{"id":"W3095304929","doi":"10.3389/frai.2021.598932","title":"COVID-FACT: A Fully-Automated Capsule Network-Based Framework for Identification of COVID-19 Cases from Chest CT Scans","year":2021,"lang":"en","type":"preprint","venue":"Frontiers in Artificial Intelligence","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre; University of Toronto; McGill University Health Centre; Sunnybrook Health Science Centre; Université de Montréal; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Coronavirus disease 2019 (COVID-19); Convolutional neural network; Artificial intelligence; Thresholding; Computer science; Segmentation; Identification (biology); Pattern recognition (psychology); Gold standard (test); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Radiology; Medicine; Disease; Pathology; Infectious disease (medical specialty); Image (mathematics); Biology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001507342,0.0007097524,0.001710304,0.0006956226,0.000234264,0.0002366776,0.0007875898,0.000799006,0.0003152996],"category_scores_gemma":[0.02127005,0.0008380149,0.0005533442,0.001328104,0.0006171142,0.0001093415,0.0003252251,0.001189813,0.00001428882],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002237823,"about_ca_system_score_gemma":0.007068266,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008454605,"about_ca_topic_score_gemma":0.001927631,"domain_scores_codex":[0.993857,0.0003901315,0.002375188,0.001783254,0.0007833164,0.0008111582],"domain_scores_gemma":[0.9929955,0.002511797,0.001329625,0.001858157,0.0006068227,0.0006980747],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001653677,0.002141805,0.01650688,0.005576331,0.0006481024,0.0009039626,0.003853526,0.8822899,0.002354434,0.0007616207,0.06247982,0.0208299],"study_design_scores_gemma":[0.0004837727,0.0002853899,0.001564665,0.004612953,0.00110461,0.0000181669,0.003137573,0.8188301,0.1120709,0.0478374,0.008683614,0.001370875],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05601015,0.002735507,0.9186538,0.01200494,0.005912839,0.00286689,0.0012761,0.0005343328,0.000005425441],"genre_scores_gemma":[0.8667321,0.0003816871,0.1191041,0.009302636,0.0007705271,0.0008679651,0.002661299,0.0001474345,0.00003228275],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8107219,"threshold_uncertainty_score":0.9994071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09065713000938583,"score_gpt":0.3876020596096901,"score_spread":0.2969449296003043,"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."}}