The LalelaLung Study: Digital Stethoscope Clinical Evaluation
试验速览
- 阶段
- 4 期
- 状态
- 尚未招募
- 发起方
- 入组人数
- 350
研究概览
简要总结
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.
详细描述
Background and Rationale The World Health Organization Integrated Management of Childhood Illness (IMCI) algorithm classifies pneumonia in children with cough and/or difficult breathing primarily on the basis of elevated respiratory rate and chest indrawing. Lung auscultation was historically excluded from IMCI because of its poor reproducibility among non-physician health workers. Since IMCI's introduction, the rollout of Haemophilus influenzae type b and pneumococcal conjugate vaccines has shifted the etiology of childhood lower respiratory infection toward viral pathogens, and placebo-controlled trials indicate that most IMCI-defined non-severe pneumonia is self-limiting. Reliance on respiratory rate alone yields low specificity, driving substantial antibiotic overuse and antimicrobial resistance. AI-enabled digital stethoscopes can reintroduce standardized, objective auscultation by automatically classifying crackles and wheezes with accuracy comparable to expert physicians. The StethoMe device, a CE-marked (EU Class IIa) system using a deep convolutional recurrent neural network trained on more than 25,000 labeled lung-sound recordings, has demonstrated 85-90% agreement with physician reference panels in prior validation and pilot work conducted by the study consortium across multiple low- and middle-income settings.
Overall Study Design The LaLeLa Lung Study comprises two objectives conducted at two primary healthcare facilities in Cape Town, South Africa. Objective 1 is a randomized, triple-blinded, individually allocated diagnostic accuracy study (with nested device-validation sub-studies) evaluating whether IMCI enhanced by an AI-enabled digital stethoscope improves pneumonia diagnostic accuracy and antibiotic targeting relative to standard IMCI. Objective 2 is a mixed-methods, concurrent-triangulation implementation study evaluating usability, acceptability, and fidelity of the digital devices in routine care. The study will enroll a total of approximately 380 participants (350 in Objective 1; up to 30 health workers and caregivers in Objective 2).
Objective 1: Diagnostic Accuracy Study
Objective 1 enrolls 350 children at Site B Clinic, Khayelitsha, randomized 1:1 to IMCI enhanced by the StethoMe AI-enabled digital stethoscope or to standard IMCI care. A computer-generated randomization sequence prepared in advance by the study statistician and implemented through REDCap is used, with stratification by age group (<1 year and >=1 year) and allocation concealment from enrollment staff. The design is triple-blinded. Caregivers/participants, routine health workers performing IMCI assessments, and study clinicians performing the digital recordings are all blinded to the device's real-time AI classifications, which are permanently disabled on the device interface for field users. The independent physician reference panel is blinded to study arm, AI outputs, and participant identifiers. Only the statistician holds the allocation key. Importantly, AI outputs do not inform clinical care in either arm, and all participants receive identical study procedures and full IMCI-standard care.
After informed consent and screening, each child is first assessed by a routine clinic health worker who documents IMCI findings and management (including antibiotic prescription or referral) on a study case-management form, without access to the digital stethoscope or study-arm allocation. The child then undergoes an independent structured IMCI-based respiratory assessment by a study clinician, who obtains StethoMe lung-sound recordings at four standardized chest positions. The embedded algorithm computes respiratory rate and classifies abnormal sounds in real time, but all outputs remain concealed. Pulse oximetry (Masimo Rad-G or equivalent), lung ultrasound (Butterfly iQ+), and chest radiography are also obtained. Imaging may be shared with the health worker on request but only after the initial treatment decision and is stored in the regional system using study identifiers. Each enrolled child completes a single in-person encounter (anticipated 60 minutes, integrated into routine clinic flow) followed by a telephone outcome assessment at day 7.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Diagnostic
- 盲法
- Quadruple (Participant, Care Provider, Investigator, Outcomes Assessor)
盲法说明
The trial is masked both to randomization-arm allocation and to the digital stethoscope's real-time AI classification outputs. Caregivers/participants and the routine clinic health care workers who make all clinical management decisions are masked to study-arm allocation. Study clinicians and research staff performing the digital recordings and assessments are masked to the device's AI outputs, which are permanently disabled in the field interface, and the independent physician reference panel that adjudicates the reference diagnosis is masked to study arm, AI outputs, and participant identifiers. Only the study statistician, who holds the randomization schedule, has access to allocation. Blinding integrity is maintained by using separate personnel for enrollment, assessment, and follow-up and by a weekly blinding-compliance checklist verified by the principal investigator.
入排标准
- 年龄范围
- 2 Months 至 59 Months（Child）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Age 2 to 59 months at the time of screening
- •Presence of cough and/or difficulty breathing
- •No WHO-defined emergency/danger signs (e.g., grunting, cyanosis, apnea, convulsions, or altered level of consciousness)
- •A legal caregiver is present, able to understand the study information, and willing to provide written informed consent
- •Caregiver is willing and able to provide contact information (e.g., mobile phone number) to allow 7-day follow-up after the clinic visit
排除标准
- •Presence of WHO-defined emergency signs requiring immediate referral or hospital admission (grunting, cyanosis, apnea, uncompensated shock, convulsions, diarrhea with severe dehydration, or altered level of consciousness)
- •Critical illness or clinical instability judged by the screening clinician or study physician to require urgent medical attention
- •Age outside the target range (younger than 2 months or older than 59 months)
- •Previous enrollment in the study
- •Refusal or withdrawal of informed consent by the legal caregiver at any time prior to randomization
研究组 & 干预措施
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.
干预措施: Standard IMCI assessment (Diagnostic Test)
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.
干预措施: StethoMe AI-enabled digital stethoscope system (Device)
结局指标
主要结局
未指定
次要结局
- Accuracy of pneumonia diagnosis (Expanded lung-sound classification accuracy)(Day 1; 7-day follow-up)