Machine learning in neuroradiology: Recent developments and applications

Table 1 Summary of the most recent advances in machine learning research on stroke imaging, large vessel obstruction, cerebral aneurysms, intracerebral hemorrhage, neuro-oncology, spine imaging, and demyelinating and neurodegenerative diseases

Ref.	Title	Field of interest
Tan et al[7], 2025	A machine learning model reveals invisible microscopic variation in acute ischemic stroke (≤ 6 hours) with non-contrast computed tomography	Acute ischemic stroke
Chen et al[9], 2025	Automated estimation of ischemic core volume on noncontrast-enhanced CT via machine learning	Acute ischemic stroke
van Poppel et al[12], 2026	Machine learning models for CT-based classification of ischemic stroke onset time within or beyond 4.5 hours: A comparison of approaches	Acute ischemic stroke
Ekingen et al[13], 2025	StrokeNeXt: An automated stroke classification model using computed tomography and magnetic resonance images	Acute ischemic stroke
Ayobi et al[14], 2025	Deep learning-based ASPECTS algorithm enhances reader performance and reduces interpretation time	Acute ischemic stroke
Peng et al[15], 2025	Using deep learning to shorten the acquisition time of brain MRI in acute ischemic stroke: Synthetic T2W images generated from b0 images	Acute ischemic stroke
Xia et al[16], 2025	Machine learning prediction model for functional prognosis of acute ischemic stroke based on MRI radiomics of white matter hyperintensities	Acute ischemic stroke
Jiang et al[17], 2025	A deep learning system for detecting silent brain infarction and predicting stroke risk	Acute ischemic stroke
Liu et al[18], 2023	Prediction of recurrence of ischemic stroke within 1 year of discharge based on machine learning MRI radiomics	Acute ischemic stroke
de la Rosa et al[98], 2025	DeepISLES: A clinically validated ischemic stroke segmentation model from the ISLES’22 challenge	Acute ischemic stroke
Kim et al[22], 2024	Automated prediction of proximal middle cerebral artery occlusions in noncontrast brain computed tomography	Large vessel obstruction
Rai et al[23], 2025	Artificial intelligence-driven detection of large vessel occlusions on NCCT: A multi-institutional study	Large vessel obstruction
Kim et al[24], 2025	Automated detection of large vessel occlusion using deep learning: A pivotal multicenter study and reader performance study	Large vessel obstruction
Steinmetz et al[25], 2024	Impact of deep learning-enhanced contrast on diagnostic accuracy in stroke CT angiography	Acute ischemic stroke/CT angiography
Kim et al[26], 2023	A deep learning-based automatic collateral assessment in patients with acute ischemic stroke	Acute ischemic stroke/collateral network
Jabal et al[29], 2025	Automated CT angiography collateral scoring in anterior large vessel occlusion stroke: A multireader study	Large vessel obstruction/collateral network
Jeon et al[30], 2025	Clinical feasibility of deep learning-driven magnetic resonance angiography collateral map in acute anterior circulation ischemic stroke	Acute ischemic stroke/collateral network
Agarwal et al[19], 2025	Development and internal validation of multimodal machine learning models for predicting eligibility for mechanical thrombectomy in suspected stroke patients using routinely collected clinical and imaging data	Acute ischemic stroke/mechanical thrombectomy
von Braun et al[31], 2025	Prediction of tissue and clinical thrombectomy outcome in acute ischemic stroke using deep learning	Acute ischemic stroke/thrombectomy response prediction
Zhang et al[32], 2024	A deep learning approach to predict recanalization first-pass effect following mechanical thrombectomy in patients with acute ischemic stroke	Acute ischemic stroke/mechanical thrombectomy
González et al[33], 2025	Multimodal deep learning for predicting unsuccessful recanalization in refractory large vessel occlusion	Acute ischemic stroke/mechanical thrombectomy
Li et al[34], 2025	Development of a diagnostic prediction model for post-stroke cognitive impairment in acute large vessel occlusion stroke using multimodal MRI and PET/CT: A study protocol	Acute ischemic stroke/mechanical thrombectomy
