{"id":"W4390971323","doi":"10.1109/bibm58861.2023.10385917","title":"Deep learning boosted amyloidosis diagnosis","year":2023,"lang":"en","type":"article","venue":"","topic":"Amyloidosis: Diagnosis, Treatment, Outcomes","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Benchmark (surveying); Amyloidosis; Computer science; Artificial intelligence; AL amyloidosis; Identification (biology); Code (set theory); Amyloid fibril; Deep learning; Immunoglobulin light chain; Amyloid (mycology); Machine learning; Pattern recognition (psychology); Pathology; Medicine; Amyloid β; Disease; Antibody; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001219513,0.001091048,0.001248725,0.000795992,0.0003304972,0.0009388363,0.00142997,0.001358439,0.002535874],"category_scores_gemma":[0.002983355,0.0003577123,0.0007579615,0.0004911515,0.0003300319,0.0009378189,0.001219193,0.001790655,0.001161936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008515177,"about_ca_system_score_gemma":0.001296923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005377606,"about_ca_topic_score_gemma":0.007464204,"domain_scores_codex":[0.9994331,0.000118167,0.00004042995,0.000159138,0.0001433747,0.0001058215],"domain_scores_gemma":[0.9991552,0.0003638122,0.00007163447,0.00007873752,0.0002538045,0.00007691709],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001127546,0.0007458338,0.01869884,0.0004056407,0.0003269406,0.0005292427,0.00007673495,0.2813165,0.008031867,0.003806601,0.04247735,0.6424569],"study_design_scores_gemma":[0.00004838053,0.0000813748,0.0006316171,0.00002558648,0.00003478623,0.0001089678,0.00001141941,0.9899173,0.003506204,0.003848447,0.00177467,0.00001119178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2513591,0.01376153,0.6935707,0.00516613,0.0009937001,0.0003077533,0.005510007,0.01616279,0.01316835],"genre_scores_gemma":[0.8857917,0.001224219,0.09952884,0.001241301,0.0002801013,0.0001081407,0.004809975,0.0001242375,0.006891391],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005377606,"threshold_uncertainty_score":0.01069266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01366628319830981,"score_gpt":0.2679548454248664,"score_spread":0.2542885622265566,"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."}}