{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T11:16:39Z","timestamp":1790507799064,"version":"4.1.0"},"reference-count":99,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T00:00:00Z","timestamp":1773360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Development Program of Henan Province","award":["262102241050"],"award-info":[{"award-number":["262102241050"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biosensors"],"abstract":"<jats:p>Alzheimer\u2019s disease (AD) is the most common cause of dementia, affecting 55 million people worldwide. Its characteristics include the accumulation of senile plaques and neurofibrillary tangles. This disease is associated with changes in the concentration of AD biomarkers, such as microRNAs, amyloid peptides, Tau protein, and neurofilament light chains. Due to the fact that neuropathological processes begin decades before the onset of cognitive symptoms, accurate detection of AD biomarkers is crucial for its early diagnosis. The combination of analytical techniques and machine learning methods plays a crucial role in medical innovation. Recently, efforts have been made to develop machine learning-assisted biosensors for AD diagnosis. This article provides an overview of the progress in machine learning-assisted sensing of AD biomarkers in bodily fluids. It mainly includes three parts: machine learning algorithms, machine learning-assisted electrochemical and optical biosensors, and challenges and future perspectives. We believe that this work will contribute to the development of innovative analytical devices based on artificial intelligence for monitoring and managing neurodegenerative diseases.<\/jats:p>","DOI":"10.3390\/bios16030161","type":"journal-article","created":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T15:06:22Z","timestamp":1773414382000},"page":"161","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Progress in Machine Learning-Assisted Biosensors for Alzheimer\u2019s Disease"],"prefix":"10.3390","volume":"16","author":[{"given":"Yan","family":"Feng","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Pingdingshan University, Pingdingshan 467000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changdong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Chemistry and Environmental Engineering, Pingdingshan University, Pingdingshan 467000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101580","DOI":"10.1016\/j.eclinm.2022.101580","article-title":"Global and regional projections of the economic burden of Alzheimer\u2019s disease and related dementias from 2019 to 2050: A value of statistical life approach","volume":"51","author":"Nandi","year":"2022","journal-title":"eClinicalMedicine"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2302673","DOI":"10.1002\/adfm.202302673","article-title":"Functional nanomaterials for the diagnosis of Alzheimer\u2019s disease: Recent progress and future perspectives","volume":"33","author":"Abdullah","year":"2023","journal-title":"Adv. Funct. Mater."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1038\/s41586-025-09841-y","article-title":"Prevalence of Alzheimer\u2019s disease pathology in the community","volume":"650","author":"Aarsland","year":"2026","journal-title":"Nature"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e2404685","DOI":"10.1002\/adhm.202404685","article-title":"Porous materials for early diagnosis of neurodegenerative diseases","volume":"14","author":"Arghavani","year":"2025","journal-title":"Adv. Healthc. Mater."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6039","DOI":"10.1038\/s41598-024-56489-1","article-title":"Predicting early Alzheimer\u2019s with blood biomarkers and clinical features","volume":"14","author":"AlMansoori","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Sarma, M., and Chatterjee, S. (2024). Etiology of late-onset Alzheimer\u2019s disease, biomarker efficacy, and the role of machine learning in stage diagnosis. Diagnostics, 14.","DOI":"10.3390\/diagnostics14232640"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4495\u22124519","DOI":"10.1021\/acssensors.4c01582","article-title":"Role of machine learning assisted biosensors in point-of-care testing for clinical decisions","volume":"9","author":"Bhaiyya","year":"2024","journal-title":"ACS Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3346\u22123364","DOI":"10.1021\/acssensors.0c01424","article-title":"Advancing biosensors with machine learning","volume":"5","author":"Cui","year":"2020","journal-title":"ACS Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"6351","DOI":"10.1039\/D1AN01148K","article-title":"Machine