{"id":"W4200168405","doi":"10.3390/diagnostics11122367","title":"COVLIAS 1.0 vs. MedSeg: Artificial Intelligence-Based Comparative Study for Automated COVID-19 Computed Tomography Lung Segmentation in Italian and Croatian Cohorts","year":2021,"lang":"en","type":"article","venue":"Diagnostics","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Artificial intelligence; Lung; Receiver operating characteristic; Coronavirus disease 2019 (COVID-19); Segmentation; Computed tomography; Nuclear medicine; Computer science; Medicine; Cartography; Radiology; Machine learning; Internal medicine; Geography","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":[0.005432299,0.0009143305,0.000566806,0.001797332,0.0002992943,0.001162974,0.0009577587,0.001106804,0.001422413],"category_scores_gemma":[0.00865269,0.0002928983,0.0007225557,0.0006157626,0.0006686826,0.0006858433,0.001097293,0.0006520533,0.0006196682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008350521,"about_ca_system_score_gemma":0.0009951532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006125596,"about_ca_topic_score_gemma":0.009131093,"domain_scores_codex":[0.9985411,0.0005837714,0.00008363085,0.0004958772,0.0001918147,0.00010378],"domain_scores_gemma":[0.9969186,0.001474028,0.0003173601,0.0005408716,0.0003865974,0.0003626854],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01685013,0.001255628,0.7386931,0.0008093501,0.002958333,0.0004188123,0.0009173937,0.03082769,0.009973425,0.001064881,0.01599243,0.1802388],"study_design_scores_gemma":[0.0009322108,0.008067424,0.7636023,0.0002218543,0.001218946,0.001648437,0.001002403,0.2041989,0.01061316,0.001006198,0.007315573,0.000172687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9878961,0.001031334,0.004974739,0.0001998377,0.0001211365,0.0001886746,0.003006785,0.001112333,0.001469014],"genre_scores_gemma":[0.9800048,0.0002194195,0.009968165,0.0001077775,0.00007457972,0.0001333746,0.008676066,0.0001465602,0.0006693055],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006125596,"threshold_uncertainty_score":0.02872908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07684026537123442,"score_gpt":0.3984883201216278,"score_spread":0.3216480547503934,"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."}}