{"id":"W4389668222","doi":"10.1109/nssmicrtsd49126.2023.10337836","title":"Classifying chronic pain conditions using deep learning and resting-state fMRI","year":2023,"lang":"en","type":"article","venue":"","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McGill University Health Centre","funders":"","keywords":"Convolutional neural network; Logistic regression; Artificial intelligence; Resting state fMRI; Chronic pain; Neuroimaging; Deep learning; AdaBoost; Random forest; Machine learning; Computer science; Medicine; Physical medicine and rehabilitation; Physical therapy; Support vector machine; Radiology; Psychiatry","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":[0.0005567643,0.0004907734,0.0003488688,0.001432826,0.0001565559,0.0005722782,0.0003007181,0.000607967,0.001063104],"category_scores_gemma":[0.001682216,0.000144907,0.0004894886,0.0006093523,0.0002049964,0.0004157067,0.0003202696,0.0003532747,0.0002250646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003917535,"about_ca_system_score_gemma":0.0003308898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007263727,"about_ca_topic_score_gemma":0.009174892,"domain_scores_codex":[0.9998046,0.00003776309,0.00001716732,0.00005892762,0.00003689112,0.00004469066],"domain_scores_gemma":[0.9996105,0.000179228,0.00008825812,0.00002251165,0.00006870502,0.00003069682],"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":[0.002159009,0.0009580263,0.2613465,0.000722481,0.0007935038,0.0005998209,0.0002316463,0.1569525,0.02566612,0.001034174,0.005564392,0.5439718],"study_design_scores_gemma":[0.00004405611,0.0005354137,0.1657287,0.0001474792,0.0001161695,0.0005344969,0.0001097556,0.8239577,0.005405625,0.002291521,0.001088277,0.00004084102],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9280415,0.003565392,0.06172513,0.0005838471,0.00005411039,0.0001099018,0.003185202,0.0005586995,0.002176275],"genre_scores_gemma":[0.9817585,0.0006185088,0.0148595,0.0001037952,0.00004617202,0.00006636956,0.001928178,0.0000138875,0.0006051048],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007263727,"threshold_uncertainty_score":0.01444286,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07921768742752691,"score_gpt":0.3197916371365959,"score_spread":0.240573949709069,"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."}}