{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001571378,0.001444908,0.0008171261,0.0005653102,0.0002850286,0.0004967547,0.001034328,0.001005831,0.001390916],"category_scores_gemma":[0.002762402,0.0003175166,0.001116803,0.0003896943,0.0003302881,0.0004578361,0.000651285,0.001528607,0.0004856382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001019213,"about_ca_system_score_gemma":0.001372199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0459563,"about_ca_topic_score_gemma":0.0265869,"domain_scores_codex":[0.9995942,0.0001127786,0.00003294212,0.0001039365,0.00007421639,0.00008201431],"domain_scores_gemma":[0.9984226,0.0007883024,0.00009862673,0.00009771559,0.0005342705,0.00005838405],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007556554,0.001118257,0.0332511,0.0002167546,0.0004067337,0.000238566,0.00008190673,0.7949189,0.003481437,0.0003382636,0.004491145,0.1607013],"study_design_scores_gemma":[0.00001366603,0.00008770763,0.001951366,0.00001285814,0.00002094221,0.00001382654,0.00001218153,0.9970106,0.0006790127,0.00006996765,0.0001219607,0.00000597035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9380293,0.002833986,0.05270641,0.0005494732,0.0002127289,0.000166512,0.001117068,0.001563002,0.002821449],"genre_scores_gemma":[0.9797872,0.0004383427,0.01560241,0.0001632126,0.00002710334,0.0001114194,0.001903283,0.00003018534,0.00193679],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0459563,"threshold_uncertainty_score":0.09137762,"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."}}