{"id":"W4311086701","doi":"10.1016/j.jtherbio.2022.103404","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":"Journal of Thermal Biology","topic":"Infrared Thermography in Medicine","field":"Medicine","cited_by":40,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Support vector machine; Computer science; Machine learning; Cluster analysis; Algorithm; Segmentation; Feature extraction; Pattern recognition (psychology)","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":[0.00102585,0.0003450941,0.0007596709,0.00169475,0.0004462022,0.001004362,0.0005901522,0.0008958495,0.001830302],"category_scores_gemma":[0.00154709,0.0002502858,0.0007179193,0.0009499631,0.0004094498,0.0007446259,0.0003485062,0.0003940584,0.0005337826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004699792,"about_ca_system_score_gemma":0.0008895802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004723616,"about_ca_topic_score_gemma":0.005981433,"domain_scores_codex":[0.9995339,0.0001204751,0.00003453729,0.0001077428,0.0001485519,0.00005475687],"domain_scores_gemma":[0.9989933,0.0004701515,0.000107562,0.00009030601,0.0003043515,0.00003424793],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001015595,0.0003239874,0.01068486,0.0005038988,0.0002024484,0.0001992485,0.0002301087,0.05528436,0.1309321,0.002776328,0.001865874,0.7959813],"study_design_scores_gemma":[0.00004454628,0.0002338956,0.01853573,0.00004340304,0.0001199627,0.0005343795,0.0001349045,0.9500082,0.02629212,0.002383326,0.001630385,0.00003919276],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2162982,0.002769468,0.7768601,0.0002783409,0.0001100835,0.0001390544,0.0002359319,0.001377426,0.00193133],"genre_scores_gemma":[0.6720857,0.001048433,0.3244331,0.0001426552,0.00007202632,0.00009601619,0.0002701287,0.0001500742,0.0017018],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004723616,"threshold_uncertainty_score":0.009392202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01900526651170667,"score_gpt":0.2999267063894047,"score_spread":0.2809214398776981,"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."}}