The DOI matches its Crossref record; 387 of the 391 references it cites were found in a registry or an index.
Structural magnetic resonance imaging for the early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment
Open accessAbstract
BACKGROUND: Mild cognitive impairment (MCI) due to Alzheimer's disease is the symptomatic predementia phase of Alzheimer's disease dementia, characterised by cognitive and functional impairment not severe enough to fulfil the criteria for dementia. In clinical samples, people with amnestic MCI are at high risk of developing Alzheimer's disease dementia, with annual rates of progression from MCI to Alzheimer's disease estimated at approximately 10% to 15% compared with the base incidence rates of Alzheimer's disease dementia of 1% to 2% per year. OBJECTIVES: To assess the diagnostic accuracy of structural magnetic resonance imaging (MRI) for the early diagnosis of dementia due to Alzheimer's disease in people with MCI versus the clinical follow-up diagnosis of Alzheimer's disease dementia as a reference standard (delayed verification). To investigate sources of heterogeneity in accuracy, such as the use of qualitative visual assessment or quantitative volumetric measurements, including manual or automatic (MRI) techniques, or the length of follow-up, and age of participants. MRI was evaluated as an add-on test in addition to clinical diagnosis of MCI to improve early diagnosis of dementia due to Alzheimer's disease in people with MCI. SEARCH METHODS: On 29 January 2019 we searched Cochrane Dementia and Cognitive Improvement's Specialised Register and the databases, MEDLINE, Embase, BIOSIS Previews, Science Citation Index, PsycINFO, and LILACS. We also searched the reference lists of all eligible studies identified by the electronic searches. SELECTION CRITERIA: We considered cohort studies of any size that included prospectively recruited people of any age with a diagnosis of MCI. We included studies that compared the diagnostic test accuracy of baseline structural MRI versus the clinical follow-up diagnosis of Alzheimer's disease dementia (delayed verification). We did not exclude studies on the basis of length of follow-up. We included studies that used either qualitative visual assessment or quantitative volumetric measurements of MRI to detect atrophy in the whole brain or in specific brain regions, such as the hippocampus, medial temporal lobe, lateral ventricles, entorhinal cortex, medial temporal gyrus, lateral temporal lobe, amygdala, and cortical grey matter. DATA COLLECTION AND ANALYSIS: Four teams of two review authors each independently reviewed titles and abstracts of articles identified by the search strategy. Two teams of two review authors each independently assessed the selected full-text articles for eligibility, extracted data and solved disagreements by consensus. Two review authors independently assessed the quality of studies using the QUADAS-2 tool. We used the hierarchical summary receiver operating characteristic (HSROC) model to fit summary ROC curves and to obtain overall measures of relative accuracy in subgroup analyses. We also used these models to obtain pooled estimates of sensitivity and specificity when sufficient data sets were available. MAIN RESULTS: We included 33 studies, published from 1999 to 2019, with 3935 participants of whom 1341 (34%) progressed to Alzheimer's disease dementia and 2594 (66%) did not. Of the participants who did not progress to Alzheimer's disease dementia, 2561 (99%) remained stable MCI and 33 (1%) progressed to other types of dementia. The median proportion of women was 53% and the mean age of participants ranged from 63 to 87 years (median 73 years). The mean length of clinical follow-up ranged from 1 to 7.6 years (median 2 years). Most studies were of poor methodological quality due to risk of bias for participant selection or the index test, or both. Most of the included studies reported data on the volume of the total hippocampus (pooled mean sensitivity 0.73 (95% confidence interval (CI) 0.64 to 0.80); pooled mean specificity 0.71 (95% CI 0.65 to 0.77); 22 studies, 2209 participants). This evidence was of low certainty due to risk of bias and inconsistency. Seven studies reported data on the atrophy of the medial temporal lobe (mean sensitivity 0.64 (95% CI 0.53 to 0.73); mean specificity 0.65 (95% CI 0.51 to 0.76); 1077 participants) and five studies on the volume of the lateral ventricles (mean sensitivity 0.57 (95% CI 0.49 to 0.65); mean specificity 0.64 (95% CI 0.59 to 0.70); 1077 participants). This evidence was of moderate certainty due to risk of bias. Four studies with 529 participants analysed the volume of the total entorhinal cortex and four studies with 424 participants analysed the volume of the whole brain. We did not estimate pooled sensitivity and specificity for the volume of these two regions because available data were sparse and heterogeneous. We could not statistically evaluate the volumes of the lateral temporal lobe, amygdala, medial temporal gyrus, or cortical grey matter assessed in small individual studies. We found no evidence of a difference between studies in the accuracy of the total hippocampal volume with reg
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“a review summarising 33 studies in structural neuroimaging for the early diagnosis of AD in people with MCI (including 3935 participants) concluded that there is lack of systematic approach in data collection, analysis and interpretation [24].”
