{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:JHA4VC5FSYKYJKTDV6WVAOG53S","short_pith_number":"pith:JHA4VC5F","schema_version":"1.0","canonical_sha256":"49c1ca8ba5961584aa63afad5038dddc9422331c31163905b61c2a093e70fe11","source":{"kind":"arxiv","id":"2202.02377","version":1},"attestation_state":"computed","paper":{"title":"Deep Learning-based Assessment of Hepatic Steatosis on chest CT","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":[],"primary_cat":"q-bio.QM","authors_text":"Hugo J.W.L. Aerts, Jakob Weiss, Jana Taron, Michael T. Lu, Roman Zeleznik, Zhongyi Zhang","submitted_at":"2022-02-04T20:22:16Z","abstract_excerpt":"Purpose: Automatic methods are required for the early detection of hepatic steatosis to avoid progression to cirrhosis and cancer. Here, we developed a fully automated deep learning pipeline to quantify hepatic steatosis on non-contrast enhanced chest computed tomography (CT) scans. Materials and Methods: We developed and evaluated our pipeline on chest CT images of 1,431 randomly selected National Lung Screening Trial (NLST) participants. A dataset of 451 CT scans with volumetric liver segmentations of expert readers was used for training a deep learning model. For testing, in an independent "},"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":"2202.02377","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"q-bio.QM","submitted_at":"2022-02-04T20:22:16Z","cross_cats_sorted":[],"title_canon_sha256":"114de84da9bfb1f08d744e3e7cf0c4a2b500b782dda662e87cd3d709982ca9b2","abstract_canon_sha256":"89e637f72b84e6730fa6c8e5fec9edb8e44c832d74188598776b949f8df4b947"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:54:28.279883Z","signature_b64":"yGaTqfmj+vJ8uNQHHDfFirktqEEliDhWDDTNzHCWvXcMAdWi0//Iw5UzPgWuVJY8JC+glYKosyegG7XBg3kLAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"49c1ca8ba5961584aa63afad5038dddc9422331c31163905b61c2a093e70fe11","last_reissued_at":"2026-07-05T03:54:28.279407Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:54:28.279407Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Learning-based Assessment of Hepatic Steatosis on chest CT","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":[],"primary_cat":"q-bio.QM","authors_text":"Hugo J.W.L. Aerts, Jakob Weiss, Jana Taron, Michael T. Lu, Roman Zeleznik, Zhongyi Zhang","submitted_at":"2022-02-04T20:22:16Z","abstract_excerpt":"Purpose: Automatic methods are required for the early detection of hepatic steatosis to avoid progression to cirrhosis and cancer. Here, we developed a fully automated deep learning pipeline to quantify hepatic steatosis on non-contrast enhanced chest computed tomography (CT) scans. Materials and Methods: We developed and evaluated our pipeline on chest CT images of 1,431 randomly selected National Lung Screening Trial (NLST) participants. A dataset of 451 CT scans with volumetric liver segmentations of expert readers was used for training a deep learning model. For testing, in an independent "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.02377","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/2202.02377/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":"2202.02377","created_at":"2026-07-05T03:54:28.279467+00:00"},{"alias_kind":"arxiv_version","alias_value":"2202.02377v1","created_at":"2026-07-05T03:54:28.279467+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.02377","created_at":"2026-07-05T03:54:28.279467+00:00"},{"alias_kind":"pith_short_12","alias_value":"JHA4VC5FSYKY","created_at":"2026-07-05T03:54:28.279467+00:00"},{"alias_kind":"pith_short_16","alias_value":"JHA4VC5FSYKYJKTD","created_at":"2026-07-05T03:54:28.279467+00:00"},{"alias_kind":"pith_short_8","alias_value":"JHA4VC5F","created_at":"2026-07-05T03:54:28.279467+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/JHA4VC5FSYKYJKTDV6WVAOG53S","json":"https://pith.science/pith/JHA4VC5FSYKYJKTDV6WVAOG53S.json","graph_json":"https://pith.science/api/pith-number/JHA4VC5FSYKYJKTDV6WVAOG53S/graph.json","events_json":"https://pith.science/api/pith-number/JHA4VC5FSYKYJKTDV6WVAOG53S/events.json","paper":"https://pith.science/paper/JHA4VC5F"},"agent_actions":{"view_html":"https://pith.science/pith/JHA4VC5FSYKYJKTDV6WVAOG53S","download_json":"https://pith.science/pith/JHA4VC5FSYKYJKTDV6WVAOG53S.json","view_paper":"https://pith.science/paper/JHA4VC5F","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2202.02377&json=true","fetch_graph":"https://pith.science/api/pith-number/JHA4VC5FSYKYJKTDV6WVAOG53S/graph.json","fetch_events":"https://pith.science/api/pith-number/JHA4VC5FSYKYJKTDV6WVAOG53S/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JHA4VC5FSYKYJKTDV6WVAOG53S/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JHA4VC5FSYKYJKTDV6WVAOG53S/action/storage_attestation","attest_author":"https://pith.science/pith/JHA4VC5FSYKYJKTDV6WVAOG53S/action/author_attestation","sign_citation":"https://pith.science/pith/JHA4VC5FSYKYJKTDV6WVAOG53S/action/citation_signature","submit_replication":"https://pith.science/pith/JHA4VC5FSYKYJKTDV6WVAOG53S/action/replication_record"}},"created_at":"2026-07-05T03:54:28.279467+00:00","updated_at":"2026-07-05T03:54:28.279467+00:00"}