Applications and Challenges of Integrating Artificial Intelligence with Clinical and Multi-omics Data in Stroke Prevention, Treatment, and Pharmaceutical Research and Development

doi:10.3969/j.issn.1673-5765.2025.06.006

Abstract

Abstract: Stroke is the leading global cause of death and disability. The research paradigm that integrates artificial intelligence (AI) with clinical and multi-omics data provides novel solutions for the precision prevention and treatment of stroke. By integrating and analyzing clinical and multi-omics data, AI technology enhances the identification of high-risk populations, optimizes early diagnosis and risk assessment, enables precise subtyping of stroke, facilitates the screening of potential drug targets, and constructs prognostic prediction models. However, critical challenges, such as insufficient multi-omics resources, difficulties in multi modal data integration, and limited interpretability of algorithms, remain major bottlenecks in clinical translation. This article reviews the advances and challenges in AI-driven integration of clinical and multi-omics data in stroke prevention and treatment as well as pharmaceutical research and development.

Key words: Stroke prevention and treatment; Artificial intelligence; Multi-omics; Precision medicine; Pharmaceutical research and development

摘要： 卒中是全球致死致残的首要病因。基于人工智能（artificial intelligence，AI）融合临床与多组学数据的研究范式，为卒中精准防治提供了新方案。AI技术通过整合分析临床与多组学数据，能够提升卒中高危人群识别能力，优化早期诊断与风险评估，实现卒中亚型的精准分型，同时可筛选潜在药物靶点，构建预后预测模型。然而，卒中多组学资源相对匮乏、多模态数据融合困难，以及算法可解释性不足等仍是临床转化的主要瓶颈。本文对AI融合临床与多组学数据在卒中防治及医药研发中的应用与挑战等研究进展进行综述。

关键词: 卒中防治; 人工智能; 多组学; 精准医疗; 医药研发

CLC Number:

GOU Lan, JIANG Minghui, JIANG Yong, LIAO Xiaoling, LI Hao, ZHANG Jie, CHENG Si. Applications and Challenges of Integrating Artificial Intelligence with Clinical and Multi-omics Data in Stroke Prevention, Treatment, and Pharmaceutical Research and Development[J]. Chinese Journal of Stroke, 2025, 20(6): 710-717.

勾岚, 姜明慧, 姜勇, 廖晓凌, 李昊, 张杰, 程丝. 人工智能融合临床与多组学数据在卒中防治及医药研发中的应用与挑战[J]. 中国卒中杂志, 2025, 20(6): 710-717.

