| 2 | 0 | 3 |
| 下载次数 | 被引频次 | 阅读次数 |
近年来，传染病持续威胁全球公共卫生安全。作为传染病诊疗的核心场所，医疗机构在患者救治和疫情防控方面面临着巨大的风险和压力。早期精准识别传染病是开展有效治疗和遏制疫情传播的基础。人工智能技术在多源异构医疗数据处理方面展现出显著优势，为传染病的早期筛查和智能诊断提供了新的技术手段。本文综述了人工智能在传染病识别领域的研究进展，重点探讨了人工智能在不同场景下早期识别传染病的潜力，并对未来发展趋势和技术挑战进行展望。
Abstract：In recent years, infectious diseases have persistently threatened global public health security. As the primary setting for infectious disease diagnosis and treatment, medical institutions face significant risks and pressure in patient care and epidemic prevention. Rapid and effective detection of infectious diseases is the basis for timely treatment and containment of the spread of epidemics. Artificial intelligence(AI) has demonstrated significant advantages in processing multi-source heterogeneous medical data, offering new technological pathways for the early screening and intelligent diagnosis of infectious diseases. This paper systematically reviews the research progress in the application of AI for AI-assisted disease detection. It specifically explores the potential of AI for early detection of infectious diseases in diverse scenarios, and discusses trends and technical challenges.
[1] GBD 2021 Demographics Collaborators. Global age-sex-specific mortality, life expectancy, and population estimates in 204countries and territories and 811 subnational locations, 1950-2021, and the impact of the COVID-19 pandemic:a comprehensive demographic analysis for the Global Burden of Disease Study 2021[J]. Lancet, 2024, 403:1989-2056. DOI:10.1016/S0140-6736(24)00476-8.
[2]梁文娟，张荣光，陆家海. 2024年猴痘的流行病学特征和预防治疗研究进展[J].传染病信息，2024,37(6):481-486.DOI:10.3969/j.issn.1007-8134.2024.06.001.
[3]王文鑫，福军亮，王福生.猴痘疫情现状及防控挑战[J].传染病信息，2022,35(3):197-201. DOI:10.3969/j.issn.1007-8134.2022.03.003.
[4]国家疾病预防控制局. 2023年全国法定传染病疫情概况[EB/OL].(2024-09-18)[2024-12-01]. https://www.ndcpa.gov.cn/jbkzzx/c100016/common/content/content_1836299733133275136.html.
[5] Chen Z, Tsui JL, Gutierrez B, et al. COVID-19 pandemic interventions reshaped the global dispersal of seasonal influenza viruses[J]. Science, 2024, 386:eadq3003. DOI:10.1126/science.adq3003.
[6] World Health Organization. Dengue-global situation[EB/OL].(2023-10-21)[2024-12-01]. https://www.who.int/emergencies/disease-outbreak-news/item/2023-DON498.
[7] Mahon MB, Sack A, Aleuy OA, et al. A meta-analysis on global change drivers and the risk of infectious disease[J]. Nature,2024, 629:830-836. DOI:10.1038/s41586-024-07380-6.
[8] Abraham H.The impact of COVID-19 on health and health systems[J]. Ethiop J Health Sci, 2021, 31(6):1073-1074. DOI:10.4314/ejhs.v31i6.1.
[9] Ahuja V, Nair LV. Artificial intelligence and technology in COVID Era:a narrative review[J]. J Anaesthesiol Clin Pharmacol, 2021,37(1):28-34. DOI:10.4103/joacp.JOACP_558_20.
[10] Al Meslamani AZ, Sobrino I, de la Fuente J. Machine learning in infectious diseases:potential applications and limitations[J]. Ann Med, 2024, 56(1):2362869. DOI:10.1080/07853890.2024.2362869.
[11] Zhang H, Li L, Liu Y, et al. Serum cytokine biosignatures for identification of tuberculosis among HIV-positive inpatients[J].Thorax, 2024, 79:465-471. DOI:10.1136/thorax-2023-220782.
