{"id":"W3012028988","doi":"10.2196/16225","title":"Deep Learning–Based Prediction of Refractive Error Using Photorefraction Images Captured by a Smartphone: Model Development and Validation Study","year":2020,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Ophthalmology and Visual Impairment Studies","field":"Medicine","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Research Foundation of Korea; Korea Health Industry Development Institute; National Research Foundation","keywords":"Refractive error; Artificial intelligence; Dioptre; Deep learning; Computer science; Mean squared prediction error; Refraction; Medicine; Machine learning; Ophthalmology; Optics; Visual acuity","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003076825,0.0001530017,0.0003281274,0.00007336708,0.0001388631,0.000009273364,0.00004309077,0.000163699,0.00004325525],"category_scores_gemma":[0.0003401407,0.0001254557,0.00003175572,0.0001526119,0.000112415,0.0002545282,0.00005433179,0.000400359,0.000004303517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000735707,"about_ca_system_score_gemma":0.0001583556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008555332,"about_ca_topic_score_gemma":3.807485e-7,"domain_scores_codex":[0.998509,0.00006018231,0.0005841198,0.0001162305,0.0005739655,0.0001564925],"domain_scores_gemma":[0.9992098,0.00007162594,0.0002955926,0.00007444141,0.0001352769,0.0002132387],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005926567,0.009614513,0.5774195,0.004008802,0.001848176,0.00007014994,0.3649699,0.004916443,0.0120514,0.000005991697,0.005438051,0.01373048],"study_design_scores_gemma":[0.004047184,0.002753139,0.03675397,0.0001554843,0.0001992624,0.00002664955,0.02280443,0.9226397,0.01030158,0.000002764077,0.0001665704,0.0001493124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9779994,0.0000317019,0.02082841,0.0002345295,0.00004652781,0.0006993902,0.00000669179,0.00005046587,0.0001028246],"genre_scores_gemma":[0.9967778,0.000009746966,0.002583094,0.0004196766,0.00003522622,0.00005230818,0.00008473875,0.00001089059,0.00002653265],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9177232,"threshold_uncertainty_score":0.5115933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05286319015584507,"score_gpt":0.3621973195748922,"score_spread":0.3093341294190471,"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."}}