{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005406998,0.0001152585,0.0004191776,0.0003332359,0.0001132512,0.000003072194,0.00009534552,0.00002071063,0.0006406214],"category_scores_gemma":[0.0001395617,0.00009747803,0.00005954901,0.0003344707,0.0001524911,0.00004915322,0.00005625682,0.0001821117,0.000001587742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000249325,"about_ca_system_score_gemma":0.0001029585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000385486,"about_ca_topic_score_gemma":0.000002166521,"domain_scores_codex":[0.9985238,0.00007369002,0.0004628178,0.0002396277,0.0004264506,0.000273576],"domain_scores_gemma":[0.9994257,0.0000474826,0.0001208317,0.0002215911,0.00008665097,0.00009776025],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003007304,0.00173991,0.07075515,0.0004772303,0.0002807967,0.0002996401,0.001904796,0.00003641093,0.7821651,0.0009463625,0.005636607,0.1327506],"study_design_scores_gemma":[0.01559703,0.02949445,0.1117058,0.002709226,0.001756882,0.0003473003,0.02431394,0.2169197,0.532671,0.0372182,0.02575353,0.001512985],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6497523,0.000154607,0.3441519,0.004027474,0.000662245,0.00103773,0.00005114718,0.00001148898,0.0001510952],"genre_scores_gemma":[0.9396745,0.0001491152,0.0583807,0.0004550621,0.0002675675,0.0002776812,0.0005425474,0.00002413286,0.0002287266],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2899221,"threshold_uncertainty_score":0.7014355,"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."}}