From The use of neuroimaging techniques in the early and differential diagnosis of dementia (2023) · cites this paper as [24]
Citations
Cites
Diagnostic and Statistical Manual of Mental Disorders
American Psychiatric Association (2013) · doi:10.1176/appi.books.9780890425596 · Cited by 116,369
McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., & Stadlan, E. M. (1984) · Neurology · doi:10.1212/wnl.34.7.939 · Cited by 27,915
Kok, J. S., Berg, I. J., & Scherder, E. J. A. (2013) · Dementia and Geriatric Cognitive Disorders Extra · doi:10.1159/000353441 · Cited by 20,589
McKhann, G. M., Knopman, D. S., Chertkow, H., Hyman, B. T., Jack, C. R., Kawas, C. H., Klunk, W. E., Koroshetz, W. J., Manly, J. J., Mayeux, R., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.005 · Cited by 18,657
QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies
Whiting, P. F., Rutjes, A. W. S., Westwood, M. E., Mallett, S., Deeks, J. J., Reitsma, J. B., Leeflang, M. M. G., Sterne, J. A. C., Bossuyt, P. M. M., & the QUADAS-2 Group* (2011) · Annals of Internal Medicine · doi:10.7326/0003-4819-155-8-201110180-00009 · Cited by 14,199
Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR)
American Psychiatric Association (2000) · doi:10.1176/appi.books.9780890423349 · Cited by 11,069
NIA‐AA Research Framework: Toward a biological definition of Alzheimer's disease
Jack, C. R., Bennett, D. A., Blennow, K., Carrillo, M. C., Dunn, B., Haeberlein, S. B., Holtzman, D. M., Jagust, W., Jessen, F., Karlawish, J., et al. (2018) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2018.02.018 · Cited by 10,537
Albert, M. S., DeKosky, S. T., Dickson, D., Dubois, B., Feldman, H. H., Fox, N. C., Gamst, A., Holtzman, D. M., Jagust, W. J., Petersen, R. C., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.008 · Cited by 10,152
The Clinical Dementia Rating (CDR): Current version and scoring rules
Morris, J. C. (1993) · Neurology · doi:10.1212/wnl.43.11.2412-a · Cited by 9,882
Mild Cognitive Impairment: Clinical Characterization and Outcome
Petersen, R. C., Smith, G. E., Waring, S. C., Ivnik, R. J., Tangalos, E. G., & Kokmen, E. (1999) · Archives of Neurology · doi:10.1001/archneur.56.3.303 · Cited by 8,980
Research works printed but not linked: 11
- Chertkow, 2012, Amnestic mild cognitive impairment in a memory clinic: longitudinal course and predictors of progression (abstract), Alzheimer's & Dementia, 8 Suppl 4, 482
- Citak-Er, 2017, A novel convolutional neural network model based on voxel-based morphometry of imaging data in predicting the prognosis of patients with mild cognitive impairment, Journal of Neurological Sciences, 34, 52
- Cover, 2016, The measurement of hippocampal atrophy rates with MRI for a 3-year study appears to be at least 3 times more sensitive than a 1-year study based on back-to-back reproducibility, Alzheimer's & Dementia, 12 Suppl 7, 99–100
- Kong, 2014, Role of imaging and genetic data for predicting time to conversion to Azheimer disease in patients with mild cognitive impairment, Annals of Neurology, 76 Suppl 18, S94
- Nesteruk, 2015, Predicting the conversion of mild cognitive impairment to Alzheimer's disease based on the volumetric measurements of the selected brain structures in magnetic resonance imaging, Neurologia i Neurochirurgia Polska, 49, 349, 10.1016/j.pjnns.2015.09.003 · DOI
- Maurik, 2016, Personalized risk estimates for MCI patients: taking biomarkers into the clinic, Alzheimer's & Dementia, 12 Suppl 7, P393
- Alzheimer’s Disease International, 2019, World Alzheimer Report 2019: Attitudes to Dementia
- Bossuyt P Davenport C Deeks J Hyde C Leeflang M Scholten R Chapter 11: Interpreting results and drawing conclusions. In: Deeks JJ, Bossuyt PM, Gatsonis C (editors), Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy Version 0.9. The Cochrane Collaboration, 2013 Available from: srdta.cochrane.org
