{"id":"W1973912504","doi":"10.1016/j.compbiomed.2014.06.006","title":"Computer-aided diagnosis system for the Acute Respiratory Distress Syndrome from chest radiographs","year":2014,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"","keywords":"Radiography; Chest radiograph; Computer-aided diagnosis; ARDS; Linear discriminant analysis; Artificial intelligence; Medicine; Rib cage; Pattern recognition (psychology); Radiology; Segmentation; Support vector machine; Computer-aided; Computer science; Internal medicine; Lung; Anatomy","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":[0.0007183552,0.000262989,0.0007929641,0.000253742,0.0001691176,0.00001172145,0.0002661516,0.0002169002,0.000007416961],"category_scores_gemma":[0.0001825666,0.0001665112,0.00009939934,0.0002731285,0.0007217751,0.00003112531,0.0001186119,0.0002606037,0.000002735261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009130056,"about_ca_system_score_gemma":0.00003167826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003054078,"about_ca_topic_score_gemma":0.00003052285,"domain_scores_codex":[0.9983315,0.0001919107,0.0004535477,0.0005687756,0.0001114824,0.000342796],"domain_scores_gemma":[0.9938689,0.005219674,0.0001461058,0.0005312117,0.0000615356,0.0001725717],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007666262,0.0004383725,0.6920581,0.001259308,0.00155421,0.0003824006,0.00160665,0.0000742062,0.001403404,0.005917187,0.07435308,0.2201864],"study_design_scores_gemma":[0.01328815,0.003692708,0.7760014,0.004013458,0.001049581,0.0002448078,0.0001514332,0.01619601,0.0002595959,0.0007749221,0.183871,0.0004569823],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8170366,0.007617852,0.1114578,0.0569171,0.004831114,0.00175513,0.00009806741,0.0002558024,0.0000305246],"genre_scores_gemma":[0.9732768,0.0005205391,0.003028421,0.02173008,0.001047821,0.0002412161,0.0001244901,0.00002551189,0.000005150009],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2197294,"threshold_uncertainty_score":0.679013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02698310461564114,"score_gpt":0.3207547651198862,"score_spread":0.2937716605042451,"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."}}