[12] Chen X, Wang X, Zhang K, et al.Recent advances and clinical applications of deep learning in medical image analysis[J]. Med Image Anal, 2022, 79:102444. DOI:10.1016/j.media.2022.102444.
[13] Huang DM, Huang J, Qiao K, et al. Deep learning-based lung sound analysis for intelligent stethoscope[J]. Mil Med Res,2023, 10(1):44-44. DOI:10.1186/s40779-023-00479-3.
[14] Mermin-Bunnell K, Zhu Y, Hornback A, et al. Use of natural language processing of patient-initiated electronic health record messages to identify patients with COVID-19 infection[J]. JAMA Netw Open, 2023, 6(7):e2322299. DOI:10.1001/jamanetworkopen.2023.22299.
[15] Omar M, Brin D, Glicksberg B, et al. Utilizing natural language processing and large language models in the diagnosis and prediction of infectious diseases:a systematic review[J].Am J Infect Control, 2024, 52(9):992-1001. DOI:10.1016/j.ajic.2024.03.016.
[16]国家卫生健康委.关于发布《电子病历共享文档规范第1部分：病历概要》等57项卫生行业标准的通告[EB/OL].(2016-08-23)[2024-12-01]. http://www.nhc.gov.cn/fzs/s7852d/201609/37f11aacca5a49c2ad0984c8fc7a2873.shtml.
[17] Tang AS, Woldemariam SR, Miramontes S, et al. Harnessing EHR data for health research[J]. Nature Med, 2024, 30(7):1847-1855. DOI:10.1038/s41591-024-03074-8.
[18] Feller DJ, Zucker J, Yin MT, et al. Using clinical notes and natural language processing for automated HIV risk assessment[J]. J Acquir Immune Defic Syndr, 2018, 77(2):160-166. DOI:10.1097/QAI.0000000000001580.
[19]马家奇，王松旺，郭青，等.国家传染病智能监测预警前置软件总体设计与实施[J].中国数字医学，2025,20(3):89-95. DOI:10.3969/j.issn.1673-7571.2025.03.013.
[20] Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[J]arXiv, 2017. DOI:10.48550/arXiv.1706.03762.
[21] Annapragada AV, Donaruma-Kwoh MM, Annapragada AV, et al.Large language model symptom identification from clinical text:a multi-center study[J]. medRxiv, 2024, 16(2):e0247404. DOI:10.1371/journal.pone.0247404.
[22] Hao B, Hu Y, Adams WG, et al. A GPT-based EHR modeling system for unsupervised novel disease detection[J]. J Biomed Inform, 2024, 157:104706. DOI:10.1016/j.jbi.2024.104706.
[23] Codlin AJ, Dao TP, Vo LNQ, et al. Independent evaluation of 12artificial intelligence solutions for the detection of tuberculosis[J].Sci Rep, 2021, 11(1):23895. DOI:10.1038/s41598-021-03265-0.
[24] World Health Organization. WHO consolidated guidelines on tuberculosis module 2:screening-systematic screening for tuberculosis disease[EB/OL].(2021-03-22)[2024-12-01].https://www.who.int/publications/i/item/9789240022676.
[25]帕哈提江·尼加提，马依迪丽·尼加提，阿布都克尤木江·阿布力孜.喀什地区智能肺结核影像云平台的开发与应用[J].中国数字医学，2023,18(9):100-103. DOI:10.3969/j.issn.1673-7571.2023.09.020.
[26] Choi SY, Choi A, Baek SE, et al. Effect of multimodal diagnostic approach using deep learning-based automated detection algorithm for active pulmonary tuberculosis[J]. Sci Rep, 2023,13(1):19794. DOI:10.1038/s41598-023-47146-0.
[27] Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics:the bridge between medical imaging and personalized medicine[J].Nat Rev Clin Oncol, 2017, 14(12):749-762. DOI:10.1038/nrclinonc.2017.141.