- De Vet HC Eisinga A Riphagen II Aertgeerts B Pewsner D Chapter 7: Searching for Studies. In: Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy Version 0.4 (September 2008). The Cochrane Collaboration, 2008: Section 7.2.1.2 Available from srdta.cochrane.org/handbook-dta-reviews
- Galton, 2001, Temporal lobe rating scale: application to Alzheimer's disease and frontotemporal dementia, Journal of Neurology, Neurosurgery, and Psychiatry, 70, 165, 10.1136/jnnp.70.2.165 · DOI
- Yue, 1997, Sulcal, ventricular, and white matter changes at MR imaging in the aging brain: data from the cardiovascular health study, Radiology, 202, 33, 10.1148/radiology.202.1.8988189 · DOI
Cited by
Sorted by citations
Safiri, S., Jolfayi, A. G., Fazlollahi, A., Morsali, S., Sarkesh, A., Sorkhabi, A. D., Golabi, B., Aletaha, R., Asghari, K. M., Hamidi, S., et al. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1474043 · Cited by 273
Quinn, T. J., Richard, E., Teuschl, Y., Gattringer, T., Hafdi, M., O'Brien, J. T., Merriman, N. A., Gillebert, C. R., Huygelier, H., Verdelho, A., et al. (2021) · European Journal of Neurology · doi:10.1111/ene.15068 · Cited by 201
XGBoost-SHAP-based interpretable diagnostic framework for alzheimer’s disease
Yi, F., Yang, H., Chen, D., Qin, Y., Han, H., Cui, J., Bai, W., Ma, Y., Zhang, R., & Yu, H. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02238-9 · Cited by 205
The use of neuroimaging techniques in the early and differential diagnosis of dementia
Chouliaras, L., & O’Brien, J. T. (2023) · Molecular Psychiatry · doi:10.1038/s41380-023-02215-8 · Cited by 221
Alzheimer’s Disease: Exploring the Landscape of Cognitive Decline
Tenchov, R., Sasso, J. M., & Zhou, Q. A. (2024) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.4c00339 · Cited by 170
Quinn, T. J., Richard, E., Teuschl, Y., Gattringer, T., Hafdi, M., O'Brien, J. T., Merriman, N. A., Gillebert, C. R., Huygelier, H., Verdelho, A., et al. (2021) · European Stroke Journal · doi:10.1177/23969873211042192 · Cited by 145
The Future of Precision Medicine in the Cure of Alzheimer’s Disease
Arafah, A., Khatoon, S., Rasool, I., Khan, A., Rather, M. A., Abujabal, K. A., Faqih, Y. A. H., Rashid, H., Rashid, S. M., Bilal Ahmad, S., et al. (2023) · Biomedicines · doi:10.3390/biomedicines11020335 · Cited by 133
Ranson, J. M., Rittman, T., Hayat, S. A., Brayne, C., Jessen, F., Blennow, K., van Duijn, C. M., Barkhof, F., Tang, E. Y. H., Mummery, C. J., et al. (2021) · Alzheimer s Research & Therapy · doi:10.1186/s13195-021-00895-4 · Cited by 123
Self-citationRyu, S.-E., Shin, D.-H., & Chung, K. (2020) · IEEE Access · doi:10.1109/access.2020.3025553 · Cited by 111
Tang, F., Zhu, D., Ma, W., Yao, Q., Li, Q., & Shi, J. (2021) · Frontiers in Neurology · doi:10.3389/fneur.2021.645171 · Cited by 100
Hemrungrojn, S., Tangwongchai, S., Charoenboon, T., Phanasathit, M., Supasitthumrong, T., Chaipresertsud, P., Maleevach, P., Likitjaroen, Y., Phanthumchinda, K., & Maes, M. H. J. (2021) · Dementia and Geriatric Cognitive Disorders · doi:10.1159/000517822 · Cited by 89
Heterogeneous data fusion for predicting mild cognitive impairment conversion
Shen, H. T., Zhu, X., Zhang, Z., Wang, S., Chen, Y., Xu, X., & Shao, J. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.08.023 · Cited by 81
Ismail, Z., Leon, R., Creese, B., Ballard, C. G., Robert, P. H., & Smith, E. E. (2023) · Molecular Neurodegeneration · doi:10.1186/s13024-023-00631-6 · Cited by 79
Nicoletti, A. S. Q., Baschi, R., Cicero, C. E., Iacono, S., Re, V. L., Luca, A., Schirò, G., & Monastero, R. (2023) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2023.111821 · Cited by 79
What Is the Role of Explainability in Medical Artificial Intelligence? A Case-Based Approach
