{"id":"W3134620876","doi":"10.1038/s41598-021-84547-5","title":"Latent traits of lung tissue patterns in former smokers derived by dual channel deep learning in computed tomography images","year":2021,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Chronic Obstructive Pulmonary Disease (COPD) Research","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre","funders":"Korea Environmental Industry and Technology Institute; National Institute of Diabetes and Digestive and Kidney Diseases; National Heart, Lung, and Blood Institute; National Research Foundation of Korea; National Institutes of Health; Ministry of Education, India; National Institute of Environmental Health Sciences; Ministry of Environment; National Research Foundation","keywords":"Autoencoder; COPD; Medicine; Artificial intelligence; Spirometry; Airway; Pattern recognition (psychology); Computed tomography; Lung; Voxel; Radiology; Deep learning; Computer science; Internal medicine; Surgery","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":[0.0006588852,0.0002688476,0.0002549201,0.0007682135,0.0001007899,0.0004236981,0.0002109599,0.0003199156,0.0003779348],"category_scores_gemma":[0.001527344,0.0001917837,0.0005115123,0.0004138502,0.0002713054,0.0002725192,0.0003569319,0.0003825841,0.00009525864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003626488,"about_ca_system_score_gemma":0.0003568046,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0042541,"about_ca_topic_score_gemma":0.005473605,"domain_scores_codex":[0.9998426,0.00003679404,0.000009799413,0.00005801547,0.00002237056,0.00003032139],"domain_scores_gemma":[0.9996245,0.0001582896,0.00008117282,0.00005955326,0.00004291126,0.00003363534],"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":[0.001457972,0.0003821799,0.5924433,0.0001102802,0.0002878382,0.0004585525,0.0003941165,0.1543644,0.1015776,0.001985854,0.0009449748,0.1455929],"study_design_scores_gemma":[0.00001534583,0.00008201612,0.2404434,0.000009855916,0.00005224814,0.0001945185,0.0000599977,0.749822,0.00752072,0.001615449,0.0001575992,0.00002687601],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9615447,0.00005772957,0.03787557,0.00004698007,0.000003218601,0.00001203607,0.0002540784,0.00007449723,0.0001311981],"genre_scores_gemma":[0.9934894,0.00002790244,0.005957932,0.000006750355,0.00000276486,0.000008732993,0.000332905,0.000006968015,0.000166747],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0042541,"threshold_uncertainty_score":0.008458674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01258552687111349,"score_gpt":0.2758509755716865,"score_spread":0.2632654487005731,"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."}}