Evidence map›Paper›PMID 42179740›Full record

ArticleNeurology open access2026

Automated Imaging Differentiation for Dementia: Including Alzheimer Disease Dementia and Dementia with Lewy Bodies.

Robin Chen, Shannon Y Chiu, Jesse C DeSimone, Wei-En Wang, Angelos Barmpoutis, Corey T Mcmillan, Hamsanandini Radhakrishnan, David John Irwin, Lyles Clark, Kejal Kantarci and 2 more

Abstract read
PubMed Full text at PMC
In one paragraph

Article in Neurology open access, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Robin ChenJ. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida.
Shannon Y ChiuDepartment of Neurology, Mayo Clinic, Scottsdale, AZ.
Jesse C DeSimoneDepartment of Applied Physiology and Kinesiology, University of Florida.
Wei-En WangDepartment of Applied Physiology and Kinesiology, University of Florida.
Angelos BarmpoutisDigital Worlds Institute, University of Florida.
Corey T McmillanDepartment of Neurology, University of Pennsylvania.
Hamsanandini RadhakrishnanDepartment of Neurology, University of Pennsylvania.
David John IrwinDepartment of Neurology, University of Pennsylvania.
Lyles ClarkDepartment of Neurology, University of Pennsylvania.
Kejal KantarciDepartment of Radiology, Mayo Clinic, Rochester, MN.
Bradley F BoeveDepartment of Radiology, Mayo Clinic, Rochester, MN.
David E VaillancourtDepartment of Applied Physiology and Kinesiology, University of Florida.

Funding

Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0M
Technology and Remote Assessment CoreU19AG063911 · NIA · MAYO CLINIC ROCHESTER · PI WILLIAM W SEELEY · 2019 to 2026
$120.9M
Early Onset AD Consortium - the LEAD Study (LEADS)U01AG057195 · NIA · INDIANA UNIVERSITY INDIANAPOLIS · PI APOSTOLOVA, LIANA G, CARRILLO, MARIA C · 2018 to 2023
$70.3M
Smartphone-Based "Burst" Cognitive AssessmentsP01AG003991 · NIA · WASHINGTON UNIVERSITY · PI JOHN MORRIS · 1985 to 2026
$69.5M
The Clinical Significance of Incidental White Matter Lesions on MRI Amongst a Diverse Population with Cognitive Complaints (INDEED)U19NS120384 · NINDS · UNIVERSITY OF CALIFORNIA AT DAVIS · PI Kumar B. Rajan · 2020 to 2026
$61.9M
THERAPEUTIC EFFECTS OF INTRA-NASAL INSULIN DETEMIRP50AG005136 · NIA · UNIVERSITY OF WASHINGTON · PI GRABOWSKI, THOMAS J. · 1985 to 2019
$57.2M
The natural history of AB accumulation in preclinical ADP01AG026276 · NIA · WASHINGTON UNIVERSITY · PI MORRIS, JOHN · 2005 to 2025
$49.5M
SUPPLEMENT TO RUSH ALZHEIMERS DISEASE CENTER COREP30AG010161 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI BENNETT, DAVID ALAN · 1991 to 2020
$49.1M
Satellite Diagnostic and Treatment Clinic CoreP50AG008702 · NIA · COLUMBIA UNIV NEW YORK MORNINGSIDE · PI DE JAGER, PHILIP L · 1989 to 2019
$46.3M
National Alzheimer's Coordinating CenterU24AG072122 · NIA · UNIVERSITY OF WASHINGTON · PI STEPHENS, KARI A · 2021 to 2025
$45.8M
TREATMENT OF DEPRESSION IN ALZHEIMER'S DISEASEP50AG005133 · NIA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI SWEET, ROBERT A · 1985 to 2019
$43.0M
Washington University Institute of Clinical and Translational SciencesUL1TR000448 · NCATS · WASHINGTON UNIVERSITY · PI EVANOFF, BRADLEY A · 2012 to 2016
$41.4M
NCATS NIH HHS UL1 TR000448NCRR NIH HHS U24 RR021382NIA NIH HHS P01 AG003991NIA NIH HHS P01 AG026276NIA NIH HHS P01 AG084497NIA NIH HHS P20 AG068053NIA NIH HHS P20 AG068077NIA NIH HHS P20 AG068082NIA NIH HHS P30 AG008017NIA NIH HHS P30 AG010129NIA NIH HHS P30 AG010133NIA NIH HHS P30 AG010161NIA NIH HHS P30 AG013846NIA NIH HHS P30 AG013854NIA NIH HHS P30 AG019610NIA NIH HHS P30 AG035982NIA NIH HHS P30 AG053760NIA NIH HHS P30 AG062422NIA NIH HHS P30 AG062428NIA NIH HHS P30 AG062429NIA NIH HHS P30 AG062677NIA NIH HHS P30 AG066444NIA NIH HHS P30 AG066506NIA NIH HHS P30 AG066507NIA NIH HHS P30 AG066511NIA NIH HHS P30 AG066512NIA NIH HHS P30 AG066514NIA NIH HHS P30 AG066515NIA NIH HHS P30 AG072931NIA NIH HHS P30 AG072947NIA NIH HHS P30 AG072958NIA NIH HHS P30 AG072972NIA NIH HHS P30 AG072976NIA NIH HHS P30 AG072980NIA NIH HHS P50 AG005133NIA NIH HHS P50 AG005136NIA NIH HHS P50 AG005142NIA NIH HHS P50 AG008702NIA NIH HHS P50 AG016573NIA NIH HHS P50 AG033514NIA NIH HHS P50 AG047266NIA NIH HHS P50 AG047270NIA NIH HHS P50 AG047366NIA NIH HHS R01 AG019771NIA NIH HHS R01 AG021910NIA NIH HHS R01 AG043434NIA NIH HHS R01 AG045571NIA NIH HHS R01 AG052560NIA NIH HHS R01 AG053509NIA NIH HHS R01 AG053993NIA NIH HHS R01 AG054110NIA NIH HHS R01 AG055005NIA NIH HHS R01 AG056031NIA NIH HHS R01 AG056258NIA NIH HHS R01 AG056531NIA NIH HHS R01 AG058724NIA NIH HHS R01 AG061788NIA NIH HHS R01 AG062276NIA NIH HHS R01 AG067781NIA NIH HHS R01 AG068338NIA NIH HHS R01 AG069453NIA NIH HHS R01 AG073235NIA NIH HHS R01 AG077444NIA NIH HHS R35 AG072262NIA NIH HHS R56 AG045571NIA NIH HHS R56 AG074321NIA NIH HHS U01 AG024904NIA NIH HHS U01 AG057195NIA NIH HHS U19 AG063911NIA NIH HHS U19 AG073153NIA NIH HHS U24 AG067418NIA NIH HHS U24 AG072122NIBIB NIH HHS R01 EB009352NIDCD NIH HHS R01 DC008552NIH HHS S10 OD026738NIMH NIH HHS P50 MH071616NINDS NIH HHS P30 NS098577NINDS NIH HHS R01 NS058487NINDS NIH HHS R01 NS075075NINDS NIH HHS T32 NS082168NINDS NIH HHS U01 NS100620NINDS NIH HHS U01 NS119562NINDS NIH HHS U19 NS120384RRD VA I01 RX001534RRD VA I21 RX001381
6 · The paper itself

