{"id":"W4392455101","doi":"10.4103/jfmpc.jfmpc_695_23","title":"Segmentation and classification of lungs CT-scan for detecting COVID-19 abnormalities by deep learning technique: U-Net model","year":2024,"lang":"en","type":"article","venue":"Journal of Family Medicine and Primary Care","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Artificial intelligence; Medicine; Convolutional neural network; Segmentation; Receiver operating characteristic; Deep learning; Coronavirus disease 2019 (COVID-19); Computed tomography; Pattern recognition (psychology); Machine learning; Nuclear medicine; Radiology; Computer science; Pathology","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":[],"consensus_categories":[],"category_scores_codex":[0.000889816,0.0001408266,0.0004168461,0.0003425937,0.00009971178,0.00002220458,0.00005043952,0.0000656756,0.000005203199],"category_scores_gemma":[0.0005106352,0.0001111943,0.00006842272,0.000164316,0.0001259744,0.0001864827,0.00002177665,0.0003173173,7.732056e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003868584,"about_ca_system_score_gemma":0.0004570118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000735107,"about_ca_topic_score_gemma":0.000004719408,"domain_scores_codex":[0.9987489,0.00005435327,0.0005482303,0.0001753996,0.0003390943,0.0001340252],"domain_scores_gemma":[0.9983954,0.0007557134,0.0002991535,0.000085518,0.0002802361,0.0001839186],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007007054,0.00006256074,0.009046131,0.01819093,0.0002472243,0.0000812947,0.02034534,0.0006114956,0.7159711,0.00007254535,0.04274276,0.191928],"study_design_scores_gemma":[0.03002632,0.0247924,0.04114642,0.03191414,0.009522447,0.005721972,0.2270923,0.1121868,0.07673343,0.002223076,0.4366747,0.001965986],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7308357,0.09543424,0.1509867,0.02059346,0.0003273457,0.001107529,0.00004673139,0.00009837378,0.0005698929],"genre_scores_gemma":[0.9828424,0.004793062,0.004100974,0.007769628,0.0002508867,0.00003024376,0.00008411209,0.00003175688,0.00009688627],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6392376,"threshold_uncertainty_score":0.4534373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05859981152108323,"score_gpt":0.3653555387592535,"score_spread":0.3067557272381702,"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."}}