Hildt, E. (2025) · Bioengineering · doi:10.3390/bioengineering12040375 · Cited by 74
Explainable Artificial Intelligence in Neuroimaging of Alzheimer’s Disease
Taiyeb Khosroshahi, M., Morsali, S., Gharakhanlou, S., Motamedi, A., Hassanbaghlou, S., Vahedi, H., Pedrammehr, S., Kabir, H. M. D., & Jafarizadeh, A. (2025) · Diagnostics · doi:10.3390/diagnostics15050612 · Cited by 68
d’Abramo, C., D'adamio, L., & Giliberto, L. (2020) · Journal of Personalized Medicine · doi:10.3390/jpm10030116 · Cited by 71
Choroid plexus volume as a novel candidate neuroimaging marker of the Alzheimer’s continuum
Jiang, J., Zhuo, Z., Wang, A., Li, W., Jiang, S., Duan, Y., Ren, Q., Zhao, M., Wang, L., Yang, S., et al. (2024) · Alzheimer s Research & Therapy · doi:10.1186/s13195-024-01520-w · Cited by 67
Clarke, N., Barrick, T. R., & Garrard, P. (2021) · Frontiers in Computer Science · doi:10.3389/fcomp.2021.634360 · Cited by 65
Schilling, L. P., Balthazar, M. L. F., Radanovic, M., Forlenza, O. V., Silagi, M. L., Smid, J., Barbosa, B. J. A. P., Frota, N. A. F., de Souza, L. C., Vale, F. A. C., et al. (2022) · Dementia & Neuropsychologia · doi:10.1590/1980-5764-dn-2022-s102pt · Cited by 59
Neuroimaging modalities in the detection of Alzheimer's disease-associated biomarkers
Dang, C., Wang, Y., Li, Q., & Lu, Y. (2023) · Psychoradiology · doi:10.1093/psyrad/kkad009 · Cited by 53
Diagnosis and Treatment of Alzheimer’s Disease:
Bomasang-Layno, E., & Bronsther, R. (2021) · Delaware Journal of Public Health · doi:10.32481/djph.2021.09.009 · Cited by 60
McDade, E. (2022) · CONTINUUM Lifelong Learning in Neurology · doi:10.1212/con.0000000000001131 · Cited by 56
Neuroimaging in Alzheimer's Disease for Early Diagnosis: A Comprehensive Review
Aramadaka, S., Mannam, R., Narayanan, R. S., Bansal, A., Yanamaladoddi, V. R., Sarvepalli, S. S., & Vemula, S. L. (2023) · Cureus · doi:10.7759/cureus.38544 · Cited by 46
Kim, Y., Park, J., Choi, H., Loeser, M., Ryu, H. B., & Seo, K. (2023) · Journal of Medical Internet Research · doi:10.2196/48093 · Cited by 42
Bergamino, M., Nespodzany, A., Baxter, L. C., Burke, A. D., Caselli, R. J., Sabbagh, M. N., Walsh, R. R., & Stokes, A. M. (2020) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.27272 · Cited by 42
GANCMLAE: Individual Atrophy Detection for AD and MCI
Shi, R., Sheng, C., Jin, S., Zhang, Q., Zhang, S., Zhang, L., Ding, C., Wang, L., Wang, L., Han, Y., et al. (2022) · Human Brain Mapping · doi:10.1002/hbm.26146 · Cited by 40
Chen, S., Xu, W., Xue, C., Hu, G., Ma, W., Qi, W., Lin, D., Lin, X., & Chen, J. (2020) · Journal of Alzheimer s Disease · doi:10.3233/jad-200659 · Cited by 38
Shang, X., Zhang, X., Huang, Y., Zhu, Z., Zhang, X., Liu, J., Wang, W., Tang, S., Yu, H., Ge, Z., et al. (2022) · EClinicalMedicine · doi:10.1016/j.eclinm.2022.101413 · Cited by 37
A Machine Learning Approach for Early Diagnosis of Cognitive Impairment Using Population-Based Data
Tan, W. Y., Hargreaves, C. A., Chen, C., & Hilal, S. (2022) · Journal of Alzheimer s Disease · doi:10.3233/jad-220776 · Cited by 37
Targeting epigenetics: A novel promise for Alzheimer’s disease treatment
Jeremic, D., Jiménez‐Díaz, L., & Navarro‐López, J. D. (2023) · Ageing Research Reviews · doi:10.1016/j.arr.2023.102003 · Cited by 36
Tu, H., SU, C., Peng, C.-S., Lin, T. E., Fu, W.-C. H., Hsu, K., Hwang, T., & Pan, S. (2023) · Journal of Food and Drug Analysis · doi:10.38212/2224-6614.3462 · Cited by 35
Biomarkers for Alzheimer’s Disease (AD) and the Application of Precision Medicine
Lukiw, W. J., Vergallo, A., Lista, S., Hampel, H. J., & Zhao, Y. (2020) · Journal of Personalized Medicine · doi:10.3390/jpm10030138 · Cited by 37
A new generation of AD biomarkers: 2019 to 2021
Hawksworth, J. I., Fernández, E., & Gevaert, K. (2022) · Ageing Research Reviews · doi:10.1016/j.arr.2022.101654 · Cited by 34
