Artificial intelligence accuracy in detecting pathological breath sounds in children using digital stethoscopes
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RESEARCH
Open Access
Artificial intelligence accuracy in detecting pathological breath sounds in children using digital stethoscopes Ajay Kevat1,2* , Anaath Kalirajah1 and Robert Roseby1,2
Abstract Background: Manual auscultation to detect abnormal breath sounds has poor inter-observer reliability. Digital stethoscopes with artificial intelligence (AI) could improve reliable detection of these sounds. We aimed to independently test the abilities of AI developed for this purpose. Methods: One hundred and ninety two auscultation recordings collected from children using two different digital stethoscopes (Clinicloud™ and Littman™) were each tagged as containing wheezes, crackles or neither by a pediatric respiratory physician, based on audio playback and careful spectrogram and waveform analysis, with a subset validated by a blinded second clinician. These recordings were submitted for analysis by a blinded AI algorithm (StethoMe AI) specifically trained to detect pathologic pediatric breath sounds. Results: With optimized AI detection thresholds, crackle detection positive percent agreement (PPA) was 0.95 and negative percent agreement (NPA) was 0.99 for Clinicloud recordings; for Littman-collected sounds PPA was 0.82 and NPA was 0.96. Wheeze detection PPA and NPA were 0.90 and 0.97 respectively (Clinicloud auscultation), with PPA 0.80 and NPA 0.95 for Littman recordings. Conclusions: AI can detect crackles and wheeze with a reasonably high degree of accuracy from breath sounds obtained from different digital stethoscope devices, although some device-dependent differences do exist. Keywords: Artificial intelligence, Auscultation, Child, Respiratory sounds, Stethoscopes
Background Accurately detecting abnormal breath sounds is vital in clinical pediatric medicine, as the nature and presence of pathological sounds guides diagnosis and initial treatment of common respiratory conditions. However, use of a standard binaural stethoscope by human practitioners to detect abnormal chest sounds introduces assessment subjectivity and research has shown that significant inter-listener variability exists [1–3]. This calls into question the accuracy of diagnoses made on * Correspondence: [email protected] 1 Department of Paediatrics, Monash University, Melbourne, Australia 2 Department of Respiratory Medicine, Monash Children’s Hospital, 246 Clayton Road, Clayton, Melbourne, Victoria 3168, Australia
the basis of human auscultation. Treatment decisions informed by the diagnosis made may therefore be misguided, leading to unnecessary side effects and delay in provision of effective treatment. In recent years, stethoscopes capable of digitally recording breath sounds have become more widely available, offering the ability to capture breath sounds with superior sound quality and fidelity [4]. However, human interpretation of the digital recordings can still exhibit significant inter-listener variability [5]. As the soundwave properties of pathologic breath sounds such as crackles, wheezes and rhonchi have been well-studied and
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