Accuracy of MRI classification algorithms in a tertiary memory center clinical routine cohort
Open accessAbstract
BACKGROUND: Automated volumetry software (AVS) has recently become widely available to neuroradiologists. MRI volumetry with AVS may support the diagnosis of dementias by identifying regional atrophy. Moreover, automatic classifiers using machine learning techniques have recently emerged as promising approaches to assist diagnosis. However, the performance of both AVS and automatic classifiers have been evaluated mostly in the artificial setting of research datasets. OBJECTIVE: Our aim was to evaluate the performance of two AVS and an automatic classifier in the clinical routine condition of a memory clinic. METHODS: We studied 239 patients with cognitive troubles from a single memory center cohort. Using clinical routine T1-weighted MRI, we evaluated the classification performance of: 1) univariate volumetry using two AVS (volBrain and Neuroreader™); 2) Support Vector Machine (SVM) automatic classifier, using either the AVS volumes (SVM-AVS), or whole gray matter (SVM-WGM); 3) reading by two neuroradiologists. The performance measure was the balanced diagnostic accuracy. The reference standard was consensus diagnosis by three neurologists using clinical, biological (cerebrospinal fluid) and imaging data and following international criteria. RESULTS: Univariate AVS volumetry provided only moderate accuracies (46% to 71% with hippocampal volume). The accuracy improved when using SVM-AVS classifier (52% to 85%), becoming close to that of SVM-WGM (52 to 90%). Visual classification by neuroradiologists ranged between SVM-AVS and SVM-WGM. CONCLUSION: In the routine practice of a memory clinic, the use of volumetric measures provided by AVS yields only moderate accuracy. Automatic classifiers can improve accuracy and could be a useful tool to assist diagnosis.
Integrity signals
Registry
Cites
Statistics
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?
Morin et al. (2020) demonstrated that multimodal AI excels at differentiating diseases with distinct MRI signatures (AD/FTD/semantic dementia).
“Morin et al. (2020) demonstrated that multimodal AI excels at differentiating diseases with distinct MRI signatures (AD/FTD/semantic dementia)”
From Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine (2026) · cites this paper as Morin et al., 2020
Morin et al. (2020) demonstrated that multimodal AI struggles with atypically presenting pathologies like DLB.
“but struggles with atypically presenting pathologies like DLB”
From Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine (2026) · cites this paper as Morin et al., 2020
Cites
Voxel-Based Morphometry—The Methods
Ashburner, J., & Friston, K. J. (2000) · NeuroImage · doi:10.1006/nimg.2000.0582 · Cited by 8,676 · cited 1× · see citing works
“All T1-weighted MRI images were segmented into GM, WM, and CSF tissues maps using the Statistical Parametric Mapping unified segmentation routine with the default parameters (SPM12, London, UK http://www.fil.ion.ucl.ac.uk/spm/) [28].”
Diagnostic and Statistical Manual of Mental Disorders
Siegel, L. J., & Fletcher‐Janzen, E. (2008) · Wiley eBooks · doi:10.1002/9780470373699.speced0655 · Cited by 8,468 · cited 1× · see citing works
“…(FTD) [2], primary progressive aphasia (PPA) of the logopenic (lv-PPA), semantic (SD) or non-fluent/agrammatic (nf-PPA) [22] variant, corticobasal syndrome (CBD) [4], progressive supranuclear palsy (PSP) [23], posterior cortical atrophy (PCA) [24], Lewy body dementia (LBD) [25], and depression [26].”
A fast diffeomorphic image registration algorithm
Ashburner, J. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.07.007 · Cited by 8,122 · cited 1× · see citing works
“A population template was calculated from GM and WM tissue maps using the DARTEL diffeomorphic registration algorithm with the default parameters [29].”
Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia
Rascovsky, K., Hodges, J. R., Knopman, D., Mendez, M. F., Kramer, J. H., Neuhaus, J., van Swieten, J. C., Seelaar, H., Dopper, E. G. P., Onyike, C. U., et al. (2011) · Brain · doi:10.1093/brain/awr179 · Cited by 5,221 · cited 2× · see citing works
“Among neuroimaging biomarkers, MRI is the less invasive, most widely available, cost-effective, is systematically recommended in dementia, and can provide supportive criteria for many neurodegenerative conditions [2–4].”
Classification of primary progressive aphasia and its variants
Gorno-Tempini, M. L., Hillis, A. E., Weintraub, S., Kertesz, A., Mendez, M., Cappa, S. F., Ogar, J. M., Rohrer, J. D., Black, S., Boeve, B. F., et al. (2011) · Neurology · doi:10.1212/wnl.0b013e31821103e6 · Cited by 5,141 · cited 1× · see citing works
“For each patient, the diagnosis was assessed by a group of 3 neurologists based on clinical, biological and imaging data, following international consensus criteria for AD (IWG-2) [21], frontotemporal dementia (FTD) [2], primary progressive aphasia (PPA) of the logopenic (lv-PPA), semantic (SD) or non-fluent/agrammati…”
Diagnosis and management of dementia with Lewy bodies: Third report of the DLB consortium
McKeith, I. G., Dickson, D. W., Lowe, J., Emre, M., O'Brien, J. T., Feldman, H., Cummings, J., Duda, J. E., Lippa, C., Perry, E. K., et al. (2005) · Neurology · doi:10.1212/01.wnl.0000187889.17253.b1 · Cited by 5,069 · see citing works
Self-citationResearch criteria for the diagnosis of Alzheimer's disease: revising the NINCDS–ADRDA criteria
Dubois, B., Feldman, H. H., Jacova, C., DeKosky, S. T., Barberger-Gateau, P., Cummings, J., Delacourte, A., Galasko, D., Gauthier, S., Jicha, G., et al. (2007) · The Lancet Neurology · doi:10.1016/s1474-4422(07)70178-3 · Cited by 4,411 · cited 1× · see citing works
“Among neuroimaging biomarkers, MRI is the less invasive, most widely available, cost-effective, is systematically recommended in dementia and can provide supportive criteria for many neurodegenerative conditions [2–4].”
Self-citationAdvancing research diagnostic criteria for Alzheimer's disease: the IWG-2 criteria
Dubois, B., Feldman, H. H., Jacova, C., Hampel, H., Molinuevo, J. L., Blennow, K., DeKosky, S. T., Gauthier, S., Selkoe, D., Bateman, R., et al. (2014) · The Lancet Neurology · doi:10.1016/s1474-4422(14)70090-0 · Cited by 3,321 · cited 1× · see citing works
“…by a group of three neurologists based on clinical, biological, and imaging data, following international consensus criteria for AD (IWG-2) [21], fron-totemporal dementia (FTD) [2], primary progressive aphasia (PPA) of the logopenic (lv-PPA), semantic (SD) or non-fluent/agrammatic (nf-PPA)…”
Self-citationLitvan, I., Agid, Y., Calne, D., Campbell, G., Dubois, B., Duvoisin, R. C., Goetz, C. G., Golbe, L. I., Grafman, J., Growdon, J. H., et al. (1996) · Neurology · doi:10.1212/wnl.47.1.1 · Cited by 2,538 · cited 1× · see citing works
“…(FTD) [2], primary progressive aphasia (PPA) of the logopenic (lv-PPA), semantic (SD) or non-fluent/agrammatic (nf-PPA) [22] variant, corticobasal syndrome (CBD) [4], progressive supranuclear palsy (PSP) [23], posterior cortical atrophy (PCA) [24], Lewy body dementia (LBD) [25], and depression [26].”
