{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:2DVS3EEG5PC7E4VEYVF3DZBXWW","short_pith_number":"pith:2DVS3EEG","schema_version":"1.0","canonical_sha256":"d0eb2d9086ebc5f272a4c54bb1e437b5b1ef87e1a4a7381ed601ed615c13bc2a","source":{"kind":"arxiv","id":"2006.06292","version":2},"attestation_state":"computed","paper":{"title":"Design Considerations for High Impact, Automated Echocardiogram Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CY","authors_text":"Antonio S\\'anchez-Puente, Courtney Irwin, Dave Van Veen, Elena D\\'iaz-Pel\\'aez, Jes\\'us Sampedro-G\\'omez, Liliana Mill\\'an, Manuel Barreiro-Perez, Pedro L. S\\'anchez, P. Ignacio Dorado-D\\'iaz, Sara Guerreiro de Sousa, V\\'ictor Vicente-Palacios, Wiebke Toussaint, Yoni Nachmany","submitted_at":"2020-06-11T09:57:05Z","abstract_excerpt":"Deep learning has the potential to automate echocardiogram analysis for early detection of heart disease. Based on a qualitative analysis of design concerns, this study suggests that predicting normal heart function instead of disease accounts for data quality bias and significantly increases efficiency in cardiologists' workflows."},"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":"2006.06292","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CY","submitted_at":"2020-06-11T09:57:05Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"97e709d80259f4fd00589031685843f74b5024e22ffc78188f679e8f7788c3d6","abstract_canon_sha256":"93b3f5957440ecd9582a83f56b99aa8c195dbde36be3e8c0ddd1e5362d19a5ce"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:11:25.098000Z","signature_b64":"Clpe/3hCXAujZUhEVK5A33tJBuB1Bal56pZTmqgVd4GnrVRKh5edPDhJtBVZ3L8ueIgZmlKDmiE6VomR0aK+AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d0eb2d9086ebc5f272a4c54bb1e437b5b1ef87e1a4a7381ed601ed615c13bc2a","last_reissued_at":"2026-07-05T01:11:25.097597Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:11:25.097597Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Design Considerations for High Impact, Automated Echocardiogram Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CY","authors_text":"Antonio S\\'anchez-Puente, Courtney Irwin, Dave Van Veen, Elena D\\'iaz-Pel\\'aez, Jes\\'us Sampedro-G\\'omez, Liliana Mill\\'an, Manuel Barreiro-Perez, Pedro L. S\\'anchez, P. Ignacio Dorado-D\\'iaz, Sara Guerreiro de Sousa, V\\'ictor Vicente-Palacios, Wiebke Toussaint, Yoni Nachmany","submitted_at":"2020-06-11T09:57:05Z","abstract_excerpt":"Deep learning has the potential to automate echocardiogram analysis for early detection of heart disease. Based on a qualitative analysis of design concerns, this study suggests that predicting normal heart function instead of disease accounts for data quality bias and significantly increases efficiency in cardiologists' workflows."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.06292","kind":"arxiv","version":2},"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/2006.06292/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":"2006.06292","created_at":"2026-07-05T01:11:25.097652+00:00"},{"alias_kind":"arxiv_version","alias_value":"2006.06292v2","created_at":"2026-07-05T01:11:25.097652+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.06292","created_at":"2026-07-05T01:11:25.097652+00:00"},{"alias_kind":"pith_short_12","alias_value":"2DVS3EEG5PC7","created_at":"2026-07-05T01:11:25.097652+00:00"},{"alias_kind":"pith_short_16","alias_value":"2DVS3EEG5PC7E4VE","created_at":"2026-07-05T01:11:25.097652+00:00"},{"alias_kind":"pith_short_8","alias_value":"2DVS3EEG","created_at":"2026-07-05T01:11:25.097652+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/2DVS3EEG5PC7E4VEYVF3DZBXWW","json":"https://pith.science/pith/2DVS3EEG5PC7E4VEYVF3DZBXWW.json","graph_json":"https://pith.science/api/pith-number/2DVS3EEG5PC7E4VEYVF3DZBXWW/graph.json","events_json":"https://pith.science/api/pith-number/2DVS3EEG5PC7E4VEYVF3DZBXWW/events.json","paper":"https://pith.science/paper/2DVS3EEG"},"agent_actions":{"view_html":"https://pith.science/pith/2DVS3EEG5PC7E4VEYVF3DZBXWW","download_json":"https://pith.science/pith/2DVS3EEG5PC7E4VEYVF3DZBXWW.json","view_paper":"https://pith.science/paper/2DVS3EEG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2006.06292&json=true","fetch_graph":"https://pith.science/api/pith-number/2DVS3EEG5PC7E4VEYVF3DZBXWW/graph.json","fetch_events":"https://pith.science/api/pith-number/2DVS3EEG5PC7E4VEYVF3DZBXWW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2DVS3EEG5PC7E4VEYVF3DZBXWW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2DVS3EEG5PC7E4VEYVF3DZBXWW/action/storage_attestation","attest_author":"https://pith.science/pith/2DVS3EEG5PC7E4VEYVF3DZBXWW/action/author_attestation","sign_citation":"https://pith.science/pith/2DVS3EEG5PC7E4VEYVF3DZBXWW/action/citation_signature","submit_replication":"https://pith.science/pith/2DVS3EEG5PC7E4VEYVF3DZBXWW/action/replication_record"}},"created_at":"2026-07-05T01:11:25.097652+00:00","updated_at":"2026-07-05T01:11:25.097652+00:00"}