{"id":"W4309308368","doi":"10.3389/fmed.2022.1042706","title":"PET image enhancement using artificial intelligence for better characterization of epilepsy lesions","year":2022,"lang":"en","type":"article","venue":"Frontiers in Medicine","topic":"Epilepsy research and treatment","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre for Addiction and Mental Health","funders":"Centre For Medical Engineering, King’s College London; Université de Lyon; Hospices Civils de Lyon; Engineering and Physical Sciences Research Council; National Institute for Health and Care Research; Agence Nationale de la Recherche; LabEx PRIMES; King's College London; King's College Hospital NHS Foundation Trust; Wellcome Trust","keywords":"Nuclear medicine; Image quality; Lesion; Positron emission tomography; Coefficient of variation; Fluorodeoxyglucose; Region of interest; Mean squared error; Artificial intelligence; Computer science; Medicine; Mathematics; Pathology; Statistics; Image (mathematics)","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":[0.0007594741,0.0007236049,0.000295203,0.0007626423,0.00009656364,0.00050191,0.0003416514,0.0005598619,0.001518992],"category_scores_gemma":[0.001601718,0.0001843253,0.0006465856,0.0003828846,0.0002092539,0.000373109,0.0003443766,0.0004443472,0.0003896826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004250866,"about_ca_system_score_gemma":0.0002963185,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00168866,"about_ca_topic_score_gemma":0.001601291,"domain_scores_codex":[0.9998546,0.00003405761,0.00001280925,0.000037815,0.00004514069,0.00001558737],"domain_scores_gemma":[0.9995974,0.000180828,0.00006549645,0.00004467594,0.00009862203,0.00001297883],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002758431,0.0001792583,0.005953006,0.0003174354,0.0002189681,0.0003322295,0.00009664064,0.4494174,0.1119878,0.001605506,0.002245355,0.4273705],"study_design_scores_gemma":[0.000006706397,0.00008500661,0.002187295,0.00001753501,0.00002899412,0.0001264871,0.00000828526,0.9740677,0.0218229,0.0006550591,0.000983613,0.0000103325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.266269,0.001560463,0.7241972,0.0004950773,0.0001072411,0.0001396434,0.0004546537,0.003449976,0.003326814],"genre_scores_gemma":[0.7411929,0.0006236938,0.254784,0.0002025583,0.00004895889,0.00009227495,0.000631981,0.0001326847,0.002290986],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00168866,"threshold_uncertainty_score":0.005081534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04916978895047264,"score_gpt":0.3441667478214581,"score_spread":0.2949969588709855,"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."}}