The LalelaLung Study: Digital Stethoscope Clinical Evaluation

Sponsor
Johns Hopkins University
Study ID
NCT07631377
Phase
PHASE4
Status
Not Yet Recruiting

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Conditions

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

Eligibility Criteria

Sex
ALL
Age
2 Months - 59 Months
Healthy Volunteers
Not accepted

Interventions

  • StethoMe AI-enabled digital stethoscope system — DEVICE
    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.
  • Standard IMCI assessment — DIAGNOSTIC_TEST
    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.

Study Details

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.

Key Dates

First listed
Jun 8, 2026
Start date
Aug 17, 2026
Status verified
Jul 2026
Primary completion
Jan 31, 2028
Completion
Jun 30, 2028

Study Design

Enrollment
350 participants (estimated)
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
DIAGNOSTIC

Arms

  • Experimental: AI-enhanced IMCI
    Participants randomized to this arm undergo Integrated Management of Childhood Illness (IMCI) assessment enhanced by the StethoMe AI-enabled digital stethoscope. After the routine health worker IMCI evaluation, a study clinician performs a structured IMCI-based respiratory assessment and obtains digital lung-sound recordings at four standardized chest positions with the StethoMe device. The embedded algorithm computes respiratory rate and classifies abnormal lung sounds (crackles, wheezes) in real time. AI outputs remain concealed from health workers, study staff, and caregivers and do not influence clinical management. The intervention is the StethoMe AI-enabled digital stethoscope, which is applied during a single clinic encounter.
  • Active Comparator: Standard IMCI
    Participants randomized to this arm receive standard IMCI assessment per WHO guidelines, in which pneumonia is classified using respiratory rate and chest indrawing without AI-enabled digital auscultation. Routine clinic health workers perform the IMCI evaluation and make all management decisions, including antibiotic prescription or referral. The intervention is the standard IMCI assessment, which is delivered during a single clinic encounter.

Primary Outcome Measure

Proportion of children correctly classified with Pneumonia (Diagnostic accuracy of pneumonia diagnosis - IMCI enhanced by AI-enabled digital stethoscope vs. standard IMCI) [ Time Frame: Index clinic visit (Day 1); 7-day follow-up ]

Central Contacts

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