Increasing precision of clinical diagnosis of Alzheimer’s disease using a combined algorithm incorporating clinical and novel biomarker data
Open accessAbstract
Establishing the in vivo diagnosis of Alzheimer’s disease (AD) or other dementias relies on clinical criteria; however, the accuracy of these criteria can be limited. The diagnostic accuracy is 77% for a clinical diagnosis of AD, even among experts. We performed a review through PubMed of articles related to specific diagnostic modalities, including APOE genotyping, cerebrospinal fluid (CSF) testing, fludeoxyglucose F 18 positron emission tomography (PET), amyloid PET, tau PET, computed tomography (CT), single-photon emission CT, magnetic resonance imaging (MRI), and B12 and thyroid-stimulating hormone screening, to determine the specificity and sensitivity of each test used in the clinical diagnosis of AD. We added a novel immunomagnetic reduction assay that provides ultrasensitivity for analyzing the levels of plasma tau and beta amyloid 42 (Aβ 42 ). The sensitivity and specificity of the current diagnostic approach (structural CT or MRI with screening labs) remain low for clinical detection of AD and are primarily used to exclude other conditions. Because of limited diagnostic capabilities, physicians do not feel comfortable or skilled in rendering a clinical diagnosis of AD. Compounding this problem is the fact that inexpensive, minimally invasive diagnostic tests do not yet exist. Biomarkers (obtained through CSF testing or PET imaging), which are not routinely incorporated in clinical practice, correlate well with pathologic changes. While PET is particularly costly and difficult to assess, CSF measures of tau and beta amyloid are not costly, and these tests may be worthwhile when the tiered approach proposed here warrants further testing. There is a need for developing bloodborne biomarkers that can aid in the clinical diagnosis of AD. Here we present a streamlined questionnaire-enriched, biomarker-enriched approach that is more cost-effective than the current diagnosis of exclusion and is designed to increase clinical confidence for a diagnosis of dementia due to AD.
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In a clinical imaging/pathology series of 57 individuals clinically diagnosed with AD, 13 (23%) had no or sparse beta amyloid plaques at autopsy.
“In a clinical imaging/pathology series of 57 individuals clinically diagnosed with AD, 13 (23%) had no or sparse beta amyloid (Ab) plaques at autopsy.”
From Concordance of Clinical Alzheimer Diagnosis and Neuropathological Features at Autopsy (2020) · cites this paper as [7]
Diagnostic accuracy of clinical AD is variable, even among experts.
“The study concluded that diagnostic accuracy of clinical AD is variable, even among experts (7).”
From Concordance of Clinical Alzheimer Diagnosis and Neuropathological Features at Autopsy (2020) · cites this paper as [7]
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“Historically, a diagnosis of AD has been one of exclusion, and one only made in the latter stages of disease (26); however, the disease process can take years”
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Huggins, C. J., Escudero, J., Parra, M. A., Scally, B., Anghinah, R., Vitória Lacerda De Araújo, A., Basile, L. F., & Abasolo, D. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac05d8 · Cited by 105 · see citing works
“The reported diagnostic accuracy of AD by experts alone is only 77% (Sabbagh et al 2017).”
Preanalytical sample handling recommendations for Alzheimer's disease plasma biomarkers
Rózga, M., Bittner, T., Batrla, R., & Karl, J. (2019) · Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring · doi:10.1016/j.dadm.2019.02.002 · Cited by 99 · see citing works
“Methods: Amyloid b peptides (Ab[1-40], Ab[1-42]) and total Tau plasma concentrations were quantified using fully automated Roche Elecsys assays.”