Self-citationCriteria for the diagnosis of corticobasal degeneration
Armstrong, M. J., Litvan, I., Lang, A. E., Bak, T. H., Bhatia, K. P., Borroni, B., Boxer, A. L., Dickson, D. W., Grossman, M., Hallett, M., et al. (2013) · Neurology · doi:10.1212/wnl.0b013e31827f0fd1 · Cited by 1,904 · cited 2× · see citing works
“Among neuroimaging biomarkers, MRI is the less invasive, most widely available, cost-effective, is systematically recommended in dementia, and can provide supportive criteria for many neurodegenerative conditions [2–4].”
Scheltens, P., Leys, D., Barkhof, F., Huglo, D. G., Weinstein, H. C., Vermersch, P., Kuiper, M. A., Steinling, M., Wolters, E. C., & Valk, J. (1992) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp.55.10.967 · Cited by 1,781 · cited 2× · see citing works
“Assessment of regional atrophy using MRI in dementia has been extensively studied using visual, semi-quantitative ratings [5–7], manual volumetry, and more recently Automated Volumetry Software (AVS) [8–11].”
Hippocampal Volume Reduction in Major Depression
Bremner, J. D., Narayan, M., Anderson, E. R., Staib, L. H., Miller, H. L., & Charney, D. S. (2000) · American Journal of Psychiatry · doi:10.1176/ajp.157.1.115 · Cited by 1,670 · cited 1× · see citing works
“One explanation could be the atrophy usually described in depression [35].”
The Alzheimer's Disease Neuroimaging Initiative
Mueller, S. G., Weiner, M. W., Thal, L. J., Petersen, R. C., Jack, C., Jagust, W., Trojanowski, J. Q., Toga, A. W., & Beckett, L. (2005) · Neuroimaging Clinics of North America · doi:10.1016/j.nic.2005.09.008 · Cited by 1,525 · see citing works
Scheltens, P., & van de Pol, L. A. (2012) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp-2012-302562 · Cited by 1,275 · see citing works
Weaver, D. (2014) · Neurology · doi:10.1212/wnl.0000000000000738 · Cited by 1,191 · cited 3×
“These tools, which are progressively being implemented in clinical MRI software, have only been evaluated in research settings [10, 11, 13, 14].”
Automatic classification of MR scans in Alzheimer’s disease.
Kloppel, S., Stonnington, C. M., Chu, C., Draganski, B., Scahill, R. I., Rohrer, J. D., Fox, N. C., Jack, C. R., Ashburner, J., & Frackowiak, R. S. J. (2008) · Brain · doi:10.1093/brain/awm319 · Cited by 1,170 · see citing works
Cuingnet, R., Gérardin, E., Tessieras, J., Auzias, G., Lehéricy, S., Habert, M., Marie Chupin, A., Benali, H., & Olivier Colliot, A. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.013 · Cited by 962 · cited 3× · see citing works
“Please refer to Cuingnet et al. [15] for more details.”
Self-citationPatterns of brain atrophy in frontotemporal dementia and semantic dementia
Rosen, H. J., Gorno–Tempini, M. L., Goldman, W. P., Perry, R. J., Schuff, N., Weiner, M., Feiwell, R., Kramer, J. H., & Miller, B. L. (2002) · Neurology · doi:10.1212/wnl.58.2.198 · Cited by 841 · cited 2× · see citing works
“As expected, the classifier, as well as the neuroradiologists, performed better on dementia known to have a strongly specific atrophy pattern (such as SD or FTD) [7] and worse on dementia with less specific atrophy patterns (LBD, CBD) [33,34].”
Presymptomatic hippocampal atrophy in Alzheimer's disease: A longitudinal MRI study
Fox, N. C., Warrington, E. K., Freeborough, P. A., Hartikainen, P., Kennedy, A. M., Stevens, J. M., & Rossor, M. N. (1996) · Brain · doi:10.1093/brain/119.6.2001 · Cited by 656 · see citing works
volBrain: An Online MRI Brain Volumetry System
Manjón, J. V., & Pierrick Coupé, A. (2016) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2016.00030 · Cited by 632 · cited 2× · see citing works
“AVS such as Neuroreader™ [10] and volBrain [12] provide volumetric measures of anatomical structures.”
