13%) had previous TB. Overall, 192 (38%) had bacteriologically confirmed TB. Human expert interpretation of lung ultrasound achieved a sensitivity of 0·90 (95%CI: 0·89, 0·92), specificity of 0·61 (95%CI: 0·54, 0·67) [AUROC 0·84, 95% CI: 0.82-0.85]. AI-guided interpretation with ULTRA-AI[max] reached a sensitivity of 0·91 (95% CI, 0·90–0·96) and a specificity of 0·85 (95% CI, 0·74–0·88) [AUROC 0·93, 95% 0·92-0·95].
Interpretation: In this cohort, AI-guided LUS meets the WHO requirements for a sputum-free TB triage test, enabling point-of-care testing with minimal infrastructure. ULTR-AI could further help decentralize TB diagnostics in LMICs, improving timely detection. Validation in diverse populations is crucial to confirm clinical utility.