Li, K.-R., Wu, A., Tang, Y., He, X., Yu, C., Wu, J., Hu, G., & Yu, L. (2022) · Molecular Neurobiology · doi:10.1007/s12035-022-02944-x · Cited by 34
An open-source tool for longitudinal whole-brain and white matter lesion segmentation
Cerri, S., Greve, D. N., Hoopes, A., Lundell, H., Siebner, H. R., Mühlau, M., & Leemput, K. V. (2023) · NeuroImage Clinical · doi:10.1016/j.nicl.2023.103354 · Cited by 33
Park, B., Kim, Y., Park, J., Choi, H., Kim, S., Ryu, H. B., & Seo, K. (2024) · Journal of Medical Internet Research · doi:10.2196/54538 · Cited by 33
Emotional prosody recognition is impaired in Alzheimer’s disease
Amlerová, J., Laczó, J., Nedelská, Z., Laczó, M., Vyhnálek, M., Zhang, B., Sheardová, K., Angelucci, F., Andel, R., & Hort, J. (2022) · Alzheimer s Research & Therapy · doi:10.1186/s13195-022-00989-7 · Cited by 33
Effects of Palmitoylethanolamide on Neurodegenerative Diseases: A Review from Rodents to Humans
Landolfo, E., Cutuli, D., Petrosini, L., & Caltagirone, C. (2022) · Biomolecules · doi:10.3390/biom12050667 · Cited by 33
Roth, S., Burnie, N., Suridjan, I., Yan, J. T., & Carboni, M. (2023) · Journal of Alzheimer s Disease Reports · doi:10.3233/adr230007 · Cited by 32
Bi, X.-A., Wang, Y., Luo, S., Chen, K., Xing, Z., & Xu, L. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3212700 · Cited by 29
Schilling, L. P., Balthazar, M. L. F., Radanovic, M., Forlenza, O. V., Silagi, M. L., Smid, J., Barbosa, B. J. A. P., Frota, N. A. F., de Souza, L. C., Vale, F. A. C., et al. (2022) · Dementia & Neuropsychologia · doi:10.1590/1980-5764-dn-2022-s102en · Cited by 29
End-to-end automatic pathology localization for Alzheimer’s disease diagnosis using structural MRI
Cao, G., Zhang, M., Wang, Y., Zhang, J., Han, Y., Xu, X., Huang, J., & Kang, G. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107110 · Cited by 28
Saville, L., Wu, L. M., Habtewold, J. Z., Cheng, Y., Gollen, B., Mitchell, L. R., Stuart-Edwards, M., Haight, T., Mohajerani, M. H., & Zovoilis, A. (2024) · Genome biology · doi:10.1186/s13059-024-03375-8 · Cited by 27
Xiong, S., Hou, N., Tang, F., Li, J., & Deng, H. (2023) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-023-01186-8 · Cited by 29
Yu, Z., Si, X., Chen, Y., Chao, Y., Lin, C., Li, S., Zhang, X., Ming, D., & Li, Q. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab407 · Cited by 26
Alzheimer's Disease: Many Failed Trials, So Where Do We Go from Here?
Reiss, A. B., Glass, A. D., Wısnıewskı, T., Wolozin, B. L., Gomolin, I. H., Pinkhasov, A., Leon, J. D., & Stecker, M. M. (2020) · Journal of Investigative Medicine · doi:10.1136/jim-2020-001297 · Cited by 26
Göschel, L., Kurz, L., Dell’Orco, A., Köbe, T., Körtvelyessy, P., Fillmer, A., Aydın, S., Riemann, L. T., Wang, H., Ittermann, B., et al. (2023) · NeuroImage Clinical · doi:10.1016/j.nicl.2023.103439 · Cited by 24
Glucometabolic Changes Are Associated with Structural Gray Matter Alterations in Prodromal Dementia
Gentreau, M., Reynès, C., Sabatier, R., Maller, J. J., Meslin, C., Deverdun, J., Bars, E. L., Michel Raymond, A., Berticat, C., & Artéro, S. (2022) · Journal of Alzheimer s Disease · doi:10.3233/jad-220490 · Cited by 23
T1-Weighted Imaging-Based Hippocampal Radiomics in the Diagnosis of Alzheimer's Disease
Yin, T. T., Cao, M., Yu, J. C., Shi, T. Y., Mao, X., Wei, X., & Jia, Z. Z. (2024) · Academic Radiology · doi:10.1016/j.acra.2024.06.012 · Cited by 22
Visual-Saliency-Based Abnormality Detection for MRI Brain Images—Alzheimer’s Disease Analysis
Andrushia, A. D., Sagayam, K. M., Dang, H., Pomplun, M., & Quach, L. (2021) · Applied Sciences · doi:10.3390/app11199199 · Cited by 22
Said, A., & Göker, H. (2023) · Cognitive Neurodynamics · doi:10.1007/s11571-023-10010-y · Cited by 21
