AIMC Topic: Magnetic Resonance Imaging

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Decoding pulsatile patterns of cerebrospinal fluid dynamics through enhancing interpretability in machine learning.

Scientific reports
Analyses of complex behaviors of Cerebrospinal Fluid (CSF) have become increasingly important in diseases diagnosis. The changes of the phase-contrast magnetic resonance imaging (PC-MRI) signal formed by the velocity of flowing CSF are represented as...

MRI-based deep learning and radiomics for prediction of occult cervical lymph node metastasis and prognosis in early-stage oral and oropharyngeal squamous cell carcinoma: a diagnostic study.

International journal of surgery (London, England)
INTRODUCTION: The incidence of occult cervical lymph node metastases (OCLNM) is reported to be 20-30% in early-stage oral cancer and oropharyngeal cancer. There is a lack of an accurate diagnostic method to predict occult lymph node metastasis and to...

A novel assessment of whole-mount Gleason grading in prostate cancer to identify candidates for radical prostatectomy: a machine learning-based multiomics study.

Theranostics
: This study aims to assess whole-mount Gleason grading (GG) in prostate cancer (PCa) accurately using a multiomics machine learning (ML) model and to compare its performance with biopsy-proven GG (bxGG) assessment. : A total of 146 patients with PCa...

Brain MRI detection and classification: Harnessing convolutional neural networks and multi-level thresholding.

PloS one
Brain tumor