{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:DFFIPBNBAKZCVRC336F7HUAPKK","short_pith_number":"pith:DFFIPBNB","schema_version":"1.0","canonical_sha256":"194a8785a102b22ac45bdf8bf3d00f52894c38174ddd4fd1119012ceddfc56aa","source":{"kind":"arxiv","id":"2605.02207","version":2},"attestation_state":"computed","paper":{"title":"MultiSense-Pneumo: A Multimodal Learning Framework for Pneumonia Screening in Resource-Constrained Settings","license":"http://creativecommons.org/licenses/by/4.0/","headline":"The paper describes MultiSense-Pneumo, an offline-capable multimodal framework that fuses symptom triage, audio classification, speech recognition, and radiograph analysis for pneumonia screening in low-resource settings.","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Chameli Dommanige, Dineth Jayakody, Pasindu Thenahandi","submitted_at":"2026-05-04T04:14:35Z","abstract_excerpt":"Pneumonia remains a leading global cause of morbidity and mortality, particularly in low-resource settings where access to imaging, laboratory testing, and specialist care is limited. Clinical assessment relies on heterogeneous evidence, including symptoms, respiratory patterns, spoken descriptions, and chest imaging, making frontline screening inherently multimodal. However, many existing computational approaches remain unimodal and focus primarily on radiographs. In this work, we present MultiSense-Pneumo, a multimodal research prototype for pneumonia-oriented screening and triage support th"},"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":true},"canonical_record":{"source":{"id":"2605.02207","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-04T04:14:35Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"1be4c244be69b5e4e87fc82070240a7101cb69c08e5a4b49c1ae381136eff99d","abstract_canon_sha256":"d1cb907f84e8d597ef8b6bb24fcd3cfca2f5450f982383c52eb73527db7c68f3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-27T01:04:58.651036Z","signature_b64":"kAHKJ3wBp5i1lxPq6Hp8oexPa49T1x8MtzSPv8vamBdC4dnu5USVjerrhjJYZeszhm/0msHFNv31GZv+GJJCAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"194a8785a102b22ac45bdf8bf3d00f52894c38174ddd4fd1119012ceddfc56aa","last_reissued_at":"2026-05-27T01:04:58.650389Z","signature_status":"signed_v1","first_computed_at":"2026-05-27T01:04:58.650389Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MultiSense-Pneumo: A Multimodal Learning Framework for Pneumonia Screening in Resource-Constrained Settings","license":"http://creativecommons.org/licenses/by/4.0/","headline":"The paper describes MultiSense-Pneumo, an offline-capable multimodal framework that fuses symptom triage, audio classification, speech recognition, and radiograph analysis for pneumonia screening in low-resource settings.","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Chameli Dommanige, Dineth Jayakody, Pasindu Thenahandi","submitted_at":"2026-05-04T04:14:35Z","abstract_excerpt":"Pneumonia remains a leading global cause of morbidity and mortality, particularly in low-resource settings where access to imaging, laboratory testing, and specialist care is limited. Clinical assessment relies on heterogeneous evidence, including symptoms, respiratory patterns, spoken descriptions, and chest imaging, making frontline screening inherently multimodal. However, many existing computational approaches remain unimodal and focus primarily on radiographs. In this work, we present MultiSense-Pneumo, a multimodal research prototype for pneumonia-oriented screening and triage support th"},"claims":{"count":3,"items":[{"kind":"strongest_claim","text":"MultiSense-Pneumo is a multimodal framework for pneumonia oriented screening and triage support that integrates structured symptom descriptors, cough audio, spoken language, and chest radiographs and can operate fully offline on standard laptop class hardware.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the normalized risk signals from each modality can be meaningfully aggregated into a unified screening estimate that improves triage decisions in real resource-constrained environments, an assumption stated in the abstract but without supporting performance data or validation studies.