Chéour, S., Chéour, C., Gendreau, T., Bouazizi, M., Singh, K. P., Saeidi, A., Tao, D., Supriya, R., Bragazzi, N. L., Baker, J. S., et al. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2023.1216052 · Cited by 23
Couvy‐Duchesne, B., Frouin, V., Bouteloup, V., Koussis, N. C., Sidorenko, J., Jiang, J., Wink, A. M., Lorenzini, L., Barkhof, F., Trollor, J. N., et al. (2025) · Human Brain Mapping · doi:10.1002/hbm.70089 · Cited by 21
Wang, X., Peng, L., Zhan, S., Yin, X., Huang, L. T., Huang, J., Yang, J., Zhang, Y., Zeng, Y., & Liang, S. (2024) · Asian Journal of Psychiatry · doi:10.1016/j.ajp.2024.103921 · Cited by 20
Neurological update: neuroimaging in dementia
Rittman, T. (2020) · Journal of Neurology · doi:10.1007/s00415-020-10040-0 · Cited by 23
Epidemiology of Dementia in China in 2010–2020: A Systematic Review and Meta-Analysis
Yin, Y., Tam, H. L., Quint, J. K., Chen, M., Ding, R., & Zhang, X. (2024) · Healthcare · doi:10.3390/healthcare12030334 · Cited by 19
Corneal nerve loss predicts dementia in patients with mild cognitive impairment
Ponirakis, G., Hamad, H. K. A., Omar, D. A. M., Petropoulos, I. N., Khan, A., Gad, H., Chandran, M., Gadelseed, M., Elsotouhy, A., Ramadan, M., et al. (2023) · Annals of Clinical and Translational Neurology · doi:10.1002/acn3.51747 · Cited by 17
Mohanty, S., Mohanty, P. K., Trivedi, C. G., Assadourian, J. N., Mayedo, A., MacDonald, B. C., Rocca, D. G. D., Gianni, C., Horton, R., Al‐Ahmad, A., et al. (2021) · Journal of the American Heart Association · doi:10.1161/jaha.120.019664 · Cited by 17
Kapasouri, E. M., Ioannidis, D. C., Cameron, D., Vassiliou, V. S., & Hornberger, M. (2022) · Diagnostics · doi:10.3390/diagnostics12122967 · Cited by 17
Sohail, M. U., Batool, R. M., Saad, M., Waqas, S. A., Noushad, M. A., Sohail, M. O., Bates, M. G. D., Ahmed, R., & Ripley, D. P. (2025) · Journal of Cardiovascular Electrophysiology · doi:10.1111/jce.16644 · Cited by 16
Quantification of perforant path fibers for early detection of Alzheimer's disease
Uchida, Y., Hou, Z., Gómez-Isaza, L., Luongo, M., Troncoso, J. C., Miller, M. I., Mori, S., & Oishi, K. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.70142 · Cited by 16
Kaipainen, A. L., Jääskeläinen, O., Liu, Y., Haapalinna, F., Nykänen, N., Vanninen, R. L., Koivisto, A. M., Julkunen, V., Remes, A. M., & Herukka, S. (2020) · Journal of Alzheimer s Disease · doi:10.3233/jad-200175 · Cited by 15
Vali, Y., Leeflang, M., & Bossuyt, P. M. (2021) · Systematic Reviews · doi:10.1186/s13643-021-01744-z · Cited by 15
Custodio, N., Málaga, M., Chambergo‐Michilot, D., Montesinos, R., Moron, E., Vences, M. A., Huilca, J. C., Lira, D., Failoc‐Rojas, V. E., & Diaz, M. M. (2022) · Frontiers in Neurology · doi:10.3389/fneur.2022.962192 · Cited by 14
Alzheimer's disease classification for MRI images using Convolutional Neural Networks
Esam, S., & Mohammed, A. (2024) · International Conference on Computing and Informatics (ICCI) · doi:10.1109/icci61671.2024.10485049 · Cited by 13
Piersson, A. D., Ibrahim, B., Suppiah, S., Mohamad, M., Hassan, H. A., Omar, N. F., Ibrahim, M. I., Yusoff, A. N., Ibrahim, N., Saripan, M. I., et al. (2021) · PLoS ONE · doi:10.1371/journal.pone.0252883 · Cited by 14
Chow, C. L. J., Shum, J. S. F., Hui, K. T. P., Lin, A. F. C., & Chu, E. C. (2023) · Cureus · doi:10.7759/cureus.37022 · Cited by 13
Sui, S. X., Hordacre, B. G., & Pasco, J. A. (2021) · Biomedicines · doi:10.3390/biomedicines9020223 · Cited by 14
Mai, Y., Cao, Z., Zhao, L., Yu, Q., Xu, J., Liu, W., Liu, B., Tang, J., Luo, Y., Liao, W., et al. (2023) · CNS Neuroscience & Therapeutics · doi:10.1111/cns.14492 · Cited by 12
Hippocampal volume reduction is associated with direct measure of insulin resistance in adults
Frangou, S., Abbasi, F., Watson, K., Haas, S. S., Antoniades, M., Modabbernia, A., Myoraku, A., Robakis, T. K., & Rasgon, N. (2021) · Neuroscience Research · doi:10.1016/j.neures.2021.07.006 · Cited by 13