learning and chemometrics for electrochemical sensors: Moving forward to the future of analytical chemistry","volume":"146","author":"Puthongkham","year":"2021","journal-title":"Analyst"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Fabrizio, C., Termine, A., Caltagirone, C., and Sancesario, G. (2021). Artificial intelligence for Alzheimer\u2019s disease: Promise or challenge?. Diagnostics, 11.","DOI":"10.3390\/diagnostics11081473"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Singh, S.G., Das, D., Barman, U., and Saikia, M.J. (2024). Early Alzheimer\u2019s disease detection: A review of machine learning techniques for forecasting transition from mild cognitive impairment. Diagnostics, 14.","DOI":"10.3390\/diagnostics14161759"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"100343","DOI":"10.1016\/j.medntd.2024.100343","article-title":"A systematic review on recent methods on deep learning for automatic detection of Alzheimer\u2019s disease","volume":"25","author":"Chamakuri","year":"2025","journal-title":"Med. Nov. Technol. Devices"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"109912","DOI":"10.1109\/ACCESS.2024.3438081","article-title":"A review on machine learning approaches for diagnosis of Alzheimer\u2019s disease and mild cognitive impairment based on brain MRI","volume":"12","author":"Givian","year":"2024","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Chuah, J.H., Lai, K.W., Chow, C.-O., Gochoo, M., Dhanalakshmi, S., Wang, N., Bao, W., and Wu, X. (2023). Conventional machine learning and deep learning in Alzheimer\u2019s disease diagnosis using neuroimaging: A review. Front. Comput. Neurosci., 17.","DOI":"10.3389\/fncom.2023.1038636"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Saif, F.H., Al-Andoli, M.N., and Bejuri, W.M.Y.W. (2024). Explainable AI for Alzheimer detection: A Review of current methods and applications. Appl. Sci., 14.","DOI":"10.3390\/app142210121"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1145\/3344998","article-title":"Machine learning techniques for the diagnosis of Alzheimer\u2019s disease: A review","volume":"16","author":"Tanveer","year":"2020","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1186\/s13195-021-00900-w","article-title":"Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer\u2019s disease dementia: A systematic review","volume":"13","author":"Grueso","year":"2021","journal-title":"Alzheimer\u2019s Res. Ther."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"114100","DOI":"10.1016\/j.measurement.2023.114100","article-title":"Machine and deep learning approaches for alzheimer disease detection using magnetic resonance images: An updated review","volume":"226","author":"Menagadevi","year":"2024","journal-title":"Measurement"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"121373","DOI":"10.1016\/j.neuroimage.2025.121373","article-title":"Future of Alzheimer\u2019s detection: Advancing diagnostic accuracy through the integration of qEEG and artificial intelligence","volume":"317","author":"Rezaei","year":"2025","journal-title":"NeuroImage"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Christodoulou, R.C., Woodward, A., Pitsillos, R., Ibrahim, R., and Georgiou, M.F. (2025). Artificial intelligence in Alzheimer\u2019s disease diagnosis and prognosis using PET-MRI: A narrative review of high-impact literature post-tauvid approval. J. Clin. Med., 14.","DOI":"10.3390\/jcm14165913"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"101614","DOI":"10.1016\/j.arr.2022.101614","article-title":"Artificial intelligence in brain MRI analysis of Alzheimer\u2019s disease over the past 12 years: A systematic review","volume":"77","author":"Frizzell","year":"2022","journal-title":"Ageing Res. Rev."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"102943","DOI":"10.1016\/j.arr.2025.102943","article-title":"Magnetic resonance imaging analysis for Alzheimer\u2019s disease diagnosis using artificial intelligence: Methods, challenges, and opportunities","volume":"113","author":"Shi","year":"2026","journal-title":"Ageing Res. Rev."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2134","DOI":"10.4103\/1673-5374.367840","article-title":"Detection of Alzheimer\u2019s disease onset using MRI and PET neuroimaging: Longitudinal data analysis and machine learning","volume":"18","author":"Aberathne","year":"2023","journal-title":"Neural Regen. Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1464","DOI":"10.3390\/eng5030078","article-title":"Machine and deep learning trends in EEG-based detection and diagnosis of Alzheimer\u2019s disease: A systematic review","volume":"5","author":"Aviles","year":"2024","journal-title":"Eng"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1038\/s41582-020-0348-0","article-title":"An update on blood-based biomarkers for non-Alzheimer neurodegenerative disorders","volume":"16","author":"Ashton","year":"2020","journal-title":"Nat. Rev. Neurol."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lin, C.-H., Chiu, S.-I., Chen, T.-F., Jang, J.-S.R., and Chiu, M.-J. (2020). Classifications of neurodegenerative disorders using a multiplex blood biomarkers-based machine learning model. Int. J. Mol. Sci., 21.","DOI":"10.3390\/ijms21186914"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1038\/nature25456","article-title":"High performance plasma amyloid-\u03b2 biomarkers for Alzheimer\u2019s disease","volume":"554","author":"Nakamura","year":"2018","journal-title":"Nature"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"104415","DOI":"10.1016\/j.drudis.2025.104415","article-title":"Advancements and challenges in using AI for biomarker detection in early Alzheimer\u2019s disease","volume":"30","author":"Beheshti","year":"2025","journal-title":"Drug Discov. Today"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chang, C.-H., Lin, C.-H., and Lane, H.-Y. (2021). Machine learning and novel biomarkers for the diagnosis of Alzheimer\u2019s disease. Int. J. Mol. Sci., 22.","DOI":"10.3390\/ijms22052761"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Benjamin, S.R., Lima, F.D., Nunes, P.I.G., Dutra, R.F., Andrade, G.M.D., and Ori\u00e1, R.B. (2025). Advanced biosensing technologies: Leading innovations in Alzheimer\u2019s disease diagnosis. Chemosensors, 13.","DOI":"10.3390\/chemosensors13060220"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"883","DOI":"10.3233\/JAD-240126","article-title":"Prognostic power? Do the plasma biomarkers, neurofilament light and phospho-Tau 181, improve prediction of progression to Alzheimer\u2019s disease using a machine learning approach in the ADNI cohort?","volume":"99","author":"McIntire","year":"2024","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"104269","DOI":"10.1016\/j.medengphy.2024.104269","article-title":"Advances and future trends in the detection of beta-amyloid: A comprehensive review","volume":"135","author":"Ganguly","year":"2025","journal-title":"Med. Eng. Phys."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1007\/s13206-023-00105-3","article-title":"Recent advances in photoelectrochemical sensing of Alzheimer\u2019s biomarkers","volume":"17","author":"Kim","year":"2023","journal-title":"BioChip J."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.ijbiomac.2020.05.257","article-title":"Biosensing: The best alternative for conventional methods in detection of Alzheimer\u2019s disease biomarkers","volume":"161","author":"Mobeda","year":"2020","journal-title":"Int. J. Biol. Macromol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1080\/14737159.2021.1887732","article-title":"Nanomaterial-based optical and electrochemical biosensors for amyloid beta and Tau: Potential for early diagnosis of Alzheimer\u2019s disease","volume":"21","author":"Phan","year":"2021","journal-title":"Expert Rev. Mol. Diagn."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"6255","DOI":"10.20964\/2017.07.24","article-title":"Recent progress in electrochemical detection of beta-amyloid peptides and their aggregates","volume":"12","author":"Xia","year":"2017","journal-title":"Int. J. Electrochem. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Sharma, A., Angnes, L., Sattarahmady, N., Negahdary, M., and Heli, H. (2023). Electrochemical immunosensors developed for amyloid-beta and Tau proteins, leading biomarkers of Alzheimer\u2019s disease. Biosensors, 13.","DOI":"10.3390\/bios13070742"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"\u015eak, B., Sousa, H.B.A., and Prior, J.A.V. (2025). Carbon nanomaterial-based electrochemical biosensors for Alzheimer\u2019s disease biomarkers: Progress, challenges, and future perspectives. Biosensors, 15.","DOI":"10.3390\/bios15100684"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"102954","DOI":"10.1016\/j.arr.2025.102954","article-title":"Next-generation biosensor technologies for Alzheimer\u2019s disease: Innovations in diagnosis, monitoring, and treatment","volume":"114","author":"Yin","year":"2026","journal-title":"Ageing Res. Rev."