Optical coherence tomography angiography of the retina and choroid in systemic diseases
Chua, J., Tan, B., Wong, D. W. K., Garhöfer, G., Liew, X. W., Popa‐Cherecheanu, A., Chin, C. W. L., Miléa, D., Chen, C., & Schmetterer, L. F. (2024) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2024.101292 · Cited by 95 · see citing works
An overview of the genes and biomarkers in Alzheimer’s disease
Krishnamurthy, H. K., Jayaraman, V., Krishna, K., Wang, T., Bei, K., Changalath, C., & Rajasekaran, J. J. (2024) · Ageing Research Reviews · doi:10.1016/j.arr.2024.102599 · Cited by 85 · see citing works
“The current available diagnostic tools can diagnose AD with an accuracy of 77% (Sabbagh et al., 2017).”
Breath-based non-invasive diagnosis of Alzheimer’s disease: a pilot study
Tiele, A., Wicaksono, A. N., Daulton, E., Ifeachor, E., Eyre, V., Clarke, S., Timings, L., Pearson, S., Covington, J. A., & Li, X. (2019) · Journal of Breath Research · doi:10.1088/1752-7163/ab6016 · Cited by 73 · see citing works
Alzheimer’s Disease Diagnosis Using Machine Learning: A Survey
Dara, O. A., López-Guede, J. M., Raheem, H. I., Rahebi, J., Zulueta, E., & Fernández‐Gámiz, U. (2023) · Applied Sciences · doi:10.3390/app13148298 · Cited by 77 · see citing works
d’Abramo, C., D'adamio, L., & Giliberto, L. (2020) · Journal of Personalized Medicine · doi:10.3390/jpm10030116 · Cited by 68 · see citing works
Shea, D. W., Colasurdo, E. A., Smith, A. S. T., Paschall, C. J., Jayadev, S., Keene, C. D., Galasko, D. R., Ko, A. L., Li, G., Peskind, E. R., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2213157119 · Cited by 59 · see citing works
“1A) (16), but there is interest in establishing new biomarkers to provide earlier and more accurate diagnoses (17, 18).”
Feed-forward LPQNet based Automatic Alzheimer's Disease Detection Model
Kaplan, E., Doğan, Ş., Tuncer, T., Bayğın, M., & Altunışık, E. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104828 · Cited by 52 · see citing works
Hajjo, R., Sabbah, D. A., Abusara, O. H., & Bawab, A. Q. A. (2022) · Diagnostics · doi:10.3390/diagnostics12122975 · Cited by 55 · see citing works
Renner, H., Schöler, H. R., & Bruder, J. M. (2021) · Movement Disorders · doi:10.1002/mds.28775 · Cited by 47 · see citing works
“164,165 Here, data from very different techniques need to be correlated, and novel AI and ML techniques can aid in this process.”
BackgroundGenetic architecture of common non-Alzheimer’s disease dementias
Guerreiro, R. J., Gibbons, E., Tábuas‐Pereira, M., Kun‐Rodrigues, C., Cordeiro, G., & Brás, J. (2020) · Neurobiology of Disease · doi:10.1016/j.nbd.2020.104946 · Cited by 46 · see citing works
Concordance of Clinical Alzheimer Diagnosis and Neuropathological Features at Autopsy
Gauthreaux, K., Bonnett, T. A., Besser, L. M., Brenowitz, W. D., Teylan, M., Mock, C., Chen, Y.-C., Chan, K. C. G., Keene, C. D., Zhou, X.-H., et al. (2020) · Journal of Neuropathology & Experimental Neurology · doi:10.1093/jnen/nlaa014 · Cited by 44 · see citing works
“The study concluded that diagnostic accuracy of clinical AD is variable, even among experts (7).”
BackgroundScreening for Early-Stage Alzheimer’s Disease Using Optimized Feature Sets and Machine Learning
Kleiman, M. J., Barenholtz, E., Galvin, J. E., & Initiative, F. T. A. D. N. (2021) · Journal of Alzheimer s Disease · doi:10.3233/jad-201377 · Cited by 44 · see citing works
Lucke Wold, B. (2017) · Journal of Neurology and Neurosurgery · doi:10.19104/jnn.2017.40 · Cited by 41 · see citing works
“It is the location and amount of tau found on autopsy in AD subjects that correlates best with stage and severity of symptomatology, not the amyloid plaque [14].”
