{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:ZMIGV7GA4UIGMPOWQT5T7JZCSK","short_pith_number":"pith:ZMIGV7GA","schema_version":"1.0","canonical_sha256":"cb106afcc0e510663dd684fb3fa722928df3eb34a76d5cfc3e11beae3e27de4c","source":{"kind":"arxiv","id":"2203.05784","version":1},"attestation_state":"computed","paper":{"title":"AI-enabled Automatic Multimodal Fusion of Cone-Beam CT and Intraoral Scans for Intelligent 3D Tooth-Bone Reconstruction and Clinical Applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"eess.IV","authors_text":"Bing Fang, Hangzheng Lin, Haoji Hu, Huikai Wu, Huimin Xiong, Jianfei Yang, Jiaxiang Liu, Jin Hao, Jin Li, Kaiwei Sun, Ruizhe Chen, Wanghui Ding, Wanlu Liu, Wei Pan, Yang Feng, Youyi Zheng, Yueling Zhang, Zeyu Zhao, Zhihe Zhao, Zuozhu Liu","submitted_at":"2022-03-11T07:50:15Z","abstract_excerpt":"A critical step in virtual dental treatment planning is to accurately delineate all tooth-bone structures from CBCT with high fidelity and accurate anatomical information. Previous studies have established several methods for CBCT segmentation using deep learning. However, the inherent resolution discrepancy of CBCT and the loss of occlusal and dentition information largely limited its clinical applicability. Here, we present a Deep Dental Multimodal Analysis (DDMA) framework consisting of a CBCT segmentation model, an intraoral scan (IOS) segmentation model (the most accurate digital dental m"},"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":"2203.05784","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2022-03-11T07:50:15Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"590afdb7ae624a03f047675c45b53cc90b41cee3db7593e5b59f3c26873d9a5e","abstract_canon_sha256":"1fe8618c9cd25307992979d660babffece8efbd780a3e9d729072d37fca06902"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:04:09.293626Z","signature_b64":"hfA/tZ7hExVrSIvnpvhu40WILSbKMoBywJaqUYYQwq+HCeaxbuWjrFLYxmw2nIg3hSM2cf8MpYIOl9BYP6m0CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cb106afcc0e510663dd684fb3fa722928df3eb34a76d5cfc3e11beae3e27de4c","last_reissued_at":"2026-07-05T04:04:09.293189Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:04:09.293189Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AI-enabled Automatic Multimodal Fusion of Cone-Beam CT and Intraoral Scans for Intelligent 3D Tooth-Bone Reconstruction and Clinical Applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"eess.IV","authors_text":"Bing Fang, Hangzheng Lin, Haoji Hu, Huikai Wu, Huimin Xiong, Jianfei Yang, Jiaxiang Liu, Jin Hao, Jin Li, Kaiwei Sun, Ruizhe Chen, Wanghui Ding, Wanlu Liu, Wei Pan, Yang Feng, Youyi Zheng, Yueling Zhang, Zeyu Zhao, Zhihe Zhao, Zuozhu Liu","submitted_at":"2022-03-11T07:50:15Z","abstract_excerpt":"A critical step in virtual dental treatment planning is to accurately delineate all tooth-bone structures from CBCT with high fidelity and accurate anatomical information. Previous studies have established several methods for CBCT segmentation using deep learning. However, the inherent resolution discrepancy of CBCT and the loss of occlusal and dentition information largely limited its clinical applicability. Here, we present a Deep Dental Multimodal Analysis (DDMA) framework consisting of a CBCT segmentation model, an intraoral scan (IOS) segmentation model (the most accurate digital dental m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.05784","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/2203.05784/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":"2203.05784","created_at":"2026-07-05T04:04:09.293246+00:00"},{"alias_kind":"arxiv_version","alias_value":"2203.05784v1","created_at":"2026-07-05T04:04:09.293246+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.05784","created_at":"2026-07-05T04:04:09.293246+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZMIGV7GA4UIG","created_at":"2026-07-05T04:04:09.293246+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZMIGV7GA4UIGMPOW","created_at":"2026-07-05T04:04:09.293246+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZMIGV7GA","created_at":"2026-07-05T04:04:09.293246+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2511.14286","citing_title":"NeuralBoneReg: An Instance-Specific Label-Free Point Cloud-Based Method for Multi-Modal Bone Surface Registration","ref_index":56,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZMIGV7GA4UIGMPOWQT5T7JZCSK","json":"https://pith.science/pith/ZMIGV7GA4UIGMPOWQT5T7JZCSK.json","graph_json":"https://pith.science/api/pith-number/ZMIGV7GA4UIGMPOWQT5T7JZCSK/graph.json","events_json":"https://pith.science/api/pith-number/ZMIGV7GA4UIGMPOWQT5T7JZCSK/events.json","paper":"https://pith.science/paper/ZMIGV7GA"},"agent_actions":{"view_html":"https://pith.science/pith/ZMIGV7GA4UIGMPOWQT5T7JZCSK","download_json":"https://pith.science/pith/ZMIGV7GA4UIGMPOWQT5T7JZCSK.json","view_paper":"https://pith.science/paper/ZMIGV7GA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2203.05784&json=true","fetch_graph":"https://pith.science/api/pith-number/ZMIGV7GA4UIGMPOWQT5T7JZCSK/graph.json","fetch_events":"https://pith.science/api/pith-number/ZMIGV7GA4UIGMPOWQT5T7JZCSK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZMIGV7GA4UIGMPOWQT5T7JZCSK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZMIGV7GA4UIGMPOWQT5T7JZCSK/action/storage_attestation","attest_author":"https://pith.science/pith/ZMIGV7GA4UIGMPOWQT5T7JZCSK/action/author_attestation","sign_citation":"https://pith.science/pith/ZMIGV7GA4UIGMPOWQT5T7JZCSK/action/citation_signature","submit_replication":"https://pith.science/pith/ZMIGV7GA4UIGMPOWQT5T7JZCSK/action/replication_record"}},"created_at":"2026-07-05T04:04:09.293246+00:00","updated_at":"2026-07-05T04:04:09.293246+00:00"}