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. 2020 Sep 29;21(1):253.
doi: 10.1186/s12931-020-01523-9.

Artificial intelligence accuracy in detecting pathological breath sounds in children using digital stethoscopes

Ajay Kevat  1   2 , Anaath Kalirajah  3 , Robert Roseby  3   4
Affiliations

Artificial intelligence accuracy in detecting pathological breath sounds in children using digital stethoscopes

Ajay Kevat et al. Respir Res. .

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.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Participant recruitment and recording analysis flowchart
Fig. 2
Fig. 2
Receiver Operating Characteristic (ROC) Curve: AI performance in detecting. Clinicloud-recorded wheeze
Fig. 3
Fig. 3
Receiver Operating Characteristic (ROC) Curve: AI performance in detecting. Clinicloud-recorded crackles
Fig. 4
Fig. 4
Receiver Operating Characteristic (ROC) Curve: AI performance in detecting. Littman-recorded wheeze
Fig. 5
Fig. 5
Receiver Operating Characteristic (ROC) Curve: AI performance in detecting. Littman-recorded crackles
See this image and copyright information in PMC

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