BackgroundComputational Methods for the Discovery of Metabolic Markers of Complex Traits
Lee, M. Y., & Hu, T. (2019) · Metabolites · doi:10.3390/metabo9040066 · Cited by 38 · see citing works
“Overall, AD research is trending in the direction of using computationally-robust, multivariate methods to develop new diagnostic tests that are safer, more cost-efficient, and have similar or greater accuracy rates than current neuropsychological and imaging techniques (which are estimated at 77%) [37].”
BackgroundResidual-Based Multi-Stage Deep Learning Framework for Computer-Aided Alzheimer’s Disease Detection
Hassan, N., Miah, A. S. M., & Shin, J. (2024) · Journal of Imaging · doi:10.3390/jimaging10060141 · Cited by 37 · see citing works
“Assessments of patients involve medical history, physical condition examination, and various diagnostic tests, including Nero’s logical examination such as muscle tone and reflex coordination [11].”
MethodEnhancing Alzheimer’s disease diagnosis and staging: a multistage CNN framework using MRI
Ali, M. U., Kim, K., Khalid, M., Farrash, M., Zafar, A., & Lee, S. W. (2024) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1395563 · Cited by 42 · see citing works
A Network-Based Perspective in Alzheimer's Disease: Current State and an Integrative Framework
Dragomir, A., Vrahatis, A. G., & Bezerianos, A. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2863202 · Cited by 34 · see citing works
Arnerić, S. P., Kern, V. D., & Stephenson, D. T. (2018) · Biochemical Pharmacology · doi:10.1016/j.bcp.2018.01.043 · Cited by 31 · see citing works
“Additional biomarkers under investigation include fMRI, DTI, ASL, CSF proteomic biomarkers, blood biomarkers, TMS and optical biomarkers [43,44].”
Grasso, G. (2018) · Mass Spectrometry Reviews · doi:10.1002/mas.21566 · Cited by 30 · see citing works
Rabaneda‐Bueno, R., Mena‐Montes, B., Torres‐Castro, S., Torres‐Carrillo, N., & Torres-Carrillo, N. M. (2021) · Genes · doi:10.3390/genes12121959 · Cited by 29 · see citing works
“Overall, then, the diagnosis of AD is increasingly one of exclusion and would require the development of biomarkers in the blood to confirm the correct clinical diagnosis [6].”
BackgroundMetabolic profiling of Alzheimer's disease: Untargeted metabolomics analysis of plasma samples
Milos, T., Rojo, D., Erjavec, G. N., Konjevod, M., Tudor, L., Vuić, B., Štrac, D. Š., Uzun, S., Mimica, N., Kozumplik, O., et al. (2023) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2023.110830 · Cited by 28 · see citing works
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de la Monte, S. M. (2023) · Journal of Alzheimer s Disease · doi:10.3233/jad-230555 · Cited by 27 · see citing works
“Nonetheless, the combined use of neuroimaging, clinical diagnostics, and the continually evolving array of CSF and serum-based biomarkers have greatly improved our diagnostic acumen and expanded eligibility for early interventional treatments [9].”
Llano, D. A., Devanarayan, P., & Devanarayan, V. (2022) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2022.07.015 · Cited by 22 · see citing works
Borzage, M., Saunders, A., Hughes, J. N., McComb, J. G., Blüml, S., & King, K. S. (2021) · American Journal of Neuroradiology · doi:10.3174/ajnr.a7294 · Cited by 20 · see citing works
“7%,(24) and the diagnostic accuracy for AD of 77%.(25) Thus, if a patient is diagnosed with AD, the probabilities of the true disease processes are 96.”