Jeevarajan et al[35], 2025	Can CTA-based machine learning identify patients for whom successful endovascular stroke therapy is insufficient?	Acute ischemic stroke/CT angiography
Lehnen et al[37], 2024	Impact of an AI software on the diagnostic performance and reading time for the detection of cerebral aneurysms on time of flight MR-angiography	Cerebral aneurysms
López-Rueda et al[39], 2024	Enhancing mortality prediction in patients with spontaneous intracerebral hemorrhage: Radiomics and supervised machine learning on non-contrast computed tomography	Intracerebral hemorrhage
Liang et al[40], 2025	Prediction of etiology and prognosis based on hematoma location of spontaneous intracerebral hemorrhage: A multicenter diagnostic study	Intracerebral hemorrhage
Ning et al[41], 2025	Noncontrast CT-based deep learning for predicting intracerebral hemorrhage expansion incorporating growth of intraventricular hemorrhage	Intracerebral hemorrhage
Zeng et al[42], 2025	Clinical, radiological, and radiomics feature-based explainable machine learning models for prediction of neurological deterioration and 90-day outcomes in mild intracerebral hemorrhage	Intracerebral hemorrhage
Wang et al[43], 2024	Predicting postoperative rehemorrhage in hypertensive intracerebral hemorrhage using noncontrast CT radiomics and clinical data with an interpretable machine learning approach	Intracerebral hemorrhage
Elsheikh et al[45], 2024	Machine learning-based pipeline for automated intracerebral hemorrhage and drain detection, quantification, and classification in non-enhanced CT images	Intracerebral hemorrhage
Akbari et al[48], 2025	Machine learning-based prognostic subgrouping of glioblastoma: A multicenter study	Neuro-oncology
Leone et al[51], 2025	Virtual biopsy for the prediction of MGMT promoter methylation in gliomas: A comprehensive review of radiomics and deep learning approaches applied to MRI	Neuro-oncology
Su et al[54], 2026	Machine learning-based preoperative predicting TERT promoter mutation and EGFR gene amplification phenotype in IDH wild-type glioblastoma using advanced MR habitat imaging	Neuro-oncology
Topff et al[55], 2025	A data-centric approach to deep learning for brain metastasis analysis at MRI	Neuro-oncology
Holtkamp et al[56], 2025	AI-guided virtual biopsy: Automated differentiation of cerebral gliomas from other benign and malignant MRI findings using deep learning	Neuro-oncology
Chen et al[57], 2025	A multi-modal deep learning model for prediction of Ki-67 for meningiomas using pretreatment MR images	Neuro-oncology
Cheng et al[59], 2026	Attention-based deep learning network for predicting World Health Organization meningioma grade and Ki-67 expression based on magnetic resonance imaging	Neuro-oncology
von Reppert et al[60], 2025	Image-based search in radiology: Identification of brain tumor subtypes within databases using MRI-based radiomic features	Neuro-oncology
Nakaura et al[61], 2026	Intra-axial primary brain tumor differentiation: Comparing large language models on structured MRI reports vs radiologists on images	Neuro-oncology
Song et al[63], 2024	Clinical efficacy of motion-insensitive imaging technique with deep learning reconstruction to improve image quality in cervical spine MR imaging	Spine imaging
Yasaka et al[64], 2024	Super-resolution deep learning reconstruction cervical spine 15T MRI: Improved interobserver agreement in evaluations of neuroforaminal stenosis compared to conventional deep learning reconstruction	Spine imaging
Ramos et al[65], 2024	Fast and accurate 3D spine MRI segmentation using FastCleverSeg	Spine imaging
Bogdanovic et al[66], 2024	AI-based measurement of lumbar spinal stenosis on MRI: External evaluation of a fully automated model	Spine imaging
Nikpasand et al[67], 2024	Automated magnetic resonance imaging-based grading of the lumbar intervertebral disc and facet joints	Spine imaging
van der Graaf et al[68], 2025	AI-based lumbar central canal stenosis classification on sagittal MR images is comparable to experienced radiologists using axial images	Spine imaging
Zhang et al[69], 2024	Deep learning model for the automated detection and classification of central canal and neural foraminal stenosis upon cervical spine magnetic resonance imaging	Spine imaging