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Shukla, A., Tiwari, R., and Tiwari, S. (2023). Review on Alzheimer disease detection methods: Automatic pipelines and machine learning techniques. Sci, 5.","DOI":"10.3390\/sci5010013"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.3233\/JAD-240301","article-title":"Identification of blood biomarkers related to energy metabolism and construction of diagnostic prediction model based on three independent Alzheimer\u2019s disease cohorts","volume":"100","author":"Wang","year":"2024","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1159","DOI":"10.1007\/s10439-024-03459-3","article-title":"A review of machine learning algorithms for biomedical applications","volume":"52","author":"Binson","year":"2024","journal-title":"Ann. Biomed. Eng."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1007\/s12525-021-00475-2","article-title":"Machine learning and deep learning","volume":"31","author":"Janiesch","year":"2021","journal-title":"Electron. Mark."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"120677","DOI":"10.1016\/j.cca.2025.120677","article-title":"Advancement of machine learning algorithms in biosensors","volume":"579","author":"Chin","year":"2026","journal-title":"Clin. Chim. Acta"},{"key":"ref_45","first-page":"977\u2212988","article-title":"Machine learning-enhanced hyperspectral Raman imaging for label-free molecular atlas of Alzheimer\u2019s brain","volume":"18","author":"Wang","year":"2026","journal-title":"ACS Appl. Mater. Interfaces"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1007\/s10109-018-0280-7","article-title":"Geographically weighted elastic net logistic regression","volume":"20","author":"Comber","year":"2018","journal-title":"J. Geogr. Syst."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"e70010","DOI":"10.1002\/ansa.70010","article-title":"A review of analytical and chemometric strategies for forensic classification of homemade explosives","volume":"6","author":"Aljanaahi","year":"2025","journal-title":"Anal. Sci. Adv."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1038\/s44172-024-00253-w","article-title":"SRS-Net: A universal framework for solving stimulated Raman scattering in nonlinear fiber-optic systems by physics-informed deep learning","volume":"3","author":"Song","year":"2024","journal-title":"Commun. Eng."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"156082","DOI":"10.1109\/ACCESS.2024.3482690","article-title":"Machine learning in RADAR-based physiological signals sensing: A scoping review of the models, datasets, and metrics","volume":"12","author":"Nocera","year":"2024","journal-title":"IEEE Access"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Verma, N., Qu, X., Trozzi, F., Zoltowski, B., Elsaied, M., Karki, N., Larson, E.C., and Kraka, E. (2021). SSnet: A deep learning approach for protein-ligand interaction prediction. Int. J. Mol. Sci., 22.","DOI":"10.3390\/ijms22031392"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"3112","DOI":"10.1039\/D5AN00452G","article-title":"PyFasma: An open-source, modular Python package for preprocessing and multivariate analysis of Raman spectroscopy data","volume":"150","author":"Pavlou","year":"2025","journal-title":"Analyst"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"8492\u22128500","DOI":"10.1021\/acs.analchem.4c00383","article-title":"RamanSPy: An open-source python package for integrative Raman spectroscopy data analysis","volume":"96","author":"Georgiev","year":"2024","journal-title":"Anal. Chem."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"65579","DOI":"10.1109\/ACCESS.2019.2916648","article-title":"Unsupervised machine learning for networking: Techniques, applications and research challenges","volume":"7","author":"Usama","year":"2019","journal-title":"IEEE Access"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"zsaf189","DOI":"10.1093\/sleep\/zsaf189","article-title":"Unsupervised machine learning in sleep research: A scoping review","volume":"48","author":"Biedebach","year":"2025","journal-title":"Sleep"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"118469","DOI":"10.1016\/j.trac.2025.118469","article-title":"Application of machine learning to improve the accuracy of electrochemical sensors: A review","volume":"193","author":"Li","year":"2025","journal-title":"TrAC Trends Anal. Chem."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"116861","DOI":"10.1016\/j.trac.2022.116861","article-title":"Analyzing chronic disease biomarkers using electrochemical sensors and artificial neural networks","volume":"158","author":"Sinha","year":"2023","journal-title":"TrAC Trends Anal. Chem."