# Lung Ultrasound and Artificial Intelligence Technology for the Diagnosis of TB in LMICs

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2024 · $675,306

## Abstract

Project Summary
Improved point-of-care tests and diagnostic algorithms for tuberculosis (TB) are urgently needed to enable more
timely and accurate diagnosis. Currently, lack of diagnosis and diagnostic delays are significant contributors to
increased mortality [1,2,3] in low and middle-income countries (LMICs), due to reliance on insensitive, slow
and/or locally inappropriate tests and limited access to optimal diagnostic modalities. Fortunately, rapid advances
in diagnostic imaging technology have produced affordable, portable, point-of-care ultrasound devices that can
be transported with exceptional ease to resource-limited settings. Lung ultrasound (LUS) is now regularly used
to accurately diagnose a variety of pulmonary disorders including pneumonia and pulmonary edema. Our
preliminary studies have demonstrated 96% sensitivity of LUS for detecting associated sonographic
abnormalities in patients with microbiologically confirmed pulmonary tuberculosis (PTB).5 However, there remain
barriers to implementing LUS for TB diagnosis, including a scarcity of robust data about ideal training and
scanning procedures. Our overall goal is implementation of real-time AI-facilitated LUS for timely evaluation of
people with TB-suspected symptoms to triage who needs further evaluation and testing. Our preliminary data
suggest LUS may be highly sensitive for the diagnosis of PTB, but no rigorous, adequately powered studies
have investigated lung ultrasound findings in patients with PTB versus controls without PTB. To address these
critical information gaps, we aim to:
Aim 1. Develop a LUS model for PTB detection as a triage tool for PTB diagnosis which can be used in
field settings in low-resource and remote areas. Hypothesis: LUS will have similar or better sensitivity for
diagnosis of PTB compared to CXR with moderate (i.e., 70-80%) specificity when interpreted by trained
personnel and validated by experts.
Aim 2. Develop and test an artificial intelligence (AI) algorithm for detecting PTB by LUS that does not
require trained personnel for use in LMICs. Hypothesis: An AI algorithm based on convolutional neural
networks (CNNs) will classify LUS features indicative of PTB with high sensitivity (90)% vs. the reference
standard of microbiological testing.
This study will leverage resources and expertise among partners in the United States and Peru. Our
multidisciplinary research team at the Universidad Peruana Cayetano Heredia (UPCH), the Peruvian NGO A.B.
PRISMA, and the Johns Hopkins University have a strong track record of collaborative work in novel research
projects in resource-poor settings, including TB and LUS. The use of portable AI-augmented LUS could save
lives in resource-limited settings by decreasing time to case detection and treatment initiation.

## Key facts

- **NIH application ID:** 10803802
- **Project number:** 1R01HL168261-01A1
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Paul Blair
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $675,306
- **Award type:** 1
- **Project period:** 2024-02-15 → 2029-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10803802

## Citation

> US National Institutes of Health, RePORTER application 10803802, Lung Ultrasound and Artificial Intelligence Technology for the Diagnosis of TB in LMICs (1R01HL168261-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10803802. Licensed CC0.

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