Lee et al[70], 2025	Deep learning model for automated diagnosis of degenerative cervical spondylosis and altered spinal cord signal on MRI	Spine imaging
van den Wittenboer et al[71], 2024	Diagnostic accuracy of an artificial intelligence algorithm vs radiologists for fracture detection on cervical spine CT	Spine imaging
Maki et al[72], 2024	Multimodal deep learning-based radiomics approach for predicting surgical outcomes in patients with cervical ossification of the posterior longitudinal ligament	Spine imaging
Schlaeger et al[73], 2023	AI-based detection of contrast-enhancing MRI lesions in patients with multiple sclerosis	Demyelinating diseases
Greselin et al[74], 2024	Contrast-enhancing lesion segmentation in multiple sclerosis: A deep learning approach validated in a multicentric cohort	Demyelinating diseases
Wiltgen et al[75], 2024	A deep learning ensemble for accurate MS lesion segmentation	Demyelinating diseases
Bouman et al[76], 2023	Multicenter evaluation of AI-generated DIR and PSIR for cortical and juxtacortical multiple sclerosis lesion detection	Demyelinating diseases
Rostami et al[77], 2024	Enhancing classification of active and non-active lesions in multiple sclerosis: Machine learning models and feature selection techniques	Demyelinating diseases
Peters et al[78], 2025	AI-based assessment of longitudinal multiple sclerosis MRI: Strengths and weaknesses in clinical practice	Demyelinating diseases
Huang et al[80], 2024	A joint model for lesion segmentation and classification of MS and NMOSD	Demyelinating diseases
Ding et al[81], 2024	Classification of myelin oligodendrocyte glycoprotein antibody-related disease and its mimicking acute demyelinating syndromes in children using MRI-based radiomics: From lesion to subject	Demyelinating diseases
Zhou et al[82], 2024	MRI based demyelinative diseases classification with U-Net segmentation and convolutional network	Demyelinating diseases
Rudolph et al[83], 2024	Artificial intelligence-based rapid brain volumetry substantially improves differential diagnosis in dementia	Neurodegeneration
Pérez-Millan et al[84], 2024	Probabilistic differential diagnosis of frontotemporal dementia and Alzheimer's disease with MRI and CSF biomarkers	Neurodegeneration
Rogeau et al[85], 2024	A 3D convolutional neural network to classify subjects as Alzheimer’s disease, frontotemporal dementia or healthy controls using brain 18F-FDG PET	Neurodegeneration
Sadeghi et al[86], 2024	Detecting Alzheimer’s disease stages and frontotemporal dementia in time courses of resting-state fMRI data using a machine learning approach	Neurodegeneration
Chen et al[87], 2024	An automated hybrid approach via deep learning and radiomics focused on the midbrain and substantia nigra to detect early-stage Parkinson’s disease	Neurodegeneration
Wang et al[88], 2024	Automatic substantia nigra segmentation with Swin-Unet in susceptibility- and T2W imaging: Application to Parkinson disease diagnosis	Neurodegeneration
Vaillancourt et al[89], 2025	Automated imaging differentiation for Parkinsonism	Neurodegeneration
Suh et al[90], 2025	Deep learning-based algorithm for automatic quantification of Nigrosome-1 and parkinsonism classification using susceptibility map-weighted MRI	Neurodegeneration
Mastoi et al[99], 2025	Explainable AI in medical imaging: An interpretable and collaborative federated learning model for brain tumor classification	Explainable AI

ASPECTS: Alberta stroke programme early computed tomography score; MRI: Magnetic resonance imaging; T2W: T2-weighted; NCCT: Non-contrast computed tomography; PET: Positron emission tomography; CTA: Computed Tomography angiography; AI: Artificial intelligence; MR: Magnetic resonance; MGMT: Methylated-DNA-protein-cysteine methyltransferase; TERT: Telomerase reverse transcriptase; IDH: Isocitrate dehydrogenase; MS: Multiple sclerosis; DIR: Double inversion recovery; PSIR: Phase sensitive inversion recovery; NMOSD: Neuromyelitis optica spectrum disorder; CSF: Cerebrospinal fluid; 18F-FDG PET: 18F-fluoro-deoxy-glucose positron emission tomography; fMRI: Functional magnetic resonance imaging.