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"118592","DOI":"10.1016\/j.trac.2025.118592","article-title":"Advances and challenges in the application of artificial intelligence to medical biosensing technology: A review","volume":"195","author":"Lei","year":"2026","journal-title":"TrAC Trends Anal. Chem."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Bae, M., Kim, N., Cho, E., Lee, T., and Lee, J.-H. (2025). Recent advances in electrochemical biosensors for neurodegenerative disease biomarkers. Biosensors, 15.","DOI":"10.3390\/bios15030151"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Kang, M., Kim, D., Kim, J., Kim, N., and Lee, S. (2024). Strategies to enrich electrochemical sensing data with analytical relevance for machine learning applications: A focused review. Sensors, 24.","DOI":"10.3390\/s24123855"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2417520","DOI":"10.1002\/adma.202417520","article-title":"Machine-learning-aided advanced electrochemical biosensors","volume":"37","author":"Bocan","year":"2025","journal-title":"Adv. Mater."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"4131","DOI":"10.1038\/s41591-025-03965-4","article-title":"Primary care detection of Alzheimer\u2019s disease using a self-administered digital cognitive test and blood biomarkers","volume":"31","author":"Tideman","year":"2025","journal-title":"Nat. Med."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"171406","DOI":"10.1016\/j.cej.2025.171406","article-title":"High-velocity DNA nanoharvester-enabled ultrasensitive analysis: Decoding extracellular vesicles miRNA Assisted by machine learning for grading of Alzheimer\u2019s disease","volume":"526","author":"Cheng","year":"2025","journal-title":"Chem. Eng. J."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"8924","DOI":"10.1021\/acsami.4c18204","article-title":"Using machine learning to design a FeMOF bidirectional regulator for electrochemiluminescence sensing of Tau protein","volume":"17","author":"Yuan","year":"2025","journal-title":"ACS Appl. Mater. Interfaces"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"e03155","DOI":"10.1002\/adhm.202503155","article-title":"Redox-active polyphenol red molecularly imprinted polymers on porous gold electrodes for ultrasensitive, AI-assisted detection of Alzheimer\u2019s biomarkers in undiluted biofluids","volume":"14","author":"Deshpande","year":"2025","journal-title":"Adv. Healthc. Mater."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Zhang, L., Wang, D., Dai, Y., Wang, X., Cao, Y., Liu, W., and Tao, Z. (2022). Machine learning reveals a multipredictor nomogram for diagnosing the Alzheimer\u2019s disease based on chemiluminescence immunoassay for total Tau in plasma. Front. Aging Neurosci., 14.","DOI":"10.3389\/fnagi.2022.863673"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Le, P.G., Choi, S.H., and Cho, S. (2023). Alzheimer\u2019s disease biomarker detection using field effect transistor-based biosensor. Biosensors, 13.","DOI":"10.3390\/bios13110987"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"2300055","DOI":"10.1002\/anbr.202300055","article-title":"Orthogonal surface-enhanced Raman scattering\/field-effect transistor detection of breast and colorectal cancer-derived exosomes using graphene as a tag-free diagnostic template","volume":"3","author":"Gil","year":"2023","journal-title":"Adv. NanoBiomed Res."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"2117","DOI":"10.1021\/acsnano.3c09311","article-title":"High-performance plasma biomarker panel for Alzheimer\u2019s disease screening using a femtomolar-level label-free biosensing system","volume":"18","author":"Wang","year":"2023","journal-title":"ACS Nano"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1033\u22121043","DOI":"10.1021\/acs.analchem.5c06515","article-title":"Dopamine self-polymerization-assisted oriented antibody immobilization on a transistor for Alzheimer\u2019s disease diagnosis","volume":"98","author":"Gong","year":"2026","journal-title":"Anal. Chem."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"118488","DOI":"10.1016\/j.trac.2025.118488","article-title":"Label-free plasmonic biosensors for neurodegenerative disease diagnosis: Progress and perspectives","volume":"194","author":"Dun","year":"2026","journal-title":"TrAC Trends Anal. Chem."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"18464","DOI":"10.1039\/D5CC04899K","article-title":"Emerging trends in AI-integrated optical biosensors for point-of-care diagnostics: Current status and future prospects","volume":"61","author":"Subburaj","year":"2025","journal-title":"Chem. Commun."