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

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.

Digital stethoscopes compared to standard auscultation for detecting abnormal paediatric breath sounds.

Our study aimed to objectively describe the audiological characteristics of wheeze and crackles in children by using digital stethoscope (DS) auscultation, as well as assess concordance between standard auscultation and two different DS devices in their ability to detect pathological breath sounds. Twenty children were auscultated by a paediatric consultant doctor and digitally recorded using the Littman™ 3200 Digital Electronic Stethoscope and a Clinicloud™ DS with smart device. Using spectrographic analysis, we found those with clinically described wheeze had prominent periodic waveform segments spanning expiration for a period of 0.03-1.2 s at frequencies of 100-1050 Hz, and occasionally spanning shorter inspiratory segments; paediatric crackles were brief discontinuous sounds with a distinguishing waveform. There was moderate concordance with respect to wheeze detection between digital and standard binaural stethoscopes, and 100% concordance for crackle detection. Importantly, DS devices were more sensitive than clinician auscultation in detecting wheeze in our study. CONCLUSION: Objective definition of audio characteristics of abnormal paediatric breath sounds was achieved using DS technology. We demonstrated superiority of our DS method compared to traditional auscultation for detection of wheeze. What is Known: • The audiological characteristics of abnormal breath sounds have been well-described in adult populations but not in children. • Inter-observer agreement for detection of pathological breath sounds using standard auscultation has been shown to be poor, but the clinical value of now easily available digital stethoscopes has not been sufficiently examined. What is New: • Digital stethoscopes can objectively define the nature of pathological breath sounds such as wheeze and crackles in children. • Paediatric wheeze was better detected by digital stethoscopes than by standard auscultation performed by an expert paediatric clinician.