{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T06:22:05Z","timestamp":1787638925507,"version":"build-2736575974"},"reference-count":35,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T00:00:00Z","timestamp":1787616000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Pediatr."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Privacy-preserving integration of respiratory pathogen, host and treatment data remains difficult in preschool-aged children. We evaluated secure multi-party computation (SMC) as clinician-reviewed decision support for respiratory pathogen diagnosis and treatment planning, while distinguishing technical validation from clinical efficacy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This prospective observational study with a parallel in silico comparator was conducted at a tertiary paediatric centre in 2023 among children aged 3\u20136 years undergoing respiratory pathogen testing and treatment planning. A three-party SMC system using Brakerski-Gentry-Vaikuntanathan homomorphic encryption analysed pathogen markers, clinical features, immune status, inflammatory biomarkers and prespecified treatment rules. Diagnostic-support metrics were calculated against final clinician-documented diagnoses; the non-encrypted comparator assessed encryption overhead and output equivalence.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Among 1,380 enrolled children,\n                      <jats:italic>Mycoplasma pneumoniae<\/jats:italic>\n                      immunoglobulin M (IgM), an atypical bacterial marker, had the highest positivity rate (41.30%), followed by influenza B (9.13%) and influenza A (7.39%) virus IgM. The SMC system showed 94.7% diagnostic-support concordance, macro-AUROC 0.962, precision 94.0%, recall 95.1% and F1-score 94.5%. Mean processing time was 2.3\u2009\u00b1\u20090.5 seconds, uptime was 99.97% and no cryptographic failure or unauthorised access event was detected. Treatment outcomes were interpreted descriptively.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>SMC-based clinician-reviewed decision support was feasible for privacy-preserving auxiliary respiratory pathogen diagnosis and treatment planning in preschool-aged children at a single tertiary centre. The findings supported technical feasibility and reference-standard concordance but did not establish superiority over standard physician care. Broader multicentre, age-specific validation is required.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fped.2026.1798007","type":"journal-article","created":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T05:34:20Z","timestamp":1787636060000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Clinical research on secure multi-party computation technology for auxiliary respiratory pathogen diagnosis and treatment design in preschool-aged children"],"prefix":"10.3389","volume":"14","author":[{"given":"Xi","family":"Zhai","sequence":"first","affiliation":[{"name":"Department of Information Center, Hebei Children's Hospital","place":["Shijiazhuang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengqian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Information Center, Hebei Children's Hospital","place":["Shijiazhuang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,25]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"584","DOI":"10.1016\/j.cmi.2019.09.009","article-title":"Machine learning for clinical decision support in infectious diseases: a narrative review of current applications","volume":"26","author":"Peiffer-Smadja","year":"2020","journal-title":"Clin Microbiol Infect"},{"key":"B2","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1111\/cts.12884","article-title":"Precision medicine, AI, and the future of personalized health care","volume":"14","author":"Johnson","year":"2021","journal-title":"Clin Transl Sci"},{"key":"B3","first-page":"190","article-title":"Secure multi-party computation (SMPC) protocols and privacy. 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