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"e00280","DOI":"10.1016\/j.teac.2025.e00280","article-title":"Artificial intelligence and machine learning for colorimetric detections: Techniques, applications, and future prospects","volume":"48","author":"Parakh","year":"2025","journal-title":"Trends Environ. Anal. Chem."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"2406196","DOI":"10.1002\/advs.202406196","article-title":"Integrated ultrasound-enrichment and machine learning in colorimetric lateral flow assay for accurate and sensitive clinical Alzheimer\u2019s biomarker diagnosis","volume":"11","author":"Wang","year":"2024","journal-title":"Adv. Sci."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"117722","DOI":"10.1016\/j.bios.2025.117722","article-title":"Small-molecule fluorescence probes for biomarkers of Alzheimer\u2019s disease","volume":"287","author":"Wang","year":"2025","journal-title":"Biosens. Bioelectron."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"161578","DOI":"10.1016\/j.cej.2025.161578","article-title":"Amplification-free detection of Porphyromonas gingivalis with no-PAM CRISPR-Cas12a using a smartphone-based platform","volume":"510","author":"Wang","year":"2025","journal-title":"Chem. Eng. J."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1315","DOI":"10.1021\/acssensors.2c00132","article-title":"Machine learning-assisted sensor array based on poly(amidoamine) (PAMAM) dendrimers for diagnosing Alzheimer\u2019s disease","volume":"7","author":"Xu","year":"2022","journal-title":"ACS Sens."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"2757","DOI":"10.1021\/acs.analchem.1c03623","article-title":"Machine learning-assisted pattern recognition of amyloid beta aggregates with fluorescent conjugated polymers and graphite oxide electrostatic complexes","volume":"94","author":"Wang","year":"2022","journal-title":"Anal. Chem."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"14315","DOI":"10.1039\/D5TB01103E","article-title":"An AI-assisted multiplex fluorescence sensing platform for grading diagnosis of Alzheimer\u2019s disease","volume":"13","author":"Liu","year":"2025","journal-title":"J. Mater. Chem. B"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"11879","DOI":"10.1039\/D4NR01413H","article-title":"Unveiling brain disorders using liquid biopsy and Raman spectroscopy","volume":"16","author":"Ranasinghe","year":"2024","journal-title":"Nanoscale"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"087002","DOI":"10.1117\/1.JBO.25.8.087002","article-title":"Surface-enhanced Raman spectroscopy of tears: Toward a diagnostic tool for neurodegenerative disease identification","volume":"25","author":"Cennamo","year":"2020","journal-title":"J. Biomed. Opt."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"117378","DOI":"10.1016\/j.trac.2023.117378","article-title":"Toward smart diagnostics via artificial intelligence-assisted surface-enhanced Raman spectroscopy","volume":"169","author":"Arce","year":"2023","journal-title":"TrAC Trends Anal. Chem."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Elsheikh, S., Coles, N.P., Achadu, O.J., Filippou, P.S., and Khundakar, A.A. (2024). Advancing brain research through surface-enhanced Raman spectroscopy (SERS): Current applications and future prospects. Biosensors, 14.","DOI":"10.3390\/bios14010033"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1186\/s40035-023-00367-9","article-title":"Impact of seed amplification assay and surface-enhanced Raman spectroscopy combined approach on the clinical diagnosis of Alzheimer\u2019s disease","volume":"12","author":"Cazzaniga","year":"2023","journal-title":"Transl. Neurodegener."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"6426","DOI":"10.1021\/acsnano.2c00538","article-title":"Rapid biomarker screening of Alzheimer\u2019s disease by interpretable machine learning and graphene-assisted Raman spectroscopy","volume":"16","author":"Wang","year":"2022","journal-title":"ACS Nano"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"4390","DOI":"10.1021\/acschemneuro.4c00369","article-title":"Ultrasensitive detection of blood-based Alzheimer\u2019s disease biomarkers: A comprehensive SERS-immunoassay platform enhanced by machine learning","volume":"15","author":"Resmi","year":"2024","journal-title":"ACS Chem. Neurosci."