{
  "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_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.27222",
  "author_total": 8,
  "authors": [
    {
      "name": "Nicholas Rasmussen"
    },
    {
      "name": "Oleg Zaslavsky"
    },
    {
      "name": "Zih-Ling Wang"
    },
    {
      "name": "Hongyu Yu"
    },
    {
      "name": "Joelle Fathi"
    },
    {
      "name": "Kaibao Nie"
    },
    {
      "name": "Amil Khanzada"
    },
    {
      "name": "Tomoko Ito"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8xwbe8skxnbkz.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.27222",
  "fulltext_url": null,
  "id": "8xwbe8skxnbkz",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.27222",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.CV"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "cs.SD"
    ],
    [
      "arxiv",
      "eess.AS"
    ],
    [
      "arxiv",
      "q-bio.TO"
    ],
    [
      "openalex",
      "2740|Phonocardiography and Auscultation Techniques"
    ]
  ],
  "openalex": "W7214215107",
  "pdf_url": "https://arxiv.org/pdf/2609.27222",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-23",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8xwbe8skxnbkz.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:01Z",
      "native_id": "2609.27222",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.27222"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "arxiv:q-bio.TO",
      "subfield": "physiology"
    }
  ],
  "title": "Physiologically Informed Digital Auscultation for Pneumonia Detection in Long-term Care Residents",
  "title_text": "Physiologically Informed Digital Auscultation for Pneumonia Detection in Long-term Care Residents",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:01Z",
  "url": "https://research.draconitic.com/paper/8xwbe8skxnbkz",
  "venue": "arXiv",
  "versions": []
}