{"id":"W4404184518","doi":"10.1186/s13640-024-00656-x","title":"Deep learning-based Covid-19 diagnosis: a thorough assessment with a focus on generalization capabilities","year":2024,"lang":"en","type":"article","venue":"EURASIP Journal on Image and Video Processing","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Agence Universitaire de la Francophonie","keywords":"Coronavirus disease 2019 (COVID-19); Biometrics; Generalization; 2019-20 coronavirus outbreak; Artificial intelligence; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Focus (optics); Computer science; Machine learning; Medicine; Virology; Mathematics; Pathology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006695343,0.001988586,0.0009853138,0.001787831,0.0003266739,0.001453879,0.001562477,0.001687191,0.001507728],"category_scores_gemma":[0.01385078,0.0003113461,0.0008336306,0.000816174,0.000591749,0.001810357,0.001856991,0.001388542,0.0008708435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001231083,"about_ca_system_score_gemma":0.00118474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007207846,"about_ca_topic_score_gemma":0.005818435,"domain_scores_codex":[0.9974058,0.000944699,0.0002402154,0.0005425063,0.0006334333,0.0002334524],"domain_scores_gemma":[0.9957467,0.002125856,0.0002947996,0.0007074964,0.0009259995,0.0001991593],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001453588,0.0005472786,0.0441622,0.001098534,0.0009448365,0.0004520499,0.000214393,0.3252439,0.008886377,0.002733102,0.01356894,0.6006947],"study_design_scores_gemma":[0.0000567935,0.0005929091,0.01221072,0.0003382966,0.0002272319,0.0005637682,0.0001631259,0.9593046,0.01686882,0.004002937,0.005612143,0.00005878433],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5944002,0.02208834,0.3513962,0.005026183,0.0007891497,0.0006428324,0.005117346,0.007006403,0.01353336],"genre_scores_gemma":[0.9202564,0.003328727,0.06610754,0.0005717383,0.0001343249,0.0001506796,0.006723553,0.0002037051,0.002523443],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007207846,"threshold_uncertainty_score":0.03540879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02691316948459703,"score_gpt":0.3578263477303801,"score_spread":0.330913178245783,"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."}}