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"28757","DOI":"10.1021\/acsanm.4c06056","article-title":"Approach for label-free and high-accuracy identification of Alzheimer\u2019s disease combining machine learning and SERS technology","volume":"7","author":"Lu","year":"2024","journal-title":"ACS Appl. Nano Mater."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"123088","DOI":"10.1016\/j.saa.2023.123088","article-title":"PCA-WRKNN-assisted label-free SERS serum analysis platform enabling non-invasive diagnosis of Alzheimer\u2019s disease","volume":"302","author":"Li","year":"2023","journal-title":"Spectrochim. Acta Part A Mol. Biomol. Spectrosc."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"116128","DOI":"10.1016\/j.bios.2024.116128","article-title":"Surface-functionalized SERS platform for deep learning-assisted diagnosis of Alzheimer\u2019s disease","volume":"251","author":"Kim","year":"2024","journal-title":"Biosens. Bioelectron."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"118222","DOI":"10.1016\/j.bios.2025.118222","article-title":"Machine learning-assisted high-performance immunoSERS platform using silk fibroin as a natural etching mask for early diagnosis of Alzheimer\u2019s disease","volume":"294","author":"Lee","year":"2026","journal-title":"Biosens. Bioelectron."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1038\/s42003-021-01981-x","article-title":"Multimodal, label-free fluorescence and Raman imaging of amyloid deposits in snap-frozen Alzheimer\u2019s disease human brain tissue","volume":"4","author":"Lochocki","year":"2021","journal-title":"Commun. Biol."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"eaat7715","DOI":"10.1126\/sciadv.aat7715","article-title":"Label-free imaging of amyloid plaques in Alzheimer\u2019s disease with stimulated Raman scattering microscopy","volume":"4","author":"Ji","year":"2018","journal-title":"Sci. Adv."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"922","DOI":"10.1021\/acsbiomedchemau.5c00182","article-title":"A tutorial review on surface plasmon resonance biosensors: Applications in biomedicine","volume":"5","author":"Chirco","year":"2025","journal-title":"ACS Bio Med Chem Au"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"8472886","DOI":"10.1155\/ijo\/8472886","article-title":"Advances in fiber optic biosensing technologies for detection of neurological biomarkers: A narrative review","volume":"2025","author":"Alavian","year":"2025","journal-title":"Int. J. Opt."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"5221","DOI":"10.1007\/s00216-024-05367-w","article-title":"Trends in surface plasmon resonance biosensing: Materials, methods, and machine learning","volume":"416","author":"Stuart","year":"2024","journal-title":"Anal. Bioanal. Chem."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Hu, C., Guan, M., Mi, F., Zhang, S., and Geng, P. (2026). Multimodal SERS biosensing platforms: Emerging opportunities for ultrasensitive biomarker detection and intelligent diagnostics. ACS Sens., 40.","DOI":"10.1021\/acssensors.5c04741"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Ma, X., Ge, Y., and Xia, N. (2024). Overview of the design and application of dual-signal immunoassays. Molecules, 29.","DOI":"10.3390\/molecules29194551"},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Yu, X., Srivastava, S., Huang, S., Hayden, E., Teplow, D., and Xie, Y.-H. (2022). The feasibility of early Alzheimer\u2019s disease diagnosis using a neural network hybrid platform. Biosensors, 12.","DOI":"10.3390\/bios12090753"},{"key":"ref_98","first-page":"204","article-title":"Opening the black box: The promise and limitations of explainable machine learning in cardiology","volume":"38","author":"Petch","year":"2022","journal-title":"Can. J. Cardiol."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.cvdhj.2022.10.005","article-title":"Inside the \u201cblack box\u201d: Embedding clinical knowledge in data-driven machine learning for heart disease diagnosis","volume":"3","author":"Meng","year":"2022","journal-title":"Cardiovasc. Digit. Health J."}],"container-title":["Biosensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-6374\/16\/3\/161\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T15:21:03Z","timestamp":1773415263000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-6374\/16\/3\/161"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,13]]},"references-count":99,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["bios16030161"],"URL":"https:\/\/doi.org\/10.3390\/bios16030161","relation":{},"ISSN":["2079-6374"],"issn-type":[{"value":"2079-6374","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,13]]}}}