{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:YN3G6QKGJM52ABHW3MJM3UXDDX","short_pith_number":"pith:YN3G6QKG","schema_version":"1.0","canonical_sha256":"c3766f41464b3ba004f6db12cdd2e31df5421336a329dc094fb697100ad06b76","source":{"kind":"arxiv","id":"2210.06664","version":1},"attestation_state":"computed","paper":{"title":"Are Macula or Optic Nerve Head Structures better at Diagnosing Glaucoma? An Answer using AI and Wide-Field Optical Coherence Tomography","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"eess.IV","authors_text":"Alexandre Thiery, Charis Y.N. Chiang, Fabian Braeu, Jacqueline Chua, Leopold Schmetterer, Martin Buist, Micha\\\"el J.A. Girard, Royston K.Y. Tan, Thanadet Chuangsuwanich","submitted_at":"2022-10-13T01:51:29Z","abstract_excerpt":"Purpose: (1) To develop a deep learning algorithm to automatically segment structures of the optic nerve head (ONH) and macula in 3D wide-field optical coherence tomography (OCT) scans; (2) To assess whether 3D macula or ONH structures (or the combination of both) provide the best diagnostic power for glaucoma. Methods: A cross-sectional comparative study was performed which included wide-field swept-source OCT scans from 319 glaucoma subjects and 298 non-glaucoma subjects. All scans were compensated to improve deep-tissue visibility. We developed a deep learning algorithm to automatically lab"},"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":"2210.06664","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2022-10-13T01:51:29Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"5c5f4c9b4530f83b1a4557cbb5d79dde7258b129ba96b111162a2467250997e8","abstract_canon_sha256":"69d53fddc9226e26ae99e5e6da6d28d93e7816333f196f4a42f924c751cdb173"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:06:18.471900Z","signature_b64":"3b5M94JMqOAECMkxVjh8BIVtIYcDZVY5F34nZYMsTaz03JYlmmHAmGG5kNYAOPbWHIghWUUo6JUggEMmXWhGDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c3766f41464b3ba004f6db12cdd2e31df5421336a329dc094fb697100ad06b76","last_reissued_at":"2026-07-05T05:06:18.471451Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:06:18.471451Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Are Macula or Optic Nerve Head Structures better at Diagnosing Glaucoma? An Answer using AI and Wide-Field Optical Coherence Tomography","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"eess.IV","authors_text":"Alexandre Thiery, Charis Y.N. Chiang, Fabian Braeu, Jacqueline Chua, Leopold Schmetterer, Martin Buist, Micha\\\"el J.A. Girard, Royston K.Y. Tan, Thanadet Chuangsuwanich","submitted_at":"2022-10-13T01:51:29Z","abstract_excerpt":"Purpose: (1) To develop a deep learning algorithm to automatically segment structures of the optic nerve head (ONH) and macula in 3D wide-field optical coherence tomography (OCT) scans; (2) To assess whether 3D macula or ONH structures (or the combination of both) provide the best diagnostic power for glaucoma. Methods: A cross-sectional comparative study was performed which included wide-field swept-source OCT scans from 319 glaucoma subjects and 298 non-glaucoma subjects. All scans were compensated to improve deep-tissue visibility. We developed a deep learning algorithm to automatically lab"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.06664","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/2210.06664/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":"2210.06664","created_at":"2026-07-05T05:06:18.471509+00:00"},{"alias_kind":"arxiv_version","alias_value":"2210.06664v1","created_at":"2026-07-05T05:06:18.471509+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.06664","created_at":"2026-07-05T05:06:18.471509+00:00"},{"alias_kind":"pith_short_12","alias_value":"YN3G6QKGJM52","created_at":"2026-07-05T05:06:18.471509+00:00"},{"alias_kind":"pith_short_16","alias_value":"YN3G6QKGJM52ABHW","created_at":"2026-07-05T05:06:18.471509+00:00"},{"alias_kind":"pith_short_8","alias_value":"YN3G6QKG","created_at":"2026-07-05T05:06:18.471509+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/YN3G6QKGJM52ABHW3MJM3UXDDX","json":"https://pith.science/pith/YN3G6QKGJM52ABHW3MJM3UXDDX.json","graph_json":"https://pith.science/api/pith-number/YN3G6QKGJM52ABHW3MJM3UXDDX/graph.json","events_json":"https://pith.science/api/pith-number/YN3G6QKGJM52ABHW3MJM3UXDDX/events.json","paper":"https://pith.science/paper/YN3G6QKG"},"agent_actions":{"view_html":"https://pith.science/pith/YN3G6QKGJM52ABHW3MJM3UXDDX","download_json":"https://pith.science/pith/YN3G6QKGJM52ABHW3MJM3UXDDX.json","view_paper":"https://pith.science/paper/YN3G6QKG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2210.06664&json=true","fetch_graph":"https://pith.science/api/pith-number/YN3G6QKGJM52ABHW3MJM3UXDDX/graph.json","fetch_events":"https://pith.science/api/pith-number/YN3G6QKGJM52ABHW3MJM3UXDDX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YN3G6QKGJM52ABHW3MJM3UXDDX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YN3G6QKGJM52ABHW3MJM3UXDDX/action/storage_attestation","attest_author":"https://pith.science/pith/YN3G6QKGJM52ABHW3MJM3UXDDX/action/author_attestation","sign_citation":"https://pith.science/pith/YN3G6QKGJM52ABHW3MJM3UXDDX/action/citation_signature","submit_replication":"https://pith.science/pith/YN3G6QKGJM52ABHW3MJM3UXDDX/action/replication_record"}},"created_at":"2026-07-05T05:06:18.471509+00:00","updated_at":"2026-07-05T05:06:18.471509+00:00"}