Self-citationClinical, genetic, and neuropathologic characteristics of posterior cortical atrophy
Tang‐Wai, D. F., Graff‐Radford, N. R., Boeve, B. F., Dickson, D. W., Parisi, J. E., Crook, R., Caselli, R. J., Knopman, D. S., & Petersen, R. C. (2004) · Neurology · doi:10.1212/01.wnl.0000140289.18472.15 · Cited by 624 · cited 1× · see citing works
“…(FTD) [2], primary progressive aphasia (PPA) of the logopenic (lv-PPA), semantic (SD) or non-fluent/agrammatic (nf-PPA) [22] variant, corticobasal syndrome (CBD) [4], progressive supranuclear palsy (PSP) [23], posterior cortical atrophy (PCA) [24], Lewy body dementia (LBD) [25], and depression [26].”
Support vector machine-based classification of Alzheimer’s disease from whole-brain anatomical MRI
Magnin, B., Mesrob, L., Kinkingnéhun, S., Pélégrini‐Issac, M., Colliot, O., Sarazin, M., Dubois, B., Lehéricy, S., & Benali, H. (2008) · Neuroradiology · doi:10.1007/s00234-008-0463-x · Cited by 474 · see citing works
Self-citationEarly-Versus Late-Onset Alzheimer's Disease: More than Age Alone
Koedam, E. L. G. E., Lauffer, V., van der Vlies, A. E., van der Flier, W. M., Scheltens, P., & Pijnenburg, Y. A. L. (2010) · Journal of Alzheimer's Disease · doi:10.3233/jad-2010-1337 · Cited by 452 · cited 1× · see citing works
“atrophy patterns depend mostly on the age of onset of AD [27], the AD group was separated into Early-onset”
Alzheimer's disease diagnosis in individual subjects using structural MR images: Validation studies
Vemuri, P., Gunter, J. L., Senjem, M. L., Whitwell, J. L., Kantarci, K., Knopman, D. S., Boeve, B. F., Petersen, R. C., & Jack, C. R. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.09.073 · Cited by 450 · cited 1× · see citing works
“Automatic classifiers have also mainly been evaluated in research settings, with standardized MRI acquisition and focusing on a single type of dementia (most often AD) and age-matched healthy controls [15–19].”
Marie Chupin, A., Gérardin, E., Cuingnet, R., Boutet, C., Lemieux, L., Lehéricy, S., Benali, H., Garnero, L., & Colliot, O. (2009) · Hippocampus · doi:10.1002/hipo.20626 · Cited by 341 · cited 1× · see citing works
“In previous studies, the role of the hippocampus has been mainly evaluated for the diagnosis of AD versus controls or in mild cognitive impairment populations to identify patients who will later progress to AD [8, 9, 11, 36, 37].”
Self-citationMesulam, M.-M., Weintraub, S., Rogalski, E. J., Wieneke, C., Geula, C., & Bigio, E. H. (2014) · Brain · doi:10.1093/brain/awu024 · Cited by 334 · cited 1× · see citing works
“Anotherchallengingsituationcan bethedisentanglementofPPAvariantswhichall includepredominantlanguageimpairmentbutare associatedtovariableneuropathologicallesions[32].”