Consensus on rapid screening for prodromal Alzheimer’s disease in China
Huang, L., Li, Q., Lu, Y., Pan, F., Cui, L., Wang, Y., Miao, Y., Chen, T., Li, Y., Wu, J., et al. (2024) · General Psychiatry · doi:10.1136/gpsych-2023-101310 · Cited by 12
Daou, L., Alayli, A. E., Constantinos, F., Dib, G., & Barakat, M. (2023) · Biomarkers in Neuropsychiatry · doi:10.1016/j.bionps.2023.100069 · Cited by 11
Tonietto, M., Sotolongo‐Grau, Ó., Roé-Vellvé, N., Bullich, S., Tartari, J. P., Sanabria, Á., García‐Sánchez, A., Borroni, E., Galli, C., Pérez-Martínez, E., et al. (2024) · Alzheimer s Research & Therapy · doi:10.1186/s13195-024-01622-5 · Cited by 12
Devi, A., & Latha, M. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.126553 · Cited by 11
Accuracy of Raman spectroscopy in the diagnosis of Alzheimer's disease
Xu, Y., Pan, X., Li, H., Cao, Q., Xu, F., & Zhang, J. (2023) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2023.1112615 · Cited by 11
Performance Enhancement of Alzheimer's Disease Diagnosis Using Generative Adversarial Network
Ching, W. P., Abdullah, S. S., Shapiai, M. I., & Islam, A. K. M. M. (2024) · Journal of Advanced Research in Applied Sciences and Engineering Technology · doi:10.37934/araset.45.2.191201 · Cited by 10
Xu, Q., Kim, Y., Chung, K., Schulz, P. E., & Gottlieb, A. (2024) · JMIR Formative Research · doi:10.2196/55575 · Cited by 12
Shang, X., Wang, W., Tian, L., Shi, D., Huang, Y., Zhang, X., Zhu, Z., Zhang, X., Liu, J., Tang, S., et al. (2024) · Archives of Gerontology and Geriatrics · doi:10.1016/j.archger.2024.105546 · Cited by 10
Chaudhary, S., Chao, H. H., Agarwal, K., Gupta, S., & Li, C. R. (2025) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2025.106270 · Cited by 11
Sharma, N., Kim, D., Sharma, H., Kim, M. I., Lee, H., Kim, M., Ryoo, N., Kang, M. J., Pyun, J., Park, Y. H., et al. (2025) · Biomarker Research · doi:10.1186/s40364-025-00829-4 · Cited by 10
Functional activity level reported by an informant is an early predictor of Alzheimer’s disease
Vik, A., Kociński, M., Rye, I., Lundervold, A. J., & Lundervold, A. S. (2023) · BMC Geriatrics · doi:10.1186/s12877-023-03849-7 · Cited by 13
Nie, D., He, X., Zheng, H., Deng, D., He, F., Li, R., Ni, X., Li, S., & Xu, F. (2024) · European Journal of Nutrition · doi:10.1007/s00394-023-03312-8 · Cited by 10
Ryu, D.-W., Hong, Y. J., Cho, J. H., Kwak, K., Lee, J. M., Shim, Y. S., Youn, Y. C., & Yang, D. W. (2022) · Brain Imaging and Behavior · doi:10.1007/s11682-022-00678-x · Cited by 10
Misiura, M. B., Munkombwe, C., Igwe, K. C., Verble, D. D., Likos, K. D. S., Minto, L. R., Bartlett, A., Zetterberg, H., Turner, J. A., Dotson, V. M., et al. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.14051 · Cited by 9
Associations of Accelerated Biological Aging With Dementia and the Mediation Role of Brain Structure
Bo, Y., Sun, L., Hou, S., Zhang, F., Zhang, H., Feng, X., Zhuo, S., Feng, S., Chang, H., Zhang, X., et al. (2025) · Neurology · doi:10.1212/wnl.0000000000213616 · Cited by 9
Chavan, A. B., Patil, S. R., Patel, A. M., Chaugule, S. V., & Gharal, S. K. (2023) · Journal for Research in Applied Sciences and Biotechnology · doi:10.55544/jrasb.2.4.8 · Cited by 9
Advances in nanoprobes for molecular MRI of Alzheimer's disease
Parekh, P. A., Badachhape, A., Tanifum, E. A., Annapragada, A., & Ghaghada, K. B. (2024) · Wiley Interdisciplinary Reviews Nanomedicine and Nanobiotechnology · doi:10.1002/wnan.1946 · Cited by 9
Jiang, Y., Wang, P., Wen, J., Wang, J., Li, H., & Biswal, B. B. (2022) · Brain Structure and Function · doi:10.1007/s00429-022-02521-x · Cited by 9
Multimodal magnetic resonance imaging analysis of early mild cognitive impairment
Xu, S., Fan, Y., Mao, C., Hu, Z., Yang, Z., Qu, L., Xu, Y., Yu, L., Zhu, X., & Initiative, A. D. N. (2025) · Journal of Alzheimer s Disease · doi:10.1177/13872877251321187 · Cited by 8
