The LalelaLung Study: Digital Stethoscope Clinical Evaluation

Summary

Pneumonia is the leading infectious cause of death in children under five years of age worldwide, and most of these deaths occur in low- and middle-income countries. In these settings, frontline health workers diagnose pneumonia using the World Health Organization's Integrated Management of Childhood Illness (IMCI) guidelines, which rely mainly on counting how fast a child is breathing and checking for chest indrawing. This approach has saved many lives, but it is not very specific. As a result, many children who actually have self-limiting viral illnesses that do not require antibiotics are nonetheless treated with antibiotics, contributing to the global rise of antimicrobial resistance.

New digital stethoscopes paired with artificial intelligence (AI) can record a child's lung sounds and automatically detect abnormal sounds such as crackles and wheezes with accuracy comparable to physicians. The LaLeLa Lung Study will evaluate whether adding an AI-enabled digital stethoscope to standard IMCI assessment improves the accuracy of pneumonia diagnosis among children aged 2 to 59 months who present with cough and/or difficult breathing at a primary care clinic in Cape Town, South Africa.

The main component (Objective 1) is a randomized, triple-blinded diagnostic accuracy study that will enroll 350 children, randomly assigned in a 1:1 ratio to either IMCI care enhanced by the AI-enabled digital stethoscope or standard IMCI care. An independent panel of physicians, blinded to the AI results and to study-arm assignment, will review each case and serve as the reference standard for determining whether pneumonia was truly present. The investigators hypothesize that IMCI enhanced by the AI stethoscope will diagnose pneumonia more accurately, and target antibiotics more appropriately, than standard IMCI alone. Nested sub-studies will additionally evaluate a second AI stethoscope for tuberculosis detection, a wearable lung-sound and respiratory-rate patch, an automated respiratory-rate monitor, and a smartphone-connected pulse oximeter.

A separate component (Objective 2) is a mixed-methods implementation study at a second clinic that will assess how easily health workers can use these devices, how acceptable the devices are to health workers and caregivers, and how well the devices fit into routine clinic workflows.

Throughout the study, all AI-generated results will remain concealed from clinic staff, study clinicians, and caregivers, so the AI-generated results will not influence the care any child receives. All children continue to receive standard IMCI care. Findings will help inform whether AI-enabled digital auscultation should be integrated into childhood pneumonia care in South Africa and similar low-resource settings, with the goal of improving diagnosis, strengthening antibiotic stewardship, and reducing antimicrobial resistance and child mortality.

Conditions

• Pneumonia
• Tuberculosis, Pulmonary
• Respiratory Tract Infections
• Bronchiolitis
• Respiratory Sounds
• Antibiotic Resistant Strain

Interventions

DEVICE

StethoMe AI-enabled digital stethoscope system

A CE-marked (EU Class IIa) wireless electronic stethoscope paired with a mobile application and an on-device deep convolutional recurrent neural network trained on more than 25,000 labeled lung-sound recordings. The device captures high-fidelity respiratory sounds, automatically computes respiratory rate, and classifies sounds in real time as normal or abnormal (fine/coarse crackles, high-/low-pitched wheezes), with ambient-noise detection to flag low-quality signals. Recordings are obtained at four standardized chest positions; the algorithm's classifications are generated automatically but the output display is permanently disabled for field users so results stay concealed and do not inform care.

DIAGNOSTIC_TEST

Standard IMCI assessment

The World Health Organization's standardized clinical algorithm for children with cough and/or difficult breathing, in which pneumonia is classified on the basis of age-specific fast breathing and/or chest indrawing in the absence of general danger signs, without digital or AI-assisted auscultation. Conducted by routine clinic health workers using standard equipment, it represents the current WHO-recommended standard of care for outpatient pneumonia assessment.

Sponsors & Collaborators

• University of Stellenbosch

  collaborator OTHER

• Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)

  collaborator NIH

• Johns Hopkins University

  lead OTHER

Principal Investigators

• Eric McCollum, MD, MPH · Johns Hopkins University

Study Design

Allocation

RANDOMIZED

Purpose

DIAGNOSTIC

Masking

QUADRUPLE

Model

PARALLEL

Eligibility

Min Age

2 Months

Max Age

59 Months

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2026-08-17

Primary Completion

2028-01-31

Completion

2028-06-30