Sharma, G., Vijayvargiya, A., & Kumar, R. (2021) · IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) · doi:10.1109/upcon52273.2021.9667607 · Cited by 19 · see citing works
Doke, R. R., Lamkhade, G., Vinchurkar, K., & Singh, S. K. (2024) · ACS Pharmacology & Translational Science · doi:10.1021/acsptsci.4c00457 · Cited by 23 · see citing works
MRI-Driven Alzheimer’s Disease Diagnosis Using Deep Network Fusion and Optimal Selection of Feature
Ali, M. U., Hussain, S. J., Khalid, M., Farrash, M., Lahza, H. F. M., & Zafar, A. (2024) · Bioengineering · doi:10.3390/bioengineering11111076 · Cited by 17 · see citing works
Carbon Nanostructure-Based Materials: A Novel Tool for Detection of Alzheimer’s Disease
Ashfaq, M., Talreja, N., Chuahan, D., & Srituravanich, W. (2019) · Springer eBooks · doi:10.1007/978-981-13-9636-6_4 · Cited by 16 · see citing works
Lazli, L., Boukadoum, M., & Mohamed, O. A. (2018) · IEEE Life Sciences Conference (LSC) · doi:10.1109/lsc.2018.8572122 · Cited by 16 · see citing works
“In that case, many studies have shown no increase in sensitivity or specificity or even a decreased specificity without an improvement in correct diagnostic rate [8].”
BackgroundExosomes in Age-Related Cognitive Decline: Mechanistic Insights and Improving Outcomes
Duggan, M. R., Lu, A., Foster, T. C., Wimmer, M. E., & Parikh, V. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.834775 · Cited by 15 · see citing works
Zhang, H., Shi, F., Yan, Y., Deng, C., & Sun, N. (2023) · Advanced Healthcare Materials · doi:10.1002/adhm.202301136 · Cited by 14 · see citing works
Cell-free amplification of prions: Where do we stand?
Cazzaniga, F. A., Luca, C. M. G. D., Bistaffa, E., Consonni, A., Legname, G., Giaccone, G., & Moda, F. (2020) · Progress in molecular biology and translational science · doi:10.1016/bs.pmbts.2020.08.005 · Cited by 14 · see citing works
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Novel techniques for early diagnosis and monitoring of Alzheimer’s disease
Parul, P., Singh, A. K., & Shukla, S. (2024) · Expert Review of Neurotherapeutics · doi:10.1080/14737175.2024.2415985 · Cited by 13 · see citing works
“In the past, AD used to be only diagnosed in the later stages of the illness, although the disease process could take years to manifest and would have a severe impact on the patient, caregiver, and healthcare system during that time [110].”
Validation of a novel and accurate ApoE4 assay for automated chemistry analyzers
Veiga, S. J., Rodríguez‐Martín, A., García‐Ribas, G., Arribas, I., Menacho-Román, M., & Calero, M. (2020) · Scientific Reports · doi:10.1038/s41598-020-58841-7 · Cited by 13 · see citing works
Song, Y., Kim, H., Lee, J., & Kim, K. (2024) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2024.116431 · Cited by 11 · see citing works
Contributions of Molecular and Optical Techniques to the Clinical Diagnosis of Alzheimer’s Disease
Bistaffa, E., Tagliavini, F., Matteini, P., & Moda, F. (2020) · Brain Sciences · doi:10.3390/brainsci10110815 · Cited by 11 · see citing works
“At present, the in vivo diagnostic accuracy for AD is about 77%, even among expert neurologists [75].”
BackgroundThe role of pragmatics in the diagnosis of dementia
Cummings, L. (2020) · East Asian Pragmatics · doi:10.1558/eap.39716 · Cited by 10 · see citing works
Gaubert, S., Garcés, P., Hipp, J. F., Bruña, R., López, M. E., Maestú, F., Vaghari, D., Henson, R. N., Paquet, C., & Engemann, D. A. (2025) · EBioMedicine · doi:10.1016/j.ebiom.2025.105659 · Cited by 10 · see citing works
Stereoscopic Depth Perception and Visuospatial Dysfunction in Alzheimer’s Disease
Kim, N.-G., & Lee, H. (2021) · Healthcare · doi:10.3390/healthcare9020157 · Cited by 10 · see citing works
Using CatBoost and Other Supervised Machine Learning Algorithms to Predict Alzheimer's Disease
An, J. (2022) · International Conference on Machine Learning and Applications · doi:10.1109/icmla55696.2022.00265 · Cited by 12 · see citing works
“The clinical diagnosis accuracy of AD is about 77% [2].”