Accuracy of dementia diagnosis: a direct comparison between radiologists and a computerized method
Kloppel, S., Stonnington, C. M., Barnes, J., Chen, F., Chu, C., Good, C. D., Mader, I., Mitchell, L. A., Patel, A. C., Roberts, C. C., et al. (2008) · Brain · doi:10.1093/brain/awn239 · Cited by 271 · see citing works
Imaging correlates of pathology in corticobasal syndrome
Whitwell, J. L., Jack, C. R., Boeve, B. F., Parisi, J. E., Ahlskog, J. E., Drubach, D. A., Senjem, M. L., Knopman, D. S., Petersen, R. C., Dickson, D. W., et al. (2010) · Neurology · doi:10.1212/wnl.0b013e3181feb2e8 · Cited by 241 · cited 5× · see citing works
“For each patient, the diagnosis was assessed by a group of three neurologists based on clinical, biological, and imaging data, following international consensus criteria for AD (IWG-2) [21], fron-totemporal dementia (FTD) [2], primary progressive aphasia (PPA) of the logopenic (lv-PPA), semantic (SD) or non-fluent/agra…”
InfluentialIndividual patient diagnosis of AD and FTD via high-dimensional pattern classification of MRI
Davatzikos, C. A., Resnick, S. M., Wu, X., Parmpi, P., & Clark, C. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.03.050 · Cited by 240 · cited 1× · see citing works
“Two studies evaluated SVM classification between AD and FTD in a research setting [16,30].”
Cui, Y. J., Liu, B., Luo, S., Zhen, X., Fan, M., Liu, T., Zhu, W., Park, M., Jiang, T., Jin, J. S., et al. (2011) · PLoS ONE · doi:10.1371/journal.pone.0021896 · Cited by 237 · cited 1× · see citing works
“In previous studies, the role of the hippocampus has been mainly evaluated for the diagnosis of AD versus controls or in mild cognitive impairment populations to identify patients who will later progress to AD [8, 9, 11, 36, 37].”
Hippocampal atrophy on MRI in frontotemporal lobar degeneration and Alzheimer’s disease
van de Pol, L. A., Hensel, A., van der Flier, W. M., Visser, P., Pijnenburg, Y., Barkhof, F., Gertz, H.-J., & Scheltens, P. (2005) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp.2005.075341 · Cited by 223 · see citing works
Patterns of Cerebral Atrophy in Dementia with Lewy Bodies Using Voxel-Based Morphometry
Burton, E. J., Karas, G. B., Paling, S. M., Barber, R., Williams, E. D., Ballard, C. G., McKeith, I. G., Scheltens, P., Barkhof, F., & O'Brien, J. T. (2002) · NeuroImage · doi:10.1006/nimg.2002.1197 · Cited by 214 · cited 1× · see citing works
“As expected, the classifier, as well as the neuroradiologists, performed better on dementia known to have a strongly specific atrophy pattern (such as SD or FTD) [7] and worse on dementia with less specific atrophy patterns (LBD, CBD) [33, 34].”
Differential diagnosis of neurodegenerative diseases using structural MRI data
Koikkalainen, J., Rhodius-Meester, H., Tolonen, A., Barkhof, F., Tijms, B., Lemstra, A. W., Tong, T., Guerrero, R., Schuh, A., Ledig, C., et al. (2016) · NeuroImage: Clinical · doi:10.1016/j.nicl.2016.02.019 · Cited by 193 · cited 2× · see citing works
“To the best of our knowledge, only one study has previously evaluated SVM classifiers in clinical routine with various types of dementia [31].”
de Souza, L. C., Marie Chupin, A., Bertoux, M., Lehéricy, S., Dubois, B., Lamari, F., Ber, I. L., Bottlaender, M., Olivier Colliot, A., & Sarazin, M. (2013) · Journal of Alzheimer s Disease · doi:10.3233/jad-122293 · Cited by 87 · cited 1× · see citing works
“In our study, we evaluated MRI measurements in AD versus other dementia (FTD for instance), where hippocampal volumetry alone is known to perform poorly [38, 39].”
Self-citationTeichmann, M., Epelbaum, S., Samri, D., Lévy, M., Michon, A., Hampel, H. J., Lamari, F., & Dubois, B. (2017) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2016.12.014 · Cited by 86 · cited 1× · see citing works
“All subjects were recruited retrospectively in a tertiary academic expert memory center (Institute for Memory and Alzheimer’s disease – Department of Neurology, Piti´e-Salpˆetri`ere University Hospital) from the ClinAD cohort [20].”