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"The paper describes MultiSense-Pneumo, an offline-capable multimodal framework that fuses symptom triage, audio classification, speech recognition, and radiograph analysis for pneumonia screening in low-resource settings.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"}],"snapshot_sha256":"b7049a86fd9ea202998b1069e4a8316670cd70521e4e2de570338d85a9490bb9"},"source":{"id":"2605.02207","kind":"arxiv","version":2},"verdict":{"id":"56c0ec3d-acfd-4778-9d24-2ac799e6b519","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-08T19:35:57.692610Z","strongest_claim":"MultiSense-Pneumo is a multimodal framework for pneumonia oriented screening and triage support that integrates structured symptom descriptors, cough audio, spoken language, and chest radiographs and can operate fully offline on standard laptop class hardware.","one_line_summary":"The paper describes MultiSense-Pneumo, an offline-capable multimodal framework that fuses symptom triage, audio classification, speech recognition, and radiograph analysis for pneumonia screening in low-resource settings.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the normalized risk signals from each modality can be meaningfully aggregated into a unified screening estimate that improves triage decisions in real resource-constrained environments, an assumption stated in the abstract but without supporting performance data or validation studies.","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.02207/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-20T16:36:14.049358Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_title_agreement","ran_at":"2026-05-20T04:01:22.192113Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T16:37:17.852869Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"ffffb2e5e01b19d0a04a14630c179179f4afd866e3494d228d04f9b3e329baf5"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"06cbe69a1ed1397fe59b69856f91d15e5fa34f1a5e17eb31f69e76e7e4167abc"},"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":"2605.02207","created_at":"2026-05-27T01:04:58.650480+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.02207v2","created_at":"2026-05-27T01:04:58.650480+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.02207","created_at":"2026-05-27T01:04:58.650480+00:00"},{"alias_kind":"pith_short_12","alias_value":"DFFIPBNBAKZC","created_at":"2026-05-27T01:04:58.650480+00:00"},{"alias_kind":"pith_short_16","alias_value":"DFFIPBNBAKZCVRC3","created_at":"2026-05-27T01:04:58.650480+00:00"},{"alias_kind":"pith_short_8","alias_value":"DFFIPBNB","created_at":"2026-05-27T01:04:58.650480+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":2,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DFFIPBNBAKZCVRC336F7HUAPKK","json":"https://pith.science/pith/DFFIPBNBAKZCVRC336F7HUAPKK.json","graph_json":"https://pith.science/api/pith-number/DFFIPBNBAKZCVRC336F7HUAPKK/graph.json","events_json":"https://pith.science/api/pith-number/DFFIPBNBAKZCVRC336F7HUAPKK/events.json","paper":"https://pith.science/paper/DFFIPBNB"},"agent_actions":{"view_html":"https://pith.science/pith/DFFIPBNBAKZCVRC336F7HUAPKK","download_json":"https://pith.science/pith/DFFIPBNBAKZCVRC336F7HUAPKK.json","view_paper":"https://pith.science/paper/DFFIPBNB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.02207&json=true","fetch_graph":"https://pith.science/api/pith-number/DFFIPBNBAKZCVRC336F7HUAPKK/graph.json","fetch_events":"https://pith.science/api/pith-number/DFFIPBNBAKZCVRC336F7HUAPKK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DFFIPBNBAKZCVRC336F7HUAPKK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DFFIPBNBAKZCVRC336F7HUAPKK/action/storage_attestation","attest_author":"https://pith.science/pith/DFFIPBNBAKZCVRC336F7HUAPKK/action/author_attestation","sign_citation":"https://pith.science/pith/DFFIPBNBAKZCVRC336F7HUAPKK/action/citation_signature","submit_replication":"https://pith.science/pith/DFFIPBNBAKZCVRC336F7HUAPKK/action/replication_record"}},"created_at":"2026-05-27T01:04:58.650480+00:00","updated_at":"2026-05-27T01:04:58.650480+00:00"}