{"id":"W4293096168","doi":"10.2139/ssrn.4191391","title":"Automated Segmentation and Classification of Hand Thermal Images in Rheumatoid Arthritis Using Machine Learning Algorithms: A Comparison with Quantum Machine Learning Technique","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Infrared Thermography in Medicine","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Segmentation; Support vector machine; Artificial intelligence; Rheumatoid arthritis; Machine learning; Computer science; Algorithm; Quantum; Pattern recognition (psychology); Medicine; Physics","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":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.002358091,0.0002075877,0.0004665368,0.0005095159,0.0004694844,0.00002702542,0.00009523939,0.00006371427,0.00005476096],"category_scores_gemma":[0.00006924038,0.000182341,0.00005011487,0.0005880852,0.0001624484,0.0001460643,0.00004312941,0.003447707,3.494167e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006131541,"about_ca_system_score_gemma":0.0006190951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003811168,"about_ca_topic_score_gemma":0.0001226845,"domain_scores_codex":[0.9973452,0.0004797965,0.0005880738,0.0002381813,0.0005053594,0.0008433864],"domain_scores_gemma":[0.9990687,0.00006743312,0.0005734965,0.0001126549,0.00010869,0.00006900803],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009318106,0.0002279977,0.3212048,0.00005149002,0.0002088602,0.00004437497,0.00170616,0.00341154,0.6513382,0.0003467325,0.000002770911,0.02052527],"study_design_scores_gemma":[0.01550186,0.01130034,0.06454286,0.001252104,0.0002151713,0.01457744,0.0228575,0.8361924,0.0313855,0.001363589,0.0001605103,0.0006507392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9719816,0.01278113,0.01438382,0.0001855068,0.00003715983,0.0004790951,0.000005175977,0.000105852,0.00004061977],"genre_scores_gemma":[0.9936717,0.003955338,0.002131928,0.0000171902,0.0000316153,0.00003882653,0.00007676935,0.00004950323,0.00002717925],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8327808,"threshold_uncertainty_score":0.9988514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0109737704002964,"score_gpt":0.2734788872971449,"score_spread":0.2625051168968485,"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."}}