BackgroundPrecision Medicine: Role of Biomarkers in Early Prediction and Diagnosis of Alzheimer’s Disease
Shen, L., Xia, S., Zhang, H., Yao, F., Liu, X., Zhao, Y., Ying, M., Iqbal, J., & Liu, Q. (2019) · IntechOpen eBooks · doi:10.5772/intechopen.82035 · Cited by 10 · see citing works
“On the other hand, in clinical practice, a diagnosis of AD is primarily made on the base of clinical features, results of neurological and neuropsychological tests, and by exclusion of other causes of dementia, including vascular and frontotemporal dementia or other neurological diseases [11].”
BackgroundStatsenko, Y. A., Meribout, S., Habuza, T., Almansoori, T. M., Gorkom, K., Gelovani, J. G., & Ljubisavljević, M. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.943566 · Cited by 10 · see citing works
“Contrarily, a combination of structural and functional data seems to be a reasonable alternative: it is easy to conduct, more affordable, and reliable (Sabbagh et al., 2017).”
BackgroundEratne, D., Li, Q.-X., Lewis, C. C., Dang, C., Kang, M. J., Grewal, J. K., Loi, S. M., Walterfang, M., Evans, A. H., Malpas, C. B., et al. (2024) · Journal of Neurology · doi:10.1007/s00415-024-12732-3 · Cited by 12 · see citing works
“This is likely a reflection of the real-world clinically heterogenous clinical cohort and ranges, that many non-AD disorders have A+T− and other abnormal CSF profiles [35, 36], as well as the recognised challenges (and inaccuracies) of in vivo clinical diagnosis [13, 24, 25, 29, 30].”
Kirimtat, A., Krejcar, O., Marešová, P., Selamat, A., & Kuča, K. (2024) · IEEE Access · doi:10.1109/access.2024.3484928 · Cited by 8 · see citing works
“In addition to this, according to [60], even among professionals, the diagnostic accuracy for a traditional clinical diagnosis of AD is 77%.”
BackgroundZalianty, F. R., Sirait, D. R. E. S., Wijayanto, I., & Hadiyoso, S. (2024) · IEEE International Conference on Artificial Intelligence and Mechatronics Systems (AIMS) · doi:10.1109/aims61812.2024.10512538 · Cited by 8 · see citing works
“Physicians employ a combination of diagnostic tools along with medical records and additional data, such as neurological examinations, evaluations of cognition and function, brain imaging methods (MRI, CT, PET), and tests on blood or cerebrospinal fluid samples, to arrive at a precise diagnosis [5].”
MethodJuengling, F. D., Allenbach, G., Bruehlmeier, M., Klaeser, B., Wissmeyer, M., Garibotto, V., Felbecker, A., & Georgescu, D. (2020) · Nuklearmedizin - NuclearMedicine · doi:10.1055/a-1277-6014 · Cited by 8 · see citing works
Chen, T., Lee, W., Chen, J., Chang, S.-Y., Lin, C.-F., & Chen, H.-C. (2022) · Alzheimer s Research & Therapy · doi:10.1186/s13195-022-01083-8 · Cited by 8 · see citing works
“A diagnostic accuracy was reported to range from 70 to 80% for a clinical diagnosis of AD [70].”
