{"id":"W4403405342","doi":"10.3389/fcell.2024.1484329","title":"Development of a transformer-based deep learning algorithm for diabetic peripheral neuropathy classification using corneal confocal microscopy images","year":2024,"lang":"en","type":"article","venue":"Frontiers in Cell and Developmental Biology","topic":"Retinal Diseases and Treatments","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Confocal microscopy; Peripheral neuropathy; Peripheral; Medicine; Ophthalmology; Microscopy; Artificial intelligence; Algorithm; Computer science; Pathology; Biomedical engineering; Diabetes mellitus; Optics; Internal medicine; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001081935,0.000545529,0.000494897,0.000730186,0.0002293695,0.0005882229,0.001008496,0.0006334115,0.001337225],"category_scores_gemma":[0.002229705,0.0002208899,0.0005020418,0.0003459372,0.0002236559,0.0004977401,0.0007185277,0.0008547783,0.0004916016],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007740547,"about_ca_system_score_gemma":0.001134669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005412703,"about_ca_topic_score_gemma":0.004837587,"domain_scores_codex":[0.9997292,0.00005096999,0.00002466525,0.00008303883,0.00007285937,0.00003933108],"domain_scores_gemma":[0.9994152,0.0001936901,0.00004756322,0.0000396033,0.0002665432,0.0000374998],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004308578,0.0003216738,0.01501573,0.0000947898,0.0001337486,0.000145683,0.00009112847,0.19785,0.01958656,0.002419177,0.004788308,0.7591224],"study_design_scores_gemma":[0.00001089457,0.00006277705,0.0007396021,0.000006907534,0.00001201312,0.0000397566,0.00001174707,0.9950186,0.003051429,0.0006786931,0.0003620939,0.000005406745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1403875,0.0004712771,0.8540384,0.0004311381,0.00008037558,0.0002272148,0.000312114,0.001988525,0.002063437],"genre_scores_gemma":[0.7277968,0.0002783074,0.2672544,0.0003655544,0.00003672724,0.0002965831,0.0009884313,0.00006465476,0.002918404],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005412703,"threshold_uncertainty_score":0.01076239,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0171063241374714,"score_gpt":0.2805287682080669,"score_spread":0.2634224440705955,"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."}}