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Can ChatGPT4-vision identify radiologic progression of multiple sclerosis on brain MRI?
Eur Radiol Exp
January 2025
St Vincent's University Hospital, Dublin, Ireland.
Background: The large language model ChatGPT can now accept image input with the GPT4-vision (GPT4V) version. We aimed to compare the performance of GPT4V to pretrained U-Net and vision transformer (ViT) models for the identification of the progression of multiple sclerosis (MS) on magnetic resonance imaging (MRI).
Methods: Paired coregistered MR images with and without progression were provided as input to ChatGPT4V in a zero-shot experiment to identify radiologic progression.
Explaining cognitive function in multiple sclerosis through networks of grey and white matter features: a joint independent component analysis.
J Neurol
January 2025
NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, Faculty of Brain Sciences, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, WC1N 3BG, UK.
Cognitive impairment (CI) in multiple sclerosis (MS) is only partially explained by whole-brain volume measures, but independent component analysis (ICA) can extract regional patterns of damage in grey matter (GM) or white matter (WM) that have proven more closely associated with CI. Pathology in GM and WM occurs in parallel, and so patterns can span both. This study assessed whether joint-ICA of GM and WM features better explained cognitive function compared to single-tissue ICA.
View Article and Find Full Text PDFBalance recovery and its link to vestibular agnosia in traumatic brain injury: a longitudinal behavioural and neuro-imaging study.
J Neurol
January 2025
Centre for Vestibular Neurology (CVeN), Department of Brain Sciences, Charing Cross Hospital, Imperial College London, London, W6 8RF, UK.
Background: Vestibular dysfunction causing imbalance affects c. 80% of acute hospitalized traumatic brain injury (TBI) cases. Poor balance recovery is linked to worse return-to-work rates and reduced longevity.
View Article and Find Full Text PDFEvaluating the feasibility of AI-predicted bpMRI image features for predicting prostate cancer aggressiveness: a multi-center study.
Insights Imaging
January 2025
Department of Radiology, the Second Affiliated Hospital of Dalian Medical University, Dalian, 116023, China.
Objective: To evaluate the feasibility of utilizing artificial intelligence (AI)-predicted biparametric MRI (bpMRI) image features for predicting the aggressiveness of prostate cancer (PCa).
Materials And Methods: A total of 878 PCa patients from 4 hospitals were retrospectively collected, all of whom had pathological results after radical prostatectomy (RP). A pre-trained AI algorithm was used to select suspected PCa lesions and extract lesion features for model development.
Deep learning of noncontrast CT for fast prediction of hemorrhagic transformation of acute ischemic stroke: a multicenter study.
Eur Radiol Exp
January 2025
Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Background: Hemorrhagic transformation (HT) is a complication of reperfusion therapy following acute ischemic stroke (AIS). We aimed to develop and validate a model for predicting HT and its subtypes with poor prognosis-parenchymal hemorrhage (PH), including PH-1 (hematoma within infarcted tissue, occupying < 30%) and PH-2 (hematoma occupying ≥ 30% of the infarcted tissue)-in AIS patients following intravenous thrombolysis (IVT) based on noncontrast computed tomography (NCCT) and clinical data.
Methods: In this six-center retrospective study, clinical and imaging data from 445 consecutive IVT-treated AIS patients were collected (01/2018-06/2023).
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