Abstract
BackgroundManual 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.MethodsOne 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.ResultsWith 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.ConclusionsAI 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.
Highlights
Manual auscultation to detect abnormal breath sounds has poor inter-observer reliability
Four recordings obtained by each digital stethoscope (DS) were excluded due to aberrant recording, occurring due to inadequate chest wall contact made with the stethoscope head at the time of the recording and/or cable connection failure
The Cohen’s kappa assessing inter-rater agreement for scoring/tagging of the subset of recordings analyzed by the blinded second clinician was 1.0
Summary
Manual auscultation to detect abnormal breath sounds has poor inter-observer reliability. 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. 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 [1,2,3]. This calls into question the accuracy of diagnoses made on. 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 previously defined, computer algorithms and programs to automatically detect them have been developed [6, 7].
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