{"id":"W4404419387","doi":"10.18280/ts.410521","title":"Bridging Auscultation and Tiny Machine Learning: A Digital Stethoscope Leveraging Convolutional Neural Networks on an Embedded Device for Organ Sound Analysis","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Phonocardiography and Auscultation Techniques","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Türkiye Bilimsel ve Teknolojik Araştırma Kurumu","keywords":"Stethoscope; Bridging (networking); Auscultation; Convolutional neural network; Computer science; Sound (geography); Speech recognition; Acoustics; Artificial intelligence; Medicine; Cardiology; Computer network; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002388758,0.0003255854,0.0002318137,0.0002524925,0.00009468402,0.000256485,0.0004993225,0.0005018564,0.001182003],"category_scores_gemma":[0.0005923237,0.0001237247,0.0001439345,0.0001212977,0.000176292,0.0004052109,0.0004267138,0.0003356412,0.0003214513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001939206,"about_ca_system_score_gemma":0.0002337537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001240888,"about_ca_topic_score_gemma":0.002372202,"domain_scores_codex":[0.9998652,0.000019189,0.000008315977,0.0000313169,0.00006013433,0.00001571958],"domain_scores_gemma":[0.9998523,0.00005101337,0.00002084464,0.00002361996,0.0000372592,0.00001486244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009216858,0.0002779527,0.007841236,0.000326488,0.000121428,0.001093832,0.0001646391,0.04141825,0.4291295,0.00241568,0.004660564,0.5116287],"study_design_scores_gemma":[0.00003379543,0.0006732774,0.007221835,0.00004763652,0.00005831797,0.001070421,0.0000315988,0.8366773,0.1463614,0.0008474903,0.006926729,0.00005022857],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4448358,0.001252748,0.5388314,0.0005607863,0.0004047147,0.000154848,0.00024319,0.006540285,0.00717633],"genre_scores_gemma":[0.9006844,0.0003031524,0.09341621,0.0002904166,0.00004302055,0.00005608263,0.000258734,0.00005599867,0.004892009],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001240888,"threshold_uncertainty_score":0.003954172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02451495757731436,"score_gpt":0.2974108504379216,"score_spread":0.2728958928606073,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}