Self-citationTanpitukpongse, T. P., Mazurowski, M. A., Ikhena, J., & Petrella, J. R. (2017) · American Journal of Neuroradiology · doi:10.3174/ajnr.a5061 · Cited by 62 · see citing works
Coupé, P., Fonov, V. S., Bernard, C., Zandifar, A., Eskildsen, S., Helmer, C., Manjón, J., Amieva, H., Dartigues, J., Allard, M., et al. (2015) · Human Brain Mapping · doi:10.1002/hbm.22926 · Cited by 60 · see citing works
Self-citationQuantitative Neuroimaging Software for Clinical Assessment of Hippocampal Volumes on MR Imaging
Ahdidan, J., Raji, C. A., DeYoe, E. A., Mathis, J. R., Noe, K. Ø., Rimestad, J., Kjeldsen, T. K., Mosegaard, J., Becker, J. T., & López, O. L. (2015) · Journal of Alzheimer s Disease · doi:10.3233/jad-150559 · Cited by 53 · cited 3× · see citing works
“net) is a commercial clinical brain image analysis tool [10].”
Self-citationMesial Temporal Sclerosis: Accuracy of NeuroQuant versus Neuroradiologist
Azab, M., Carone, M., Ying, S., & Yousem, D. M. (2015) · American Journal of Neuroradiology · doi:10.3174/ajnr.a4313 · Cited by 45 · cited 2× · see citing works
“These tools, which are progressively being implemented in clinical MRI software, have only been evaluated in research settings [10, 11, 13, 14].”
Suppa, P., Initiative, A. A. D. N., Hampel, H. J., Spies, L., Fiebach, J. B., Dubois, B., & Buchert, R. (2015) · Journal of Alzheimer s Disease · doi:10.3233/jad-142280 · Cited by 30 · see citing works
Self-citationKeator, D. B., Fallon, J. H., Lakatos, A., Fowlkes, C. C., Potkin, S. G., Ihler, A., & Initiative, A. D. N. (2012) · Human Brain Mapping · doi:10.1002/hbm.22149 · Cited by 7 · see citing works
Flagged first, then by citations
2 of the 43 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Chicco, D., Tötsch, N., & Jurman, G. (2021) · BioData Mining · doi:10.1186/s13040-021-00244-z · Cited by 893 · see citing works
Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: a systematic review
Borchert, R. J., Azevedo, T., Badhwar, A., Bernal, J., Betts, M., Bruffaerts, R., Burkhart, M. C., Dewachter, I., Gellersen, H. M., Low, A., et al. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.13412 · Cited by 125 · see citing works
“In the few studies that tested traditional and AI approaches head-to-head, AI methods outperformed raw volumetric measurements, for example, against hippocampal volume for diagnosis 52,53 and for predicting conversion of MCI to AD.”
Burgos, N., & Colliot, O. (2020) · Current Opinion in Neurology · doi:10.1097/wco.0000000000000838 · Cited by 53 · see citing works
“In deep learning, the model f is made of a very large set of artificial neurons, organized into layers learning a hierarchy of representations.”
BackgroundSelf-citationBottani, S., Burgos, N., Maire, A., Wild, A., Ströer, S., Dormont, D., & Colliot, O. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102219 · Cited by 48 · see citing works
“, 2011), or clinical datasets of limited size (Morin et al., 2020; Zhang et al., 2019; Campese et al., 2019; Oh et al., 2019).”