[28] Hu Y, Zhong L, Liu H, et al. Lung CT-based multi-lesion radiomic model to differentiate between nontuberculous mycobacteria and Mycobacterium tuberculosis[J]. Med Phys, 2024, 52(2):1086-1095. DOI:10.1002/mp.17537.
[29] Wang Z, Li F, Cai J, et al. Identification of lesion bioactivity in hepatic cystic echinococcosis using a transformer-based fusion model[J]. J Infect, 2025, 90(4):106455. DOI:10.1016/j.jinf.2025.106455.
[30] Ren B, Wang J, Miao Z, et al. Hepatic alveolar echinococcosis:predictive biological activity based on radiomics of MRI[J]. Biomed Res Int, 2021, 2021:6681092. DOI:10.1155/2021/6681092.
[31] Yang D, Ren G, Ni R, et al. Deep learning attention-guided radiomics for COVID-19 chest radiograph classification[J].Quant Imaging Med Surg, 2023, 13(2):572-584. DOI:10.21037/qims-22-531.
[32] Fusco R, Granata V, Grazzini G, et al. Radiomics in medical imaging:pitfalls and challenges in clinical management[J]. Jap JRadio,2022, 40(9):919-929. DOI:10.1007/s11604-022-01271-4.
[33] Cadamuro J. Rise of the machines:the inevitable evolution of medicine and medical laboratories intertwining with artificial intelligence—a narrative review[J]. Diagnostics(Basel), 2021,11(8):1399. DOI:10.3390/diagnostics11081399.
[34] Brenton L, Waters MJ, Stanford T, et al. Clinical evaluation of the APAS?Independence:Automated imaging and interpretation of urine cultures using artificial intelligence with composite reference standard discrepant resolution[J]. J Microbiol Methods, 2020,177:106047. DOI:10.1016/j.mimet.2020.106047.
[35] Culbreath K, Piwonka H, Korver J, et al. Benefits derived from full laboratory automation in microbiology:a tale of four laboratories[J]. J Clin Microbiol, 2020, 59(3):e01969-20. DOI:10.1128/JCM.01969-20.
[36] Ferrari A, Lombardi S, Signoroni A. Bacterial colony counting with convolutional neural networks in digital microbiology imaging[J]. Pattern Recognition, 2017, 61:629-640. DOI:10.1016/j.patcog.2016.07.016.
[37] Alberto S, Alessandro F, Stefano L, et al. Hierarchical AI enables global interpretation of culture plates in the era of digital microbiology[J]. Nat Commun, 2023, 14(1):6874. DOI:10.1038/s41467-023-42563-1.
[38] Sun H, Xiong L, Huang Y, et al. AI-aided on-chip nucleic acid assay for smart diagnosis of infectious disease[J]. Fundam Res,2022, 2(3):476-486. DOI:10.1016/j.fmre.2021.12.005.
[39] Dahiya A, Raghuvanshi D, Sharma C, et al. Machine and deep learning methods in identifying malaria through microscopic blood smear:a systematic review[J]. ArtifIntell Med, 2025,163:103114. DOI:10.1016/j.artmed.2025.103114.
[40] Mujahid M, Rustam F, Shafique R, et al. Efficient deep learningbased approach for malaria detection using red blood cell smears[J].Sci Rep, 2024, 14(1):13249. DOI:10.1038/s41598-024-63831-0.
[41] Horning MP, Delahunt CB, Bachman CM, et al. Performance of a fully-automated system on a WHO malaria microscopy evaluation slide set[J]. Malar J, 2021, 20(1):110. DOI:10.1186/s12936-021-03631-3.
[42] Wang G, Luo G, Lian H, et al. Application of deep learning in clinical settings for detecting and classifying malaria parasites in thin blood smears[J]. Open Forum Infect Dis, 2023,10(11):ofad469. DOI:10.1093/ofid/ofad469.