BackgroundHyperspectral Retinal Imaging as a Non-Invasive Marker to Determine Brain Amyloid Status
Poudel, P., Frost, S. M., Eslick, S., Sohrabi, H. R., Taddei, K., Martins, R. N., & Hone, E. (2024) · Journal of Alzheimer s Disease · doi:10.3233/jad-240631 · Cited by 7 · see citing works
Vesković, A., Nakarada, Đ. J., Pavićević, A. A., Prokić, B., Perović, M., Kanazir, S., Popović-Bijelić, A. D., & Mojović, M. (2021) · Current Alzheimer Research · doi:10.2174/1567205018666210324121156 · Cited by 7 · see citing works
Kim, S., Kim, K., Nho, K. T., Myung, W., & Won, H. (2021) · Journal of Alzheimer s Disease · doi:10.3233/jad-200671 · Cited by 7 · see citing works
Tousi, B., & Sabbagh, M. N. (2021) · Neurology and Therapy · doi:10.1007/s40120-021-00286-9 · Cited by 7 · see citing works
Self-citationJuday, T. R., Holub, A. A., Mattke, S., Betts, K. A., Kitchen, S. A., Liu, H., Batrla, R., Frech, F. H., & Khachaturian, A. S. (2024) · Journal of Alzheimer s Disease · doi:10.1177/13872877241297128 · Cited by 6 · see citing works
“7,8 In light of new therapies, which work by slowing disease progression in the early stages of disease, a timely diagnosis, particularly during the earlier stages of disease, represents a critical window when intervention can be initiated.”
Variability in Alzheimer’s disease mortality from European vital statistics, 2012–2020
Zuin, M., Brombo, G., Polastri, M., Romagnoli, T., Cervellati, C., & Zuliani, G. (2024) · International Journal of Geriatric Psychiatry · doi:10.1002/gps.6068 · Cited by 6 · see citing works
“23 Therefore, the increasing AD ‐ related mortality trend observed in some Countries may be related to the improved AD diagnosis.”
BackgroundKao, C.-C., Hsieh, H., Chang, Y., Chu, H.-C., Yang, Y., & Sheu, S. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13071118 · Cited by 6 · see citing works
“However, cognitive assessment of AD using the Mini-Mental State Examination (MMSE) score and Clinical Dementia Rating (CDR) score is time-consuming and reaching diagnostic accuracy is difficult, even by experts [4].”
Neuropsychiatric Symptoms Among Hispanics: Results of the Maracaibo Aging Study
Gil, M., Alliey‐Rodriguez, N., López-Alvarenga, J. C., Diego, V. P., Gaona, C. A., Mata, L., Pirela, R. V., Chavez, C. A., de Erausquin, G. A., Melgarejo, J. D., et al. (2021) · Journal of Alzheimer s Disease · doi:10.3233/jad-201144 · Cited by 5 · see citing works
“If these two phenotypes are incorporated within the patient’s medical history, risk assessment, neurological examination, and biomarkers such APOE genotype, there is approximately a 90% improvement of the dementia diagnosis [24].”
A Bayesian hierarchical change point model with parameter constraints
Li, H., Benitez, A. M., & Neelon, B. (2020) · Statistical Methods in Medical Research · doi:10.1177/0962280220948097 · Cited by 4 · see citing works
Noncoding RNAs in Alzheimer’s Disease: Overview of Functional and Therapeutic Significance
Adiga, D., Eswaran, S., Sriharikrishnaa, S., Khan, N. G., Kumar, D., & Kabekkodu, S. P. (2024) · Current Topics in Medicinal Chemistry · doi:10.2174/0115680266293212240405042540 · Cited by 4 · see citing works
Stefanidis, K. B., Summers, M. J., & Clark, M. S. G. (2025) · Journal of Transport & Health · doi:10.1016/j.jth.2025.102020 · Cited by 4 · see citing works
The Role of the Ganglion Cell Layer as an OCT Biomarker in Neurodegenerative Diseases
Ruggeri, F., Fumi, D., Bassis, L., Pippo, M. D., & Abdolrahimzadeh, S. (2025) · Journal of Integrative Neuroscience · doi:10.31083/jin26039 · Cited by 4 · see citing works
Exploring Frailty Status and Blood Biomarkers: A Multidimensional Approach to Alzheimer’s Diagnosis
Cermelli, A., Crisafi, A., Chiarandon, A. M., Mirabelli, G., Lombardo, C., Batti, V., Boschi, S., Piella, E. M., Roveta, F., Rainero, I., et al. (2025) · Geriatrics · doi:10.3390/geriatrics10050133 · Cited by 3 · see citing works
“Studies have shown that individuals with cognitive frailty may exhibit a specific subcortical-frontal pattern of dysfunction that is distinct from typical AD [45] and may represent an intermediate stage in the trajectory from healthy aging to dementia.”
