ArticleNeuroradiology2022
Comparing two artificial intelligence software packages for normative brain volumetry in memory clinic imaging.
Article in Neuroradiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- Commercial volumetric MRI reporting tools in multiple sclerosis: a systematic review of the evidence.Neuroradiology · 2023Pooled it
- Easy-to-use and easy-to-interpret quality control of 3D gradient echo T1-weighted MR acquisition sequences for improved test-retest stability of MRI-based hippocampus volumetry.Journal of Alzheimer's disease : JAD · 2025Article
- Quantitative MRI in Neuroimaging: A Review of Techniques, Biomarkers, and Emerging Clinical Applications.Brain sciences · 2025Review
- Scan-rescan reliability assessment of brain volumetric analysis across scanners and software solutions.Scientific reports · 2025Article
- Longitudinal automated brain volumetry versus expert visual assessment of atrophy progression on MRI: an exploratory study.Scientific reports · 2025Article
- AI-based staging, causal hypothesis and progression of subjects at risk of Alzheimer's disease: a multicenter study.Frontiers in neurology · 2025Article
- Cerebellar Structural and N-Acetylaspartate, Choline, and Creatine Metabolic Profiles in Parkinson's Disease and Essential Tremor.Diagnostics (Basel, Switzerland) · 2024Article
- Article
- Contrast-enhanced to non-contrast-enhanced image translation to exploit a clinical data warehouse of T1-weighted brain MRI.BMC medical imaging · 2024Article
- Automated hippocampal segmentation algorithms evaluated in stroke patients.Scientific reports · 2023Article
- Response to the 'Letter to the editor'-10.1007/s00234-022-02906-z.Neuroradiology · 2022Article
- Deep learning-based cortical thickness maps for diagnosis of neurodegenerative diseases: a rater study.Alzheimer's & dementia (Amsterdam, Netherlands)Article
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo compare two artificial intelligence software packages performing normative brain volumetry and explore whether they could differently impact dementia diagnostics in a clinical context.
methodsSixty patients (20 Alzheimer's disease, 20 frontotemporal dementia, 20 mild cognitive impairment) and 20 controls were included retrospectively. One MRI per subject was processed by software packages from two proprietary manufacturers, producing two quantitative reports per subject. Two neuroradiologists assigned forced-choice diagnoses using only the normative volumetry data in these reports. They classified the volumetric profile as "normal," or "abnormal", and if "abnormal," they specified the most likely dementia subtype. Differences between the packages' clinical impact were assessed by comparing (1) agreement between diagnoses based on software output; (2) diagnostic accuracy, sensitivity, and specificity; and (3) diagnostic confidence. Quantitative outputs were also compared to provide context to any diagnostic differences.
resultsDiagnostic agreement between packages was moderate, for distinguishing normal and abnormal volumetry (K = .41-.43) and for specific diagnoses (K = .36-.38). However, each package yielded high inter-observer agreement when distinguishing normal and abnormal profiles (K = .73-.82). Accuracy, sensitivity, and specificity were not different between packages. Diagnostic confidence was different between packages for one rater. Whole brain intracranial volume output differed between software packages (10.73%, p < .001), and normative regional data interpreted for diagnosis correlated weakly to moderately (r
conclusionDifferent artificial intelligence software packages for quantitative normative assessment of brain MRI can produce distinct effects at the level of clinical interpretation. Clinics should not assume that different packages are interchangeable, thus recommending internal evaluation of packages before adoption.
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