{"id":"W4220841755","doi":"10.1371/journal.pone.0265752","title":"TimTrack: A drift-free algorithm for estimating geometric muscle features from ultrasound images","year":2022,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fascicle; Algorithm; Ultrasound; Ultrasound imaging; Computer science; Mathematics; Artificial intelligence; Anatomy; Computer vision; Physics; Medicine; Acoustics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002385955,0.001372786,0.0009970095,0.002520738,0.0007512359,0.001397263,0.002077085,0.001822978,0.006519426],"category_scores_gemma":[0.006043889,0.0007351601,0.000981898,0.001871301,0.0005943236,0.001760622,0.001671873,0.001373902,0.004194635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006348672,"about_ca_system_score_gemma":0.001548659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003262622,"about_ca_topic_score_gemma":0.005002109,"domain_scores_codex":[0.9991246,0.0001079012,0.00007691782,0.0002473899,0.0003875356,0.00005558844],"domain_scores_gemma":[0.9986157,0.0005316315,0.0002231997,0.0001642224,0.0004061497,0.00005901228],"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":[0.0005218354,0.00008378552,0.002321737,0.0002823735,0.0002061461,0.0001426159,0.0001934494,0.01959674,0.04134376,0.003587413,0.01204059,0.9196795],"study_design_scores_gemma":[0.0001535538,0.0002672369,0.005747414,0.00006315785,0.0001002984,0.0006595142,0.00008157877,0.9110515,0.05075062,0.005294546,0.02569398,0.0001365752],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003664416,0.0001480441,0.9919321,0.00003375766,0.00004013076,0.00005146948,0.0001527836,0.003630721,0.0003466345],"genre_scores_gemma":[0.02375947,0.0001749378,0.971711,0.00006243146,0.00004279021,0.0002564046,0.0006685662,0.000794346,0.002530074],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006519426,"threshold_uncertainty_score":0.0218097,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01778248628058869,"score_gpt":0.2067543987327873,"score_spread":0.1889719124521986,"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."}}