[43] Liu Y, Wang R, Zhang C, et al. automated diagnosis and phenotyping of tuberculosis using serum metabolic fingerprints[J]. AdvSci(Weinh), 2024, 11(39):e2406233. DOI:10.1002/advs.202406233.
[44] Pahar M, Klopper M, Reeve B, et al. Automatic cough classification for tuberculosis screening in a real-world environment[J].Physiol Meas, 2021, 42(10):10.1088/1361-6579/ac2fb8. DOI:10.1088/1361-6579/ac2fb8.
[45] Nguyen LH, Pham NT, Do VH, et al. Fruit-CoV:An efficient vision-based framework for speedy detection and diagnosis of SARS-CoV-2 infections through recorded cough sounds[J]. Expert Syst Appl, 2023, 213:119212. DOI:10.1016/j.eswa.2022.119212.
[46] Baur S, Nabulsi Z, Weng WH, et al. HeAR--health acoustic representations[EB/OL].(2024-03-04)[2025-01-20]. https://arxiv.org/pdf/2403.02522.
[47] Kim Y, Hyon Y, Jung SS, et al. Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning[J]. Sci Rep, 2021, 11(1):17186. DOI:10.1038/s41598-021-96724-7.
[48] Zhou G, Liu C, Li X, et al. An open auscultation dataset for machine learning-based respiratory diagnosis studies[J]. JASA Express Lett, 2024, 4(5):052001. DOI:10.1121/10.0025851.
[49] Bae S, Kim JW, Cho WY, et al. Patch-mix contrastive learning with audio spectrogram transformer on respiratory sound classification[EB/OL].(2024-12-26)[2025-01-20]. https://arxiv.org/pdf/2305.14032
[50] Dvijotham KD, Winkens J, Barsbey M, et al. Enhancing the reliability and accuracy of AI-enabled diagnosis via complementarity-driven deferral to clinicians[J]. Nat Med,2023, 29(7):1814-1820. DOI:10.1038/s41591-023-02437-x.
[51] Ennab M, Mcheick H. Enhancing interpretability and accuracy of AI models in healthcare:a comprehensive review on challenges and future directions[J]. Front Rob AI, 2024, 11:1444763. DOI:10.3389/frobt.2024.1444763.
[52]工业和信息化部.国家人工智能产业综合标准化体系建设指南（2024版）[EB/OL].(2024-06-05)[2024-12-01]. https://www.gov.cn/zhengce/zhengceku/202407/P020240702716282797987.pdf.
[53] European Union. Artificial Intelligence Act[EB/OL].(2024-08-01)[2025-03-01]. https://eur-lex.europa.eu/eli/reg/2024/1689/oj.
[54] Bensoussan Y, Elemento O, Rameau A. Voice as an AI biomarker of health—introducing audiomics[J]. JAMA Otolaryngol Head Neck Surg, 2024, 150(4):283-284. DOI:10.1001/jamaoto.2023.4807.
[55] Thieme AH, Zheng Y, Machiraju G, et al. A deep-learning algorithm to classify skin lesions from mpox virus infection[J]. Nat Med,2023, 29(3):738-747. DOI:10.1038/s41591-023-02225-7.
[56] Ponomarchuk A, Burenko I, Malkin E, et al. Project Achoo:a practical model and application for COVID-19 detection from recordings of breath, voice, and cough[J]. IEEE J Sel Top Signal Process, 2022, 16(2):175-187. DOI:10.1109/JSTSP.2022.3142514.
基本信息:
DOI：
中图分类号:TP18;R51
引用信息:
[1]罗瀛宇,范培杨,周威龙等.人工智能在医疗机构传染病早期识别中的应用研究进展[J].传染病信息,2025,38(03):307-312.
基金信息:
四川省科技厅项目（2024YFHZ0123）; 成都市科技局项目（2022YF0501792SN）