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This study demonstrates how convolutional neural networks and multimodal fusion can improve pneumonia detection in long-term care residents by leveraging digital stethoscope recordings alongside radiologist-confirmed X-rays. The research highlights that models supervised by imaging data outperform those trained on clinical diagnoses, offering a more reliable objective testing method for complex cases with atypical symptoms. Furthermore, the findings suggest that reducing the number of audio channels to three maintains high diagnostic accuracy while streamlining acquisition times, presenting a viable path for operational efficiency in healthcare settings.
Physiologically Informed Digital Auscultation for Pneumonia Detection in Long-term Care Residents
arXiv:2609.27222v1 Announce Type: cross Abstract: Pneumonia is difficult to diagnose in older long-term care residents; multimorbidity and atypical presentations obscure signs, motivating operationally efficient objective testing. We analyzed multi-channel digital stethoscope recordings from 185 Japanese residents (73 pneumonia, 112 symptomatic without), using radiologist-confirmed chest X-rays and clinician diagnoses as supervisory signals that train convolutional neural networks, multimodal fusion, and channel-based variants with time-domain Grad-CAM interpretability. Models were evaluated with repeated patient-level cross-validation showing models with X-ray supervision outperformed clinician supervision (F1 0.729, accuracy 0.783 vs. F1 0.637, accuracy 0.711). Additionally, a three-channel selection protocol maintained performance (F1 0.736; accuracy 0.803), with two mid-thoracic sites ranking highest and Grad-CAM attention overlapping adventitious sounds. These findings indicate automated multi-channel lung-sound analysis can aid long-term care pneumonia diagnosis, with X-ray supervision being more reliable than clinical, and fewer channels preserving performance while lowering acquisition times.
Abstract:Pneumonia is difficult to diagnose in older long-term care residents; multimorbidity and atypical presentations obscure signs, motivating operationally efficient objective testing. We analyzed multi-channel digital stethoscope recordings from 185 Japanese residents (73 pneumonia, 112 symptomatic without), using radiologist-confirmed chest X-rays and clinician diagnoses as supervisory signals that train convolutional neural networks, multimodal fusion, and channel-based variants with time-domain Grad-CAM interpretability. Models were evaluated with repeated patient-level cross-validation showing models with X-ray supervision outperformed clinician supervision (F1 0.729, accuracy 0.783 vs. F1 0.637, accuracy 0.711). Additionally, a three-channel selection protocol maintained performance (F1 0.736; accuracy 0.803), with two mid-thoracic sites ranking highest and Grad-CAM attention overlapping adventitious sounds. These findings indicate automated multi-channel lung-sound analysis can aid long-term care pneumonia diagnosis, with X-ray supervision being more reliable than clinical, and fewer channels preserving performance while lowering acquisition times.
Sound (cs.SD); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Audio and Speech Processing (eess.AS); Tissues and Organs (q-bio.TO)
arXiv:2609.27222 [cs.SD]
(or arXiv:2609.27222v1 [cs.SD] for this version)
https://doi.org/10.48550/arXiv.2609.27222 arXiv-issued DOI via DataCite (pending registration)
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