Similarly, an AI algorithm designed to detect abnormal pediatric breath sounds based upon several thousand patient recordings collected using a digital stethoscope (DS) was reported to outperform pediatricians, especially in coarse crackle detection. ![]() ![]() AI programs based upon neural network programming have been used to identify melanomas from photographs of skin and suspicious soft tissue / calcified lesions on routine mammograms with an accuracy greater than most dermatologists and radiologists respectively, who were interpreting those images for performance comparison. Increasingly, artificial intelligence (AI) algorithms have been applied in medicine, and because they have the capability of self-improvement as they learn from new data and cases, they can evolve to outperform traditional signal processing techniques. 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. However, human interpretation of the digital recordings can still exhibit significant inter-listener variability. 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. Treatment decisions informed by the diagnosis made may therefore be misguided, leading to unnecessary side effects and delay in provision of effective treatment. ![]() This calls into question the accuracy of diagnoses made on the basis of human auscultation. 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. ConclusionsĪI 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.Īccurately 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. 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. 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. These recordings were submitted for analysis by a blinded AI algorithm (StethoMe AI) specifically trained to detect pathologic pediatric breath sounds. 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. We aimed to independently test the abilities of AI developed for this purpose. Digital stethoscopes with artificial intelligence (AI) could improve reliable detection of these sounds. ![]() Manual auscultation to detect abnormal breath sounds has poor inter-observer reliability.
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