BackgroundSelf-citationChagué, P., Marro, B., Fadili, S., Houot, M., Morin, A., Samper-González, J., Beunon, P., Arrivé, L., Dormont, D., Dubois, B., et al. (2021) · Journal of Neuroradiology · doi:10.1016/j.neurad.2020.04.004 · Cited by 31 · see citing works
Self-citationBarnard, L., Botha, H., Corriveau-Lecavalier, N., Graff-Radford, J., Dicks, E., Gogineni, V., Zhang, G., Burkett, B. J., Johnson, D. R., Huls, S. J., et al. (2025) · Neurology · doi:10.1212/wnl.0000000000213831 · Cited by 25 · see citing works
Yearley, A. G., Goedmakers, C. M. W., Panahi, A., Doucette, J. A., Rana, A., Ranganathan, K., & Smith, T. R. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102607 · Cited by 21 · see citing works
Borchert, R. J., Azevedo, T., Badhwar, A., Bernal, J., Betts, M. R., Bruffaerts, R., Burkhart, M. C., Dewachter, I., Gellersen, H. M., Low, A., et al. (2021) · medRxiv · doi:10.1101/2021.12.12.21267677 · Cited by 17 · see citing works
Topkan, T. A., Erdoğan, N., Barutcu, B., Cindil, E., Tali, E. T., & Karaman, Y. (2022) · Cognitive and Behavioral Neurology · doi:10.1097/wnn.0000000000000296 · Cited by 9 · see citing works
Dang, M., Liu, B., Chen, Y., & Zhang, Z. (2026) · Psychoradiology · doi:10.1093/psyrad/kkag007 · Cited by 7 · see citing works
Hedderich, D. M., Schmitz‐Koep, B., Schuberth, M., Schultz, V., Schlaeger, S., Schinz, D., Rubbert, C., Caspers, J., Zimmer, C., Grimmer, T., et al. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.971863 · Cited by 5 · see citing works
“, 2017) and moderate accuracy of automated identification of patients with neurodegenerative dementia disorders using support vector machines (Morin et al., 2020).”
BackgroundMaladie d'Alzheimer à un stade précoce : le rôle central de l'imagerie
Ströer, S., & Habert, M. (2020) · NPG. Neurologie, psychiatrie, gériatrie/NPG · doi:10.1016/s1627-4830(20)30197-5
Self-citation
- Authors
- Morin, A., Samper-Gonzalez, J., Bertrand, A., Ströer, S., Dormont, D., Mendes, A., Coupé, P., Ahdidan, J., Lévy, M., Samri, D., Hampel, H., Dubois, B., Teichmann, M., Epelbaum, S., & Colliot, O.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Sorbonne Universités, UPMC Univ Paris 06, Inserm, CNRS, ICM, Paris, France
- Assistance Publique – Hôpitaux de Paris (FR)
- Laboratoire Bordelais de Recherche en Informatique, Unit Mixte de Recherche CNRS (UMR 5800), PICTURA Research Group, Bordeaux, France
- Brainreader, Horsens, Denmark
- Inserm (FR)
- Corresponding author
- Colliot, Olivier
- ORCID
- Venue
- Journal of Alzheimer’s Disease
- Publisher
- SAGE Publications
- Published
- 2020-03-02
- Volume
- 74
- Issue
- 4
- Pages
- 1157-1166
- ISSN
- 1387-2877 (ISSN-L)
- 1875-8908
- OpenAlex
- W2767121381
- Cited by
- 25
- References
- 43
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 2 · 2023: 7 · 2022: 4 · 2021: 5 · 2020: 3 · 2019: 1
- FWCI
- 3.48
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Alzheimer s Disease (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- National Science Foundation
- European Commission
- Agence Nationale de la Recherche (NEUC-0001., ANR-14-CE15-0016, ANR-19-P3IA-0001, ANR-11-IDEX-0004, ANR-16-NEUC-0001)
- AXA Research Fund (ANR-10-IAIHU-06)
- Université Pierre et Marie Curie
- Assistance publique-Hôpitaux de Paris
- Fondation pour la Recherche sur Alzheimer
- National Institutes of Health (-0001)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- dementia diagnosis
- MeSH terms
- Aged, Algorithms, Alzheimer Disease, Brain, Cognition Disorders, Dementia, Female, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Retrospective Studies, Software, Reproducibility of Results, Neuroimaging, Support Vector Machine
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
