{"id":"W4387717463","doi":"10.1109/access.2023.3325346","title":"Toward Better Ear Disease Diagnosis: A Multi-Modal Multi-Fusion Model Using Endoscopic Images of the Tympanic Membrane and Pure-Tone Audiometry","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Ear Surgery and Otitis Media","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Ministry of Science and ICT, South Korea; Iran Telecommunication Research Center; National Research Foundation of Korea; Institute for Information and Communications Technology Promotion; Korea University; Hanyang University; National Research Foundation","keywords":"Computer science; Convolutional neural network; Tympanic Membrane Perforation; Artificial intelligence; Feature (linguistics); Sensorineural hearing loss; Deep learning; Cholesteatoma; Speech recognition; Hearing loss; Pattern recognition (psychology); Audiology; Eardrum; Medicine; Surgery; Radiology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000213955,0.0001863148,0.0003674869,0.0002477792,0.0001066627,0.00004139299,0.000226782,0.0000929835,0.00005112766],"category_scores_gemma":[0.000278282,0.0001323659,0.000130923,0.0006169043,0.0001605157,0.0002832142,0.0002150758,0.00021852,0.00001339164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002908333,"about_ca_system_score_gemma":0.0001093221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001130556,"about_ca_topic_score_gemma":0.00001809876,"domain_scores_codex":[0.9986324,0.00007321292,0.0002895271,0.0003260312,0.0003643102,0.0003145388],"domain_scores_gemma":[0.9990566,0.000155933,0.0001177751,0.0003861642,0.00007163084,0.0002119334],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008296254,0.0002632108,0.915675,0.000715339,0.00007377395,0.0001635865,0.0004002958,0.0009756951,0.07912819,0.000001924982,0.0005425679,0.001977486],"study_design_scores_gemma":[0.002041676,0.0000240519,0.8393281,0.0007845924,0.000256828,0.000008787781,0.00005370561,0.06218482,0.09508157,0.00004156332,0.00001982622,0.000174455],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962702,0.0007101706,0.0007290201,0.0009547568,0.0007255625,0.0004477345,0.00007317305,0.00007589834,0.00001345632],"genre_scores_gemma":[0.9975589,0.0006495976,0.0006839423,0.0005890135,0.0001683502,0.00004831687,0.00001165893,0.0000384259,0.0002518276],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07634684,"threshold_uncertainty_score":0.5397723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09290669436472956,"score_gpt":0.3626845216894148,"score_spread":0.2697778273246852,"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."}}