{"id":"W4392190377","doi":"10.1080/09273948.2024.2319281","title":"Automated Machine Learning versus Expert-Designed Models in Ocular Toxoplasmosis: Detection and Lesion Localization Using Fundus Images","year":2024,"lang":"en","type":"article","venue":"Ocular Immunology and Inflammation","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre intégré universitaire de santé et de services sociaux de l'Est-de-l'Île-de-Montréal; Centre Hospitalier de l’Université de Montréal; Université de Montréal; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Hôpital Maisonneuve-Rosemont","funders":"","keywords":"Medicine; Toxoplasmosis; Fundus (uterus); Ophthalmology; Artificial intelligence; Lesion; Optometry; Machine learning; Pathology; Computer science","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":[0.0002432468,0.0001335374,0.0002071171,0.0003495728,0.0001542671,0.00007159464,0.00002099044,0.0001623135,0.000007969774],"category_scores_gemma":[0.00009026489,0.0001227735,0.00004077145,0.0002964054,0.00007242991,0.0004062218,0.00002600564,0.0002158391,0.000003508982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007750309,"about_ca_system_score_gemma":0.00002287465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002390467,"about_ca_topic_score_gemma":0.00001136013,"domain_scores_codex":[0.9990939,0.0001672477,0.0002489318,0.0002486737,0.00009778776,0.0001434976],"domain_scores_gemma":[0.9997218,0.00004363846,0.00005738183,0.00009282312,0.00005553507,0.00002886412],"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":[0.0005041917,0.00001874068,0.001420004,0.0001539482,0.0002035586,0.00007629336,0.0008157142,0.01052877,0.9056762,0.0001295164,0.000003983767,0.08046901],"study_design_scores_gemma":[0.0009510852,0.0001551775,0.001195274,0.0002972056,0.0001391804,0.00009339572,0.0002402587,0.9338766,0.06253269,0.0001164486,0.0002886182,0.0001140603],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9139282,0.01725843,0.06794216,0.0003084683,0.00009502124,0.0001447956,5.034131e-7,0.0002633922,0.00005903191],"genre_scores_gemma":[0.9958108,0.003370697,0.000652412,0.00001561567,0.00003214677,0.00001046482,0.00005042687,0.00001751623,0.00003994163],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9233478,"threshold_uncertainty_score":0.5006559,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02232215650896639,"score_gpt":0.2860999346124337,"score_spread":0.2637777781034673,"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."}}