{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005002073,0.0003332339,0.0007322937,0.0004980222,0.0002094358,0.0001795462,0.0001073556,0.0001460466,0.00006004534],"category_scores_gemma":[0.003759548,0.0003617622,0.000107762,0.001207738,0.0001934129,0.0001167637,0.00006666405,0.0002198567,0.000005455304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004053365,"about_ca_system_score_gemma":0.0009544549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003097696,"about_ca_topic_score_gemma":0.001869642,"domain_scores_codex":[0.9974334,0.000280739,0.0007589227,0.0006891667,0.000432773,0.0004050054],"domain_scores_gemma":[0.9936839,0.004840496,0.0002018806,0.0003689181,0.0004247265,0.000480117],"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":[0.0007810778,0.006917251,0.9159083,0.001071455,0.0003924304,0.001394735,0.01472157,0.02097281,0.0004690495,0.000581324,0.03499617,0.001793855],"study_design_scores_gemma":[0.007445598,0.002546375,0.4465371,0.0008638346,0.001350269,0.00003918635,0.01217643,0.4971464,0.02589656,0.0007490377,0.004124378,0.001124845],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9270567,0.0008606164,0.04359648,0.02099494,0.0006584174,0.005780732,0.0004074058,0.0006282278,0.0000164458],"genre_scores_gemma":[0.9782599,0.0000368825,0.00534198,0.01473081,0.00008894569,0.0004081604,0.00108386,0.00004024498,0.000009206763],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4761736,"threshold_uncertainty_score":0.9998834,"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."}}