{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:DMFMA7YY32DIYBP5X7XNA3M354","short_pith_number":"pith:DMFMA7YY","schema_version":"1.0","canonical_sha256":"1b0ac07f18de868c05fdbfeed06d9bef3493b2fec4fed25d3febe0bb361bb376","source":{"kind":"arxiv","id":"2112.05900","version":1},"attestation_state":"computed","paper":{"title":"Automated assessment of disease severity of COVID-19 using artificial intelligence with synthetic chest CT","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Aiping Liu, Changbing Qu, Jie Wen, Mengqiu Liu, Peng Zhang, Shana Li, Weifu Lv, Xiaohui Qiu, Yang Li, Yidong Yang, Ying Liu","submitted_at":"2021-12-11T02:03:30Z","abstract_excerpt":"Background: Triage of patients is important to control the pandemic of coronavirus disease 2019 (COVID-19), especially during the peak of the pandemic when clinical resources become extremely limited.\n  Purpose: To develop a method that automatically segments and quantifies lung and pneumonia lesions with synthetic chest CT and assess disease severity in COVID-19 patients.\n  Materials and Methods: In this study, we incorporated data augmentation to generate synthetic chest CT images using public available datasets (285 datasets from \"Lung Nodule Analysis 2016\"). The synthetic images and masks "},"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":"2112.05900","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2021-12-11T02:03:30Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"6cfbd81e61866c4600066ec0d4b898b89dcaa01a3e563d902633dfb37f02f91f","abstract_canon_sha256":"af738eb332bc83aed6fbdd8ca540bb5f48f250c8ce24fabdac4bb900e7cf596d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:40:08.008397Z","signature_b64":"J5Nb6x3BkXIBy1pI0g2VxBO0Ji2K7enfk6MV5XE0GKdcO4rIu654/gZlsL2qRJIronok+3JpPhLd00nUZ5PaCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1b0ac07f18de868c05fdbfeed06d9bef3493b2fec4fed25d3febe0bb361bb376","last_reissued_at":"2026-07-05T03:40:08.007997Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:40:08.007997Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Automated assessment of disease severity of COVID-19 using artificial intelligence with synthetic chest CT","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Aiping Liu, Changbing Qu, Jie Wen, Mengqiu Liu, Peng Zhang, Shana Li, Weifu Lv, Xiaohui Qiu, Yang Li, Yidong Yang, Ying Liu","submitted_at":"2021-12-11T02:03:30Z","abstract_excerpt":"Background: Triage of patients is important to control the pandemic of coronavirus disease 2019 (COVID-19), especially during the peak of the pandemic when clinical resources become extremely limited.\n  Purpose: To develop a method that automatically segments and quantifies lung and pneumonia lesions with synthetic chest CT and assess disease severity in COVID-19 patients.\n  Materials and Methods: In this study, we incorporated data augmentation to generate synthetic chest CT images using public available datasets (285 datasets from \"Lung Nodule Analysis 2016\"). The synthetic images and masks "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.05900","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/2112.05900/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":"2112.05900","created_at":"2026-07-05T03:40:08.008062+00:00"},{"alias_kind":"arxiv_version","alias_value":"2112.05900v1","created_at":"2026-07-05T03:40:08.008062+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.05900","created_at":"2026-07-05T03:40:08.008062+00:00"},{"alias_kind":"pith_short_12","alias_value":"DMFMA7YY32DI","created_at":"2026-07-05T03:40:08.008062+00:00"},{"alias_kind":"pith_short_16","alias_value":"DMFMA7YY32DIYBP5","created_at":"2026-07-05T03:40:08.008062+00:00"},{"alias_kind":"pith_short_8","alias_value":"DMFMA7YY","created_at":"2026-07-05T03:40:08.008062+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/DMFMA7YY32DIYBP5X7XNA3M354","json":"https://pith.science/pith/DMFMA7YY32DIYBP5X7XNA3M354.json","graph_json":"https://pith.science/api/pith-number/DMFMA7YY32DIYBP5X7XNA3M354/graph.json","events_json":"https://pith.science/api/pith-number/DMFMA7YY32DIYBP5X7XNA3M354/events.json","paper":"https://pith.science/paper/DMFMA7YY"},"agent_actions":{"view_html":"https://pith.science/pith/DMFMA7YY32DIYBP5X7XNA3M354","download_json":"https://pith.science/pith/DMFMA7YY32DIYBP5X7XNA3M354.json","view_paper":"https://pith.science/paper/DMFMA7YY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2112.05900&json=true","fetch_graph":"https://pith.science/api/pith-number/DMFMA7YY32DIYBP5X7XNA3M354/graph.json","fetch_events":"https://pith.science/api/pith-number/DMFMA7YY32DIYBP5X7XNA3M354/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DMFMA7YY32DIYBP5X7XNA3M354/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DMFMA7YY32DIYBP5X7XNA3M354/action/storage_attestation","attest_author":"https://pith.science/pith/DMFMA7YY32DIYBP5X7XNA3M354/action/author_attestation","sign_citation":"https://pith.science/pith/DMFMA7YY32DIYBP5X7XNA3M354/action/citation_signature","submit_replication":"https://pith.science/pith/DMFMA7YY32DIYBP5X7XNA3M354/action/replication_record"}},"created_at":"2026-07-05T03:40:08.008062+00:00","updated_at":"2026-07-05T03:40:08.008062+00:00"}