{"id":"W4296528405","doi":"10.1111/cns.13963","title":"Machine learning based on Optical Coherence Tomography images as a diagnostic tool for Alzheimer's disease","year":2022,"lang":"en","type":"article","venue":"CNS Neuroscience & Therapeutics","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Nerve fiber layer; Retinal; Inner plexiform layer; Optical coherence tomography; Ophthalmology; Medicine; Atrophy; Receiver operating characteristic; Ganglion cell layer; Montreal Cognitive Assessment; Internal medicine; Pathology; Disease; Dementia","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003225523,0.0002004422,0.0002341605,0.0002088343,0.0006526429,0.000097806,0.0002903719,0.00001868475,0.00007127895],"category_scores_gemma":[0.0005294768,0.0001728432,0.0002503241,0.0007586494,0.0003199565,0.00006459968,0.00007436507,0.0004179299,0.00000760636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002805326,"about_ca_system_score_gemma":0.0001671651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006963487,"about_ca_topic_score_gemma":1.295249e-7,"domain_scores_codex":[0.998091,0.0001276188,0.0002095048,0.0005311427,0.000640095,0.0004006218],"domain_scores_gemma":[0.9985397,0.0006944677,0.00008602958,0.0003801371,0.00007551474,0.0002241838],"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":[0.003447462,0.004965729,0.6727599,0.0001784844,0.0002126698,0.001454214,0.0004464995,0.09749251,0.1222142,0.001036003,0.0007786645,0.0950137],"study_design_scores_gemma":[0.002531314,0.005903008,0.09966688,0.0001152491,0.002805857,0.00008925489,0.0001508904,0.8028739,0.008739026,0.0004969483,0.07581915,0.0008085404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8982637,0.004909406,0.03533397,0.0526793,0.001575833,0.003754318,0.0003847083,0.001013485,0.002085269],"genre_scores_gemma":[0.983702,0.00006346407,0.0003877908,0.01518621,0.00004829113,0.0001692774,0.00002587276,0.00003169211,0.0003853913],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7053814,"threshold_uncertainty_score":0.704834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03460232763328735,"score_gpt":0.3149810567383486,"score_spread":0.2803787291050613,"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."}}