Pathological Mechanisms and Molecular Imaging Advances in Alzheimer’s Disease
Liu, C., Meng, Y., Liu, R., Wang, Z., & Zhao, H. (2025) · Clinical Interventions in Aging · doi:10.2147/cia.s534015 · Cited by 8
Dong, X., Li, Y., Hao, J., Zhou, P., Yang, C., Ai, Y., He, M. Y., Zhang, W., & Hu, H. (2025) · BMC Neurology · doi:10.1186/s12883-025-04404-0 · Cited by 8
Bayahya, A. Y., Banjar, H. R., Talabay, O., & Alamri, S. H. (2025) · Scientific Reports · doi:10.1038/s41598-025-27914-w · Cited by 9
Zhou, Y., Wei, L., Gao, S., Wang, J., & Hu, Z. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1209378 · Cited by 10
Angrist, M. H., Yang, A., Kantor, B., & Chiba‐Falek, O. (2020) · Life Sciences Society and Policy · doi:10.1186/s40504-020-00106-2 · Cited by 8
Quek, Y., Fung, Y. L., Cheung, M. W. ‐., Vogrin, S. J., Collins, S. J., & Bowden, S. C. (2021) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.28037 · Cited by 8
Alotaibi, S. D., & Alharbi, A. A. K. (2025) · Scientific Reports · doi:10.1038/s41598-025-09124-6 · Cited by 7
Keshavarz, S. (., Inanlu, M. J., Omidfar, K., & Bazargan, V. (2025) · Microchemical Journal · doi:10.1016/j.microc.2025.114061 · Cited by 7
Gao, N., Liu, Z., Deng, Y., Chen, H., Ye, C., Yang, Q., & Ma, T. (2023) · Human Brain Mapping · doi:10.1002/hbm.26460 · Cited by 7
Optimizing a 3D convolutional neural network to detect Alzheimer’s disease based on MRI
Alarjani, M., & Almuaibed, A. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.3129 · Cited by 6
Metadata
- Authors
- Lombardi, G., Crescioli, G., Cavedo, E., Lucenteforte, E., Casazza, G., Bellatorre, A.-G., Lista, C., Costantino, G., Frisoni, G., Virgili, G., & Filippini, G.
- Affiliations
- University of Florence (IT)
- Sorbonne Université (FR)
- University of Pisa (IT)
- University of Milan (IT)
- Azienda sanitaria universitaria Friuli Centrale (IT)
- Fondazione IRCCS Istituto Neurologico Carlo Besta (IT)
- University of Geneva (CH)
- ORCID
- Venue
- Cochrane Database of Systematic Reviews
- Publisher
- Wiley
- Published
- 2020-03-02
- Volume
- 2020
- Issue
- 3
- Pages
- CD009628-CD009628
- ISSN
- 1361-6137 (ISSN-L)
- 1465-1858
- PMC
- PMC7059964
- OpenAlex
- W3009532950
- Cited by
- 214
- References
- 391
- Citations by year
- 2026: 16 · 2025: 51 · 2024: 47 · 2023: 47 · 2022: 21 · 2021: 19 · 2020: 13
- FWCI
- 4.18
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Cochrane Database of Systematic Reviews (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Florence Research (University of Florence) (submittedVersion, other-oa)
- Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano) (submittedVersion, other-oa)
- CINECA IRIS Institutial research information system (University of Pisa) (submittedVersion)
- Florence Research (University of Florence) (submittedVersion)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Oncology › Cancer-related cognitive impairment studies
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Keywords
- structural MRI, Alzheimer's disease, mild cognitive impairment, early diagnosis, dementia, diagnostic accuracy, brain atrophy, hippocampal atrophy, medial temporal lobe, entorhinal cortex, amygdala, lateral ventricle, gray matter atrophy, volumetric MRI, visual assessment, conversion to dementia, white matter hyperintensities, amyloid imaging, neurodegeneration biomarkers, medial temporal lobe atrophy
- MeSH terms
- Aged, Aged, 80 and over, Alzheimer Disease, Atrophy, Brain, Hippocampus, Humans, Magnetic Resonance Imaging, Middle Aged, Organ Size, Prospective Studies, Sensitivity and Specificity, Temporal Lobe, Disease Progression, Entorhinal Cortex, Lateral Ventricles, Neuroimaging, Cognitive Dysfunction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
