Evidence map›Paper›PMID 41675581›Full record

ArticleFrontiers in radiology2026

MRI approach to the patient with suspected dementia: artificial intelligence techniques and semi-quantitative rating scales compared.

S F Calloni, A Diena, G M Agazzi, M Zavarella, P Q Vezzulli, G Cecchetti, E G Spinelli, G Rugarli, A Ghirelli, G Magnani and 5 more

Abstract read
In one paragraph

Article in Frontiers in radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

15 authors.

S F Calloni *Neuroradiology Unit and Cermac, IRCCS Ospedale San Raffaele, Milan, Italy.
A Diena *Neuroradiology Unit and Cermac, IRCCS Ospedale San Raffaele, Milan, Italy.
G M AgazziNeuroradiology Unit and Cermac, IRCCS Ospedale San Raffaele, Milan, Italy.
M ZavarellaCenter for Alzheimer's Disease and Related Disorders (CARD), Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
P Q VezzulliNeuroradiology Unit and Cermac, IRCCS Ospedale San Raffaele, Milan, Italy.
G CecchettiCenter for Alzheimer's Disease and Related Disorders (CARD), Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
E G SpinelliNeurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy.
G RugarliCenter for Alzheimer's Disease and Related Disorders (CARD), Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
A GhirelliCenter for Alzheimer's Disease and Related Disorders (CARD), Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
G MagnaniNeurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
F CasoNeurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
A van LoonDeepHealth, Boston, MA, United States.
F AgostaVita-Salute San Raffaele University, Milan, Italy.
M FilippiCenter for Alzheimer's Disease and Related Disorders (CARD), Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
A FaliniNeuroradiology Unit and Cermac, IRCCS Ospedale San Raffaele, Milan, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: To assess the reliability of semi-quantitative and AI-based quantitative brain volume evaluation (Quantib® ND) in predicting clinical diagnosis in patients with suspected neurodegenerative diseases undergoing initial 1.5 T MRI. Additionally, to analyze the frequency of lobar microbleeds (MBs) at diagnosis. Methods: Two neuroradiologists (2 vs. 10 years' experience), blinded to diagnosis, independently evaluated brain atrophy on 3D-T1 images of 133 subjects using Scheltens, Koedam, and Kipps scales. Automated volumetric analysis was performed using Quantib® ND. SWI images were assessed by one neuroradiologist to classify MBs as cortical, juxtacortical, subcortical, or deep. Inter-observer agreement was measured using intraclass correlation coefficients (ICC); correlation with Quantib® ND was analyzed using Spearman's coefficient. Cohen's Kappa assessed agreement with clinical diagnosis. Results: Good inter-observer agreement was observed for the MTA scale (ICC 0.86 right, 0.82 left) and Kipps scale (ICC 0.76), with moderate concordance for Koedam (ICC 0.66). Frontal and posterior temporal Kipps subregions had good concordance (ICC 0.77, 0.79), while anterior temporal showed poor agreement (ICC 0.59). Diagnostic accuracy was moderate across observers and Quantib® ND. Observer 1 showed 77% sensitivity, 51% specificity; observer 2 had 79% sensitivity, 62% specificity; Quantib® ND reached 56% sensitivity, 74% specificity. Patients exhibited significantly more lobar MBs than non-dementia patients ( Conclusions: Semi-quantitative visual scales proved effective and sensitive for detecting brain atrophy, showing good concordance with automated volumetric data. While AI-based quantification demonstrated higher specificity, visual assessment remained more sensitive. Lobar MBs were more frequent in neurodegenerative cases.

Indexed as

artificial intelligencebrain volumetrydementiamagnetic resonance imagingmicrobleedsQuantib NDvisual rating scales

Identifiers

PMID41675581
PMCPMC12887851

What Socratic holds

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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 Socratic graph.