Involvement of neuroinflammatory mediators in the pathogenesis of neurodegeneration
Singh, R. K., & Srivastava, A. (2020) · Asian Journal of Pharmacy and Pharmacology · doi:10.31024/ajpp.2020.6.1.2 · Cited by 3 · see citing works
Nanomaterials for Point of Care Disease Detection
Sharma, C., Walia, S., & Acharya, A. (2020) · Springer eBooks · doi:10.1007/978-981-15-4280-0_4 · Cited by 3 · see citing works
Genetic Risk Factors for Sporadic Frontotemporal Dementia
Ferrari, R., Manzoni, C., & Momeni, P. (2018) · Springer eBooks · doi:10.1007/978-3-319-72938-1_9 · Cited by 3 · see citing works
Matrix Factorization-based Improved Classification of Gene Expression Data
Malik, S., & Bansal, P. (2019) · Recent Advances in Computer Science and Communications · doi:10.2174/2213275912666190715165034 · Cited by 3 · see citing works
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“18 The depression was fi ltered by the PHQ-2 scale, patients with a PHQ-2 scoring ≥ 3 were considered as high risk.”
Ojha, V. P., Riahi, S., Bobo, R. H., Moravveji, S., Meteumba, L. G., & Yarahmadian, S. (2025) · Discover Psychology · doi:10.1007/s44202-025-00335-6 · Cited by 2 · see citing works
“This can be improved by including biomarkers and imaging [5, 17, 35].”
Elmaaty, A. A. A., Eissa, M. A., El-Rashedy, S. K., Zehry, H. I., Mosa, A. A. H., Zarad, C. A., Abdel-Dayem, M. A., Nokrashy, A. E., & Elsherifi, S. S. (2024) · The Egyptian Journal of Neurology Psychiatry and Neurosurgery · doi:10.1186/s41983-024-00912-x · Cited by 1 · see citing works
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“Future research should validate the model through real-world studies and reassess its feasibility, adaptability and impact on patient outcomes as biomarkers enter routine use.”
Roth, S., Yan, J. T., Patru, M.-M., Mattke, S., Gustavsson, A., & Ortsäter, G. (2026) · Journal of Alzheimer s Disease · doi:10.1177/13872877251406909 · Cited by 1 · see citing works
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Cognitive Impairment Prediction by Normal Cognitive Brain MRI Scans Using Deep Learning
Bardwell, J., Hassan, G. M., Salami, F., & Akhtar, N. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-22695-3_40 · Cited by 1 · see citing works
Epigenetics of Alzheimer’s Disease: Past, Present and Future
Adiga, D., Eswaran, S., Sriharikrishnaa, S., Khan, N. G., Kabekkodu, S. P., & Kumar, D. (2023) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815136142123010005 · Cited by 1 · see citing works
Lange, B., Pathak, S., Vijayarani, K. R., DeRuiter, J., Alharbi, H. F., Bhupathyraaj, M., Mohandoss, K., & Dhanasekaran, M. (2024) · Nutritional neurosciences · doi:10.1007/978-981-97-2577-9_1 · Cited by 1 · see citing works
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荒井, 久., 岩崎, 光., 大城, み., 油川, 研., 川口, 宏., 村岡, 真., & 佐々木, 淳. (1988) · リハビリテーション医学 : 日本リハビリテーション医学会誌
Multi-stratification feature selection for diagnostic analysis of Alzheimer's disease
Zhang, L., Xin, B., Yan, S., Zheng, C., Zhou, Y., Lu, J., & Wang, X. (2021) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta52665.2021.9647043
Progress of Nanotechnology-Based Detection and Treatment of Alzheimer’s Disease Biomarkers
Singh, Y. R., Shukla, A., & Pattanayek, S. K. (2022) · Springer eBooks · doi:10.1007/978-981-16-3667-7_3
Detection Method of Early Data Based on SVM Classifier
Zhan, Y. (2022) · IEEE International Conference on Data Science and Computer Application (ICDSCA) · doi:10.1109/icdsca56264.2022.9988340
Alzheimer’s Disease MRI Identification, Recognition, & Evaluation - Deep LearningPreprint
Muniagurria, T. P., & Rowlands, S. (2023) · Research Square · doi:10.21203/rs.3.rs-3124095/v1
Exploring Laboratory Findings in Psychotic Patients
Yamani, I. A. J. (2024) · African Journal of Biomedical Research · doi:10.53555/ajbr.v27i3s.2770
“…as transaminases in decreased liver function, increased levels of white blood counts and C-reactive protein in inflammation, serum autoantibodies in autoimmune encephalitis, and some deficiencies such as vitamin B12, folic acid, vitamin D, and zinc (Ronkainen and Okon, 2014; Sabbagh et al., 2017).”
