{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:PVRQMOJ6FFXHDCTKRIGIXXDR2S","short_pith_number":"pith:PVRQMOJ6","schema_version":"1.0","canonical_sha256":"7d6306393e296e718a6a8a0c8bdc71d48cdda5bba0c2c60e0de0065b15a36766","source":{"kind":"arxiv","id":"2008.04370","version":1},"attestation_state":"computed","paper":{"title":"Predicting Risk of Developing Diabetic Retinopathy using Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Akinori Mitani, Ashish Bora, Avinash V Varadarajan, Boris Babenko, Dale R Webster, Greg S Corrado, Guilherme de Oliveira Marinho, Jorge Cuadros, Lily Peng, Naama Hammel, Paisan Ruamviboonsuk, Pinal Bavishi, Siva Balasubramanian, Subhashini Venugopalan, Sunny Virmani, Yun Liu","submitted_at":"2020-08-10T19:07:22Z","abstract_excerpt":"Diabetic retinopathy (DR) screening is instrumental in preventing blindness, but faces a scaling challenge as the number of diabetic patients rises. Risk stratification for the development of DR may help optimize screening intervals to reduce costs while improving vision-related outcomes. We created and validated two versions of a deep learning system (DLS) to predict the development of mild-or-worse (\"Mild+\") DR in diabetic patients undergoing DR screening. The two versions used either three-fields or a single field of color fundus photographs (CFPs) as input. The training set was derived fro"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2008.04370","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2020-08-10T19:07:22Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"4525400ece02928e4dfd58b372692a2f231809a54edea14bf6972b8211ea4d8b","abstract_canon_sha256":"d252825e11ba0415dd81d03bdeb0d0e0ee8a123e3055de9dc375fa8e58b6a888"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:09:54.813298Z","signature_b64":"dQ2mQrJcJp5eNJSkTt8R439uOLIRjcK9XkEx7Dsz1LBJ24iN92ypff05cuWbADAwiPfst3mcKCcmbqxpls6ECA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7d6306393e296e718a6a8a0c8bdc71d48cdda5bba0c2c60e0de0065b15a36766","last_reissued_at":"2026-07-05T04:09:54.812810Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:09:54.812810Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Predicting Risk of Developing Diabetic Retinopathy using Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Akinori Mitani, Ashish Bora, Avinash V Varadarajan, Boris Babenko, Dale R Webster, Greg S Corrado, Guilherme de Oliveira Marinho, Jorge Cuadros, Lily Peng, Naama Hammel, Paisan Ruamviboonsuk, Pinal Bavishi, Siva Balasubramanian, Subhashini Venugopalan, Sunny Virmani, Yun Liu","submitted_at":"2020-08-10T19:07:22Z","abstract_excerpt":"Diabetic retinopathy (DR) screening is instrumental in preventing blindness, but faces a scaling challenge as the number of diabetic patients rises. Risk stratification for the development of DR may help optimize screening intervals to reduce costs while improving vision-related outcomes. We created and validated two versions of a deep learning system (DLS) to predict the development of mild-or-worse (\"Mild+\") DR in diabetic patients undergoing DR screening. The two versions used either three-fields or a single field of color fundus photographs (CFPs) as input. The training set was derived fro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2008.04370","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2008.04370/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2008.04370","created_at":"2026-07-05T04:09:54.812879+00:00"},{"alias_kind":"arxiv_version","alias_value":"2008.04370v1","created_at":"2026-07-05T04:09:54.812879+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2008.04370","created_at":"2026-07-05T04:09:54.812879+00:00"},{"alias_kind":"pith_short_12","alias_value":"PVRQMOJ6FFXH","created_at":"2026-07-05T04:09:54.812879+00:00"},{"alias_kind":"pith_short_16","alias_value":"PVRQMOJ6FFXHDCTK","created_at":"2026-07-05T04:09:54.812879+00:00"},{"alias_kind":"pith_short_8","alias_value":"PVRQMOJ6","created_at":"2026-07-05T04:09:54.812879+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PVRQMOJ6FFXHDCTKRIGIXXDR2S","json":"https://pith.science/pith/PVRQMOJ6FFXHDCTKRIGIXXDR2S.json","graph_json":"https://pith.science/api/pith-number/PVRQMOJ6FFXHDCTKRIGIXXDR2S/graph.json","events_json":"https://pith.science/api/pith-number/PVRQMOJ6FFXHDCTKRIGIXXDR2S/events.json","paper":"https://pith.science/paper/PVRQMOJ6"},"agent_actions":{"view_html":"https://pith.science/pith/PVRQMOJ6FFXHDCTKRIGIXXDR2S","download_json":"https://pith.science/pith/PVRQMOJ6FFXHDCTKRIGIXXDR2S.json","view_paper":"https://pith.science/paper/PVRQMOJ6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2008.04370&json=true","fetch_graph":"https://pith.science/api/pith-number/PVRQMOJ6FFXHDCTKRIGIXXDR2S/graph.json","fetch_events":"https://pith.science/api/pith-number/PVRQMOJ6FFXHDCTKRIGIXXDR2S/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PVRQMOJ6FFXHDCTKRIGIXXDR2S/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PVRQMOJ6FFXHDCTKRIGIXXDR2S/action/storage_attestation","attest_author":"https://pith.science/pith/PVRQMOJ6FFXHDCTKRIGIXXDR2S/action/author_attestation","sign_citation":"https://pith.science/pith/PVRQMOJ6FFXHDCTKRIGIXXDR2S/action/citation_signature","submit_replication":"https://pith.science/pith/PVRQMOJ6FFXHDCTKRIGIXXDR2S/action/replication_record"}},"created_at":"2026-07-05T04:09:54.812879+00:00","updated_at":"2026-07-05T04:09:54.812879+00:00"}