{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:CPJQB56ETPHJMY2Y63KYWPF27K","short_pith_number":"pith:CPJQB56E","schema_version":"1.0","canonical_sha256":"13d300f7c49bce966358f6d58b3cbafa9bcbfd73e7223fbf105793d8d8ca887c","source":{"kind":"arxiv","id":"1810.10664","version":1},"attestation_state":"computed","paper":{"title":"Automated Process Incorporating Machine Learning Segmentation and Correlation of Oral Diseases with Systemic Health","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-bio.QM","stat.ML"],"primary_cat":"cs.LG","authors_text":"Ali Muftu, Aman Rana, Gregory Yauney, Lawrence C. Wong, Perikumar Javia, Pratik Shah","submitted_at":"2018-10-25T00:42:20Z","abstract_excerpt":"Imaging fluorescent disease biomarkers in tissues and skin is a non-invasive method to screen for health conditions. We report an automated process that combines intraoral fluorescent porphyrin biomarker imaging, clinical examinations and machine learning for correlation of systemic health conditions with periodontal disease. 1215 intraoral fluorescent images, from 284 consenting adults aged 18-90, were analyzed using a machine learning classifier that can segment periodontal inflammation. The classifier achieved an AUC of 0.677 with precision and recall of 0.271 and 0.429, respectively, indic"},"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":"1810.10664","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-25T00:42:20Z","cross_cats_sorted":["q-bio.QM","stat.ML"],"title_canon_sha256":"cfaddaed232e9701b1471bf41fec54e98af5c8503ee75eae8ac83b833fd18d37","abstract_canon_sha256":"09d7650f32606ab81f7acfa0aea454b8a7376a0be67c554e71385d9ef4f02454"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:02:19.947511Z","signature_b64":"13hAxAN9wmSzrq87h6EuVjU3hygBANGxXbi24zn11J9Vb0HPqb3ICG7TkhkBPw6+gGI4JJypN9rd6fbUgpGIBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"13d300f7c49bce966358f6d58b3cbafa9bcbfd73e7223fbf105793d8d8ca887c","last_reissued_at":"2026-05-18T00:02:19.947062Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:02:19.947062Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Automated Process Incorporating Machine Learning Segmentation and Correlation of Oral Diseases with Systemic Health","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-bio.QM","stat.ML"],"primary_cat":"cs.LG","authors_text":"Ali Muftu, Aman Rana, Gregory Yauney, Lawrence C. Wong, Perikumar Javia, Pratik Shah","submitted_at":"2018-10-25T00:42:20Z","abstract_excerpt":"Imaging fluorescent disease biomarkers in tissues and skin is a non-invasive method to screen for health conditions. We report an automated process that combines intraoral fluorescent porphyrin biomarker imaging, clinical examinations and machine learning for correlation of systemic health conditions with periodontal disease. 1215 intraoral fluorescent images, from 284 consenting adults aged 18-90, were analyzed using a machine learning classifier that can segment periodontal inflammation. The classifier achieved an AUC of 0.677 with precision and recall of 0.271 and 0.429, respectively, indic"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.10664","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":""},"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":"1810.10664","created_at":"2026-05-18T00:02:19.947125+00:00"},{"alias_kind":"arxiv_version","alias_value":"1810.10664v1","created_at":"2026-05-18T00:02:19.947125+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.10664","created_at":"2026-05-18T00:02:19.947125+00:00"},{"alias_kind":"pith_short_12","alias_value":"CPJQB56ETPHJ","created_at":"2026-05-18T12:32:16.446611+00:00"},{"alias_kind":"pith_short_16","alias_value":"CPJQB56ETPHJMY2Y","created_at":"2026-05-18T12:32:16.446611+00:00"},{"alias_kind":"pith_short_8","alias_value":"CPJQB56E","created_at":"2026-05-18T12:32:16.446611+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/CPJQB56ETPHJMY2Y63KYWPF27K","json":"https://pith.science/pith/CPJQB56ETPHJMY2Y63KYWPF27K.json","graph_json":"https://pith.science/api/pith-number/CPJQB56ETPHJMY2Y63KYWPF27K/graph.json","events_json":"https://pith.science/api/pith-number/CPJQB56ETPHJMY2Y63KYWPF27K/events.json","paper":"https://pith.science/paper/CPJQB56E"},"agent_actions":{"view_html":"https://pith.science/pith/CPJQB56ETPHJMY2Y63KYWPF27K","download_json":"https://pith.science/pith/CPJQB56ETPHJMY2Y63KYWPF27K.json","view_paper":"https://pith.science/paper/CPJQB56E","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1810.10664&json=true","fetch_graph":"https://pith.science/api/pith-number/CPJQB56ETPHJMY2Y63KYWPF27K/graph.json","fetch_events":"https://pith.science/api/pith-number/CPJQB56ETPHJMY2Y63KYWPF27K/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CPJQB56ETPHJMY2Y63KYWPF27K/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CPJQB56ETPHJMY2Y63KYWPF27K/action/storage_attestation","attest_author":"https://pith.science/pith/CPJQB56ETPHJMY2Y63KYWPF27K/action/author_attestation","sign_citation":"https://pith.science/pith/CPJQB56ETPHJMY2Y63KYWPF27K/action/citation_signature","submit_replication":"https://pith.science/pith/CPJQB56ETPHJMY2Y63KYWPF27K/action/replication_record"}},"created_at":"2026-05-18T00:02:19.947125+00:00","updated_at":"2026-05-18T00:02:19.947125+00:00"}