Prospecting salivary tau as a diagnostic for Alzheimer’s type dementia
Santos, G. A. A. D., Vale, F. D. A. C. D., & Fazan, V. (2025) · Dementia & Neuropsychologia · doi:10.1590/1980-5764-dn-2024-0253 · Cited by 2 · see citing works
“Furthermore, it needs to evolve so that it is conclusive and not performed with the aim of excluding possibilities 48 .”
Revolutionizing Alzheimer’s Diagnostics: AI Tool Shows Superior Accuracy over Traditional Methods
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Characteristics of Study Design and Statistical Analysis in OCT-Based Studies of Neurodegeneration
Caplash, S., Song, H., Waldman, A. T., Ying, G., & Kim, B. J. (2025) · Ophthalmology Science · doi:10.1016/j.xops.2025.100961
“33,36 As only a small percent of Alzheimer disease cases are familial, the use of CSF and PET biomarkers for Alzheimer disease in clinical studies deserves evaluation as these biomarkers have > 90% sensitivity and specificity.”
Chen, T.-B., Lee, W.-J., Chen, J.-P., Chang, S.-Y., Lin, C.-F., & Chen, H.-C. (2022) · Figshare · doi:10.6084/m9.figshare.21256971
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“These remote automated methods can both improve efficiency and reduce the burden on-site, whilst enabling the recruitment of participants from a broader and more diverse range of groups [4, 5].”
- Authors
- Sabbagh, M. N., Lue, L.-F., Fayard, D., & Shi, J.
- Affiliations
- Barrow Neurological Institute (US)
- Arizona State University (US)
- Corresponding author
- Sabbagh, Marwan N.
- Venue
- Neurology and Therapy
- Published
- 2017-07
- Volume
- 6
- Issue
- S1
- Pages
- 83-95
- ISSN
- 2193-6536 (ISSN-L)
- 2193-8253
- PMC
- PMC5520815
- OpenAlex
- W2737462298
- Cited by
- 107
- References
- 59
- Citations by year
- 2026: 10 · 2025: 12 · 2024: 20 · 2023: 9 · 2022: 11 · 2021: 19 · 2020: 12 · 2019: 6 · 2018: 5 · 2017: 2
- FWCI
- 1.04
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by-nc
- Locations
- Neurology and Therapy (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Arizona State University Library Digital Repository (Arizona State University) (submittedVersion, cc-by)
- Funders
- Barrow Neurological Foundation (NIA P30 AG019610)
- National Institute on Aging (AG019610, P30AG019610)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Cerebrospinal fluid and hydrocephalus
- Keywords
- Alzheimer's disease, diagnosis, diagnostic accuracy, biomarkers, cerebrospinal fluid, amyloid PET, tau PET, FDG-PET, APOE genotyping, plasma tau, amyloid-beta 42, blood-based biomarkers, dementia, sensitivity and specificity
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