Abstract

BACKGROUND AND

objectivesDifferentiation of Alzheimer's disease dementia (ADD) and dementia with Lewy bodies (DLB) remains a challenge. Free-water imaging has been investigated in neurodegenerative diseases and was found to be associated with neurodegeneration and neuroinflammation. This retrospective cohort study tested whether Automated Imaging Differentiation for Dementia (AIDD), combining diffusion free-water imaging (FWI) and support vector machine, predicts ADD vs DLB with high accuracy.

methodsDiffusion MRI data was rendered from ADNI, NACC, and PDBP. Free-water and free-water corrected fractional anisotropy were calculated for each participant using a bi-tensor model. Diffusion metrics were randomly assigned to training and testing sets. The primary outcome was the area under the curve (AUC) in the test set. AIDD was paired with antemortem MRI to predict postmortem pathology.

resultsA total of 519 diffusion scans were processed with 258 ADD (mean age 73.7 (8.8), 50% male), 129 DLB (mean age 69.3, 88% male), and 132 controls (mean age 73.6 (6.8), 40% male). The machine learning sample included 387 scans,129 ADD with a mean age of 72.8 (8.7), 52.7% male; 129 DLB with a mean age of 69.3 (8.1), 87.6% male; and 129 controls with a mean age of 73.7 (6.8), 39.5% male). AIDD showed high training AUC for ADD vs DLB = 0.995 (95% CI, 0.985-1.000), ADD vs controls = 0.992 (95% CI, 0.982-1.000), DLB vs controls = 0.991 (95% CI, 0.983-0.999), and controls vs ADD/DLB = 0.990 (95% CI, 0.979-1.000). The testing AUCs were similar: ADD vs DLB = 0.995, ADD vs controls = 0.958, DLB vs controls = 0.939, controls vs ADD/DLB = 0.903. AIDD predictions were confirmed pathologically in a cohort of 13 patients. DISCUSSION: This study demonstrates that machine learning in combination with free-water imaging can differentiate ADD, DLB, and normal aging with high clinical and pathological accuracy. Advancement in early detection of dementia can lead to more appropriate treatment plans, especially for DLB, and improved disease stratification that have hindered drug development trials. CLASSIFICATION of EVIDENCE: This study provides Class II evidence that Automated Imaging Differentiation for Dementia, combining diffusion free-water imaging and machine learning accurately distinguishes Alzheimer's Disease from Dementia with Lewy bodies.

Indexed as

[ 128 ] DWI[ 26 ] Alzheimer’s disease[ 28 ] Dementia with Lewy bodies[ 322 ] Class II

Identifiers

PMID42179740
PMCPMC13196839

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.