References

[1] GBD 2021 Stroke Risk Factor Collaborators. Global，regional，and national burden of stroke and its risk factors，1990—2021：a systematic analysis for the global burden of disease study 2021[J]. Lancet Neurol，2024，23（10）：973-1003.
[2] 《中国脑卒中防治报告2021》编写组. 《中国脑卒中防治报告2021》概要[J]. 中国脑血管病杂志，2023，20（11）：783-793.
Report on Stroke Prevention and Treatment in China Writing Group. Brief report on stroke prevention and treatment in China，2021[J]. Chin J Cerebrovasc Dis，2023，20（11）：783-793.
[3] 刘斯洋，林星辰，程丝，等. 多组学大数据与医学发展[J]. 科技导报，2024，42（12）：51-74.
LIU S Y，LIN X C，CHENG S，et al. Multi-omics big data and medical advancements[J]. Sci Technol Rev，2024，42（12）：51-74.
[4] GOODWIN S，MCPHERSON J D，MCCOMBIE W R. Coming of age：ten years of next-generation sequencing technologies[J]. Nat Rev Genet，2016，17（6）：333-351.
[5] ALONSO-BETANZOS A，BOLÓN-CANEDO V. Big-data analysis，cluster analysis，and machine-learning approaches[J/OL]. Adv Exp Med Biol，2018，1065：607-626[2025-02-19]. https://doi.org/10.1007/978-3-319-77932-4_37.
[6] CUI C，YANG H C，WANG Y H，et al. Deep multimodal
fusion of image and non-image data in disease diagnosis and prognosis：a review[J/OL]. Prog Biomed Eng（Bristol），2023，5（2）：10.1088/2516-1091/acc2fe[2025-02-19]. https://doi.org/10.1088/2516-1091/acc2fe.
[7] KANG M，KO E，MERSHA T B. A roadmap for multi-omics data integration using deep learning[J/OL]. Brief Bioinform，2022，23（1）：bbab454[2025-02-19]. https://doi.org/10.1093/bib/bbab454.
[8] LIPKOVA J，CHEN R J，CHEN B W，et al. Artificial intelligence for multimodal data integration in oncology[J]. Cancer Cell，2022，40（10）：1095-1110.
[9] SHAFAAT O，BERNSTOCK J D，SHAFAAT A，et al. Leveraging artificial intelligence in ischemic stroke imaging[J]. J Neuroradiol，2022，49（4）：343-351.
[10] QUISPE-OROZCO D，FAROOQUI M，ZEVALLOS C，et al. Angiography suite cone-beam computed tomography perfusion imaging in large-vessel occlusion patients using RAPID software：a pilot study[J/OL]. Stroke，2021，52（9）：e542-e544[2025-02-19]. https://doi.org/10.1161/STROKEAHA.121.035992.
[11] BOUTS M J R J，TIEBOSCH I A C W，VAN DER TOORN A，et al. Early identification of potentially salvageable tissue with MRI-based predictive algorithms after experimental ischemic stroke[J]. J Cereb Blood Flow Metab，2013，33（7）：1075-1082.
[12] RAJPURKAR P，LUNGREN M P. The current and future state of AI interpretation of medical images[J]. N Engl J Med，2023，388（21）：1981-1990.
[13] LI W T，SHAO C Y，ZHOU H F，et al. Multi-omics research strategies in ischemic stroke：a multidimensional perspective[J/OL]. Ageing Res Rev，2022，81：101730[2025-02-19]. https://doi.org/10.1016/j.arr.2022.101730.
[14] VAN SCHALKWIJK D B，DE GRAAF A A，TSIVTSIVADZE E，et al. Lipoprotein metabolism indicators improve cardiovascular risk prediction[J/OL]. PLoS One，2014，9（3）：e92840[2025-02-19].
https://doi.org/10.1371/journal.pone.0092840.
[15] FAN Y S，SONG Z S，ZHANG M Q. Emerging frontiers of artificial intelligence and machine learning in ischemic stroke：a comprehensive investigation of state-of-the-art methodologies，clinical applications，and unraveling challenges[J]. EPMA J，2023，14（4）：645-661.
[16] NTAIOS G，BAUMGARTNER H，DOEHNER W，et al. Embolic strokes of undetermined source：a clinical consensus statement of the ESC Council on Stroke，the European Association of Cardiovascular Imaging and the European Heart Rhythm Association of the ESC[J]. Eur Heart J，2024，45（19）：1701-1715.
[17] KLEINDORFER D O，TOWFIGHI A，CHATURVEDI S，et al. 2021 guideline for the prevention of stroke in patients with stroke and transient ischemic attack：a guideline from the American Heart Association/American Stroke Association[J/OL]. Stroke，52（7），e364-e467[2025-02-19]. https://doi.org/10.1161/STR.0000000000000375.
[18] DING L L，LIU C，LI Z X，et al. Incorporating artificial intelligence into stroke care and research[J/OL]. Stroke，2020，51（12）：e351-e354[2025-02-19]. https://doi.org/10.1161/STROKEAHA.120.031295.
[19] HOU Y C C，YU H C，MARTIN R，et al. Precision medicine integrating whole-genome sequencing，comprehensive metabolomics，and advanced imaging[J]. Proc Natl Acad Sci U S A，2020，117（6）：3053-3062.
