BibTex format
@article{Kelshiker:2026:10.1016/s0140-6736(25)02156-7,
author = {Kelshiker, MA and Bächtiger, P and Petri, CF and Nakhare, S and Mansell, J and Chhatwal, K and Alrumayh, A and Zaman, J and Shah, M and Young, H and Roy, H and Almonte, MT and Costelloe, C and Razak, Y and Majeed, A and Howard, JP and Barton, C and Kramer, DB and Plymen, CM and Peters, NS},
doi = {10.1016/s0140-6736(25)02156-7},
journal = {The Lancet},
pages = {704--715},
title = {Triple cardiovascular disease detection with an artificial intelligence-enabled stethoscope (TRICORDER) in the UK: a cluster-randomised controlled implementation trial},
url = {http://dx.doi.org/10.1016/s0140-6736(25)02156-7},
volume = {407},
year = {2026}
}
RIS format (EndNote, RefMan)
TY - JOUR
AB - BackgroundEarly detection of cardiovascular disease is a global public health priority. Artificial intelligence (AI)-enabled stethoscopes offer robust performance characteristics in point-of-care detection of heart failure, atrial fibrillation, and valvular heart disease (VHD). We conducted a pragmatic, cluster-randomised controlled implementation trial to determine the real-world effect and implementation challenges of AI-stethoscopes.MethodsUK primary care practices were cluster randomised 1:1 to intervention (training and implementation in use of AI-stethoscopes in routine care) or control (routine care). Given the nature of the intervention, masking of participants (practices, clinicians, and patients) was not feasible. During cardiac examinations, the AI stethoscope recorded 15 s of single-lead electrocardiogram and phonocardiogram signals for input to three AI algorithms that returned binary predictions for the presence or absence of reduced left ventricular ejection fraction (≤40%), atrial fibrillation, and VHD (all with regulatory approval). The primary endpoint was incidence of any newly coded diagnosis of heart failure (all subtypes), expressed per 1000 patient-years (incidence rate ratio [IRR]), derived from a UK National Health Service Secure Data Environment. A coprimary endpoint stratified detection of heart failure by place of diagnosis (community-based vs via hospital admission). Secondary endpoints included atrial fibrillation and VHD detection rates, performance characteristics of the AI-stethoscope, use rates, and clinician-reported implementation barriers and enablers.FindingsBetween Oct 30, 2023, and May 22, 2024, 205 practices were randomly assigned (96 to the intervention arm [701 933 registered patients] and 109 to the control arm [851 242 registered patients]). Intervention practices recorded 12 725 patient examinations with the AI-stethoscope, across 972 clinical users. Intention-to-treat analysis found heart failure
AU - Kelshiker,MA
AU - Bächtiger,P
AU - Petri,CF
AU - Nakhare,S
AU - Mansell,J
AU - Chhatwal,K
AU - Alrumayh,A
AU - Zaman,J
AU - Shah,M
AU - Young,H
AU - Roy,H
AU - Almonte,MT
AU - Costelloe,C
AU - Razak,Y
AU - Majeed,A
AU - Howard,JP
AU - Barton,C
AU - Kramer,DB
AU - Plymen,CM
AU - Peters,NS
DO - 10.1016/s0140-6736(25)02156-7
EP - 715
PY - 2026///
SN - 0140-6736
SP - 704
TI - Triple cardiovascular disease detection with an artificial intelligence-enabled stethoscope (TRICORDER) in the UK: a cluster-randomised controlled implementation trial
T2 - The Lancet
UR - http://dx.doi.org/10.1016/s0140-6736(25)02156-7
UR - https://doi.org/10.1016/s0140-6736(25)02156-7
VL - 407
ER -