{ "id": 3767, "date": "2022-10-21T12:23:18", "date_gmt": "2022-10-21T10:23:18", "guid": { "rendered": "https:\/\/staging.mediaire.ai\/?p=3767" }, "modified": "2025-09-03T09:12:07", "modified_gmt": "2025-09-03T07:12:07", "slug": "artificial-intelligence-substantially-improves-differential-diagnosis-of-dementiaadded-diagnostic-value-of-rapid-brain-volumetry", "status": "publish", "type": "post", "link": "https:\/\/mediaire.ai\/de\/artificial-intelligence-substantially-improves-differential-diagnosis-of-dementiaadded-diagnostic-value-of-rapid-brain-volumetry\/", "title": { "rendered": "Artificial intelligence substantially improves differential diagnosis of dementia\u2013added diagnostic value of rapid brain volumetry" }, "content": { "rendered": "
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

Published in<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

<\/p>\n

Clinical Neuroradiology, 31(Supplement 1), 21-22. (2021)<\/p>\n

<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

Authors<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

<\/p>\n

J.\u00a0<\/span>Rudolph, J. R\u00fcckel, J. D\u00f6pfert, X. Ling, J. Opalka, C. Brem et al.<\/p>\n

<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

Abstract<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

Purpose<\/p>\n

Brain volumetry is a key aspect in dementia diagnostics. We applied an artificial intelligence (AI) system based on a Convolutional Neural Network (CNN) which aims to perform lobe-separated rapid brain volumetry (< 1\/2 h) of three-dimensional T1-weighted magnetic resonance imaging (MRI) with automated segmentation as well as comparison to age- and gender-adapted percentiles. Our aim was to quantify the added value in the differential diagnostics of dementia.<\/p>\n

Materials & Methods<\/p>\n

A total of 55 patients\u201317 with confirmed diagnosis of Alzheimer\u2019s disease (AD), 18 with confirmed diagnosis of frontotemporal dementia (FTD) and 20 healthy controls\u2013received T1-weighted three-dimensional magnetization prepared\u2013rapid gradient echo (MPRAGE) MRI. Images were retrospectively assessed by one board-certified neuroradiologist (BCNR) and two radiology residents (RR)\u2013 one of whom had received 6 months of neuroradiology training (RR1). All cases were evaluated in a two-step reading process\u2013beginning without AI- support and followed by an AI- supported reading (AI tool: mdbrain version 3.3.0). For each subject, the suspected diagnostic category (AD, FTD and healthy controls) was determined using a likelihood score (0\u20135), adding up to a sum of 5 for all three diagnostic categories. Individual reader performance with and without AI support was statistically evaluated using receiver operating characteristics (ROC).<\/p>\n

Results<\/p>\n

AI support substantially improved AD diagnosis in all three readers. The effect was most pronounced for RR2 who had not undergone neuroradiology training (area under the curve [AUC] without AI support [\u2013 AI]: 0.629, AI supported [+ AI]: 0.885). But, even for the BCNR, a substantial benefit was measurable (AUCs: BCNR\u2014 AI: 0.827, + AI: 0.882; RR1\u2014AI: 0.713, + AI: 0.834). In diagnosing FTD RR2 improved with AI support (AUCs:\u2014AI: 0.610, + AI: 0.754), while BCNR and RR1 had comparable reading performances with and without AI support (AUCs: BCNR\u2014 AI: 0.843, + AI: 0.828; RR1\u2014AI: 0.865, + AI: 0.868).<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\t\t\t\t
ROC curves for diagnostic precision of differentiation from healthy to AD patients (left) and FTD patients (right) for radiologists with different levels of training (RR2= 1st year resident, RR1= radiologist; BCNR = neuroradiologist); dashed lines without mdbrain, solid lines with AI support by mdbrain.<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

Discussion<\/p>\n

Even experienced BCNR can improve their diagnostic accuracy for AD by using AI based rapid brain volumetry and comparison with the age- and gender-matched reference cohorts. In diagnosing FTD, especially radiologists who are less experienced in dementia differential diagnosis can strongly benefit from AI support. Conclusion: AI support in the radiological work-up of dementia patients is feasible and can substantially improve diagnostic accuracy, which might lead to earlier diagnosis and therefore optimized patient management.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t
\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tFind out more<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>", "protected": false }, "excerpt": { "rendered": "

This study evaluates the clinical value of a deep learning\u2013based artificial intelligence (AI) system that performs rapid brain volumetry with automatic lobe segmentation and age- and sex-adjusted percentile comparisons.<\/p>", "protected": false }, "author": 1, "featured_media": 0, "comment_status": "open", "ping_status": "open", "sticky": false, "template": "", "format": "standard", "meta": { "_acf_changed": false, "footnotes": "" }, "categories": [ 14 ], "tags": [], "class_list": [ "post-3767", "post", "type-post", "status-publish", "format-standard", "hentry", "category-publications" ], "acf": [], "_links": { "self": [ { "href": "https:\/\/mediaire.ai\/de\/wp-json\/wp\/v2\/posts\/3767", "targetHints": { "allow": [ "GET" ] } } ], "collection": [ { "href": "https:\/\/mediaire.ai\/de\/wp-json\/wp\/v2\/posts" } ], "about": [ { "href": "https:\/\/mediaire.ai\/de\/wp-json\/wp\/v2\/types\/post" } ], "author": [ { "embeddable": true, "href": "https:\/\/mediaire.ai\/de\/wp-json\/wp\/v2\/users\/1" } ], "replies": [ { "embeddable": true, "href": "https:\/\/mediaire.ai\/de\/wp-json\/wp\/v2\/comments?post=3767" } ], "version-history": [ { "count": 5, "href": "https:\/\/mediaire.ai\/de\/wp-json\/wp\/v2\/posts\/3767\/revisions" } ], "predecessor-version": [ { "id": 21641, "href": "https:\/\/mediaire.ai\/de\/wp-json\/wp\/v2\/posts\/3767\/revisions\/21641" } ], "wp:attachment": [ { "href": "https:\/\/mediaire.ai\/de\/wp-json\/wp\/v2\/media?parent=3767" } ], "wp:term": [ { "taxonomy": "category", "embeddable": true, "href": "https:\/\/mediaire.ai\/de\/wp-json\/wp\/v2\/categories?post=3767" }, { "taxonomy": "post_tag", "embeddable": true, "href": "https:\/\/mediaire.ai\/de\/wp-json\/wp\/v2\/tags?post=3767" } ], "curies": [ { "name": "wp", "href": "https:\/\/api.w.org\/{rel}", "templated": true } ] } }