[20] LIU Y，WANG W L，CUI X，et al. Exploring genetic associations of 3 types of risk factors with ischemic stroke：an integrated bioinformatics study[J]. Stroke，2024，55（6）：1619-1628.
[21] BARAGETTI A，GRIGORE L，OLMASTRONI E，et al. Plasma proteins associate with carotid plaques and predict incident atherosclerotic cardiovascular events[J/OL]. Vascul Pharmacol，2024，156：107394[2025-02-19]. https://doi.org/10.1016/j.vph.2024.107394.
[22] THEOFILATOS K，STOJKOVIC S，HASMAN M，et al. Proteomic atlas of atherosclerosis：the contribution of proteoglycans to sex differences，plaque phenotypes，and outcomes[J]. Circ Res，2023，133（7）：542-558.
[23] ZHANG F，ZHANG Y，ZHOU Q，et al. Using machine learning to identify proteomic and metabolomic signatures of stroke in atrial fibrillation[J/OL]. Comput Biol Med，2024，173：108375[2025-02-19]. https://doi.org/10.1016/j.compbiomed.2024.108375.
[24] SONG J W，ZAIDI S A A，HE L G，et al. Integrative analysis of machine learning and molecule docking simulations for ischemic stroke diagnosis and therapy[J/OL]. Molecules，2023，28（23）：7704[2025-02-19]. https://doi.org/10.3390/molecules28237704.
[25] FAN J X，CHEN M Y，CAO S，et al. Identification of a ferroptosis-related gene pair biomarker with immune infiltration landscapes in ischemic stroke：a bioinformatics-based comprehensive study[J/OL]. BMC Genomics，2022，23（1）：59[2025-02-19]. https://doi.org/10.1186/s12864-022-08295-0.
[26] QIN X H，YI S F，RONG J T，et al. Identification of anoikis-related genes classification patterns and immune infiltration characterization in ischemic stroke based on machine learning[J/OL]. Front Aging Neurosci，2023，15：1142163[2025-02-19]. https://doi.org/10.3389/fnagi.2023.1142163.
[27] REN P，WANG J Y，CHEN H L，et al. Diagnostic model constructed by nine inflammation-related genes for diagnosing ischemic stroke and reflecting the condition of immune-related cells[J/OL]. Front Immunol，2022，13：1046966[2025-02-19]. https://doi.org/10.3389/fimmu.2022.1046966.
[28] ZOMPOLA A，KORFIATI A，THEOFILATOS K，et al. Omics-CNN：a comprehensive pipeline for predictive analytics in quantitative omics using one-dimensional convolutional neural networks[J/OL]. Heliyon，2023，9（11）：e21165[2025-02-19]. https://doi.org/10.1016/j.heliyon.2023.e21165.
[29] TIEDT S，BRANDMAIER S，KOLLMEIER H，et al. Circulating metabolites differentiate acute ischemic stroke from stroke mimics[J]. Ann Neurol，2020，88（4）：736-746.
[30] LEE H J，SCHWAMM L H，SANSING L H，et al. StrokeClassifier：ischemic stroke etiology classification by ensemble consensus modeling using electronic health records[J/OL]. NPJ Digit Med，2024，7（1）：130[2025-02-19]. https://doi.org/10.1038/s41746-024-01120-w.
[31] BANG J H，KIM E H，KIM H J，et al. Machine learning-based etiologic subtyping of ischemic stroke using circulating exosomal microRNAs[J/OL]. Int J Mol Sci，2024，25（12）：6761[2025-02-19]. https://doi.org/10.3390/ijms25126761.
[32] LI W，BAI X S，HAO J H，et al. Thrombosis origin identification of cardioembolism and large artery atherosclerosis by distinct metabolites[J]. J Neurointerv Surg，2023，15（7）：701-707.
[33] ZHANG J X，ZHANG B B，LI T T，et al. Exploring the shared biomarkers between cardioembolic stroke and atrial fibrillation by WGCNA and machine learning[J/OL]. Front Cardiovasc Med，2024，11：1375768
[2025-02-19]. https://doi.org/10.3389/fcvm.2024.1375768.
[34] WANG X，MENG X Y，MENG L B，et al. Joint efficacy of the three biomarkers SNCA，GYPB and HBG1 for atrial fibrillation and stroke：analysis via the support vector machine neural network[J]. J Cell Mol Med，2022，26（7）：2010-2022.
[35] DING L L，LIU Y，MENG X，et al. Biomarker and genomic analyses reveal molecular signatures of non-cardioembolic ischemic stroke[J/OL]. Signal Transduct Target Ther，2023，8（1）：222[2025-02-19]. https://doi.org/10.1038/s41392-023-01465-w.
[36] MARTHA S R，LEVY S H，FEDERICO E，et al. Machine learning analysis of the cerebrovascular thrombi lipidome in acute ischemic stroke[J]. J Neurosci Nurs，2023，55（1）：10-17.
[37] AMENT Z，BEVERS M B，WOLCOTT Z，et al. Uric acid and gluconic acid as predictors of hyperglycemia and cytotoxic injury after stroke[J]. Transl Stroke Res，2021，12（2）：293-302.
[38] ZHAO Y W，MA X Y，MENG X H，et al. Integrating machine learning and single-cell transcriptomic analysis to identify potential biomarkers and analyze immune features of ischemic stroke[J/OL]. Sci Rep，2024，14（1）：26069[2025-02-19]. https://doi.org/10.1038/s41598-024-77495-3.
[39] HUANG D，ZHU Y，SHEN J F，et al. Identification of potential neddylation-related key genes in ischemic stroke based on machine learning methods[J]. Mol Neurobiol，2024，61（5）：2530-2541.
[40] YU H Y，JI X Y，OUYANG Y. Unfolded protein response pathways in stroke patients：a comprehensive landscape assessed through machine learning algorithms and experimental verification[J/OL]. J Transl Med，2023，21（1）：759[2025-02-19]. https://doi.org/10.1186/s12967-023-04567-9.
[41] LUO J，CAI Y，XIAO P，et al. Inflammation-derived and clinical indicator-based predictive model for ischemic stroke recovery[J/OL]. J Am Heart Assoc，2024，13（15）：e035609[2025-02-19]. https://doi.org/10.1161/JAHA.124.035609.
[42] CHI N F，CHANG T H，LEE C Y，et al. Untargeted metabolomics predicts the functional outcome of ischemic stroke[J]. J Formos Med Assoc，2021，120（1 Pt 1）：234-241.
[43] CHEN S D，XU Z，YIN J F，et al. Predicting functional outcome in ischemic stroke patients using genetic，environmental，and clinical factors：a machine learning analysis of population-based prospective cohort study[J/OL]. Brief Bioinform，2024，25（6）：bbae487[2025-02-19]. https://doi.org/10.1093/bib/bbae487.
[44] SHAMEER K，JOHNSON K W，GLICKSBERG B S，et al. Machine learning in cardiovascular medicine：are we there yet？[J]. Heart，2018，104（14）：1156-1164.
[45] RATWANI R M，BENDA N C，HETTINGER A Z，et al. Electronic health record vendor adherence to usability certification requirements and testing standards[J]. JAMA，2015，314（10）：1070-1071.
[46] MULLARD A. The UK biobank at 20[J]. Nat Rev Drug Discov，2022，21（9）：628-629.
[47] DEVINENI D，AKBARPOUR M，GONG Y F，et al. Inadequate use of newer treatments and glycemic control by cardiovascular risk and sociodemographic groups in US adults with diabetes in the NIH precision medicine initiative all of us research program[J]. Cardiovasc Drugs Ther，2024，38（2）：347-357.
[48] REEL P S，REEL S，PEARSON E，et al. Using machine learning approaches for multi-omics data analysis：a review[J/OL]. Biotechnol Adv，2021，49：107739[2025-02-19]. https://doi.org/10.1016/j.biotechadv.2021.107739.
[49] TANVIR R B，ISLAM M M，SOBHAN M，et al. MOGAT：a multi-omics integration framework using graph attention networks for cancer subtype prediction[J/OL]. Int J Mol Sci，2024，25（5）：2788[2025-02-19]. https://doi.org/10.3390/ijms25052788.
[50] WANG X H，YE S S，FENG J W，et al. Performance of ChatGPT on prehospital acute ischemic stroke and large vessel occlusion（LVO）stroke screening[J/OL]. Digit Health，2024，10：20552076241297127[2025-02-19]. https://doi.org/10.1177/20552076241297127.
[51] LEHNEN N C，DORN F，WIEST I C，et al. Data extraction from free-text reports on mechanical thrombectomy in acute ischemic stroke using ChatGPT：a retrospective analysis[J/OL]. Radiology，2024，311（1）：e232741[2025-02-19]. https://doi.org/10.1148/radiol.232741.
[52] CHEN T C，COULDWELL M W，SINGER J，et al. Assessing the clinical reasoning of ChatGPT for mechanical thrombectomy in patients with stroke[J]. J Neurointerv Surg，2024，16（3）：253-260.
[53] 东软医疗：DeepSeek强势来袭，AI赋能医疗新高度[EB/OL].（2025-02-10）[2025-02-19]. http://www.zhonghongwang.com/show-140-371405-1.html.
[54] KLAUSCHEN F，DIPPEL J，KEYL P，et al. Toward explainable artificial intelligence for precision pathology[J/OL]. Annu Rev Pathol，2024，19：541-570[2025-02-19]. https://doi.org/10.1146/annurev-pathmechdis-051222-113147.
[55] AN N A，ZHANG J，MO F，et al. De novo genes with an lncRNA origin encode unique human brain developmental functionality[J]. Nat Ecol Evol，2023，7（2）：264-278.
[56] KÖNIG H，FRANK D，BAUMANN M，et al. AI models and the future of genomic research and medicine：true sons of knowledge？[J/OL]. Bioessays，2021，43（10）：e2100025[2025-02-19]. https://doi.org/10.1002/bies.202100025.
[57] HENRY K E，KORNFIELD R，SRIDHARAN A，et al. Human-machine teaming is key to AI adoption：clinicians’ experiences with a deployed machine learning system[J/OL]. NPJ Digit Med，2022，5（1）：97[2025-02-19]. https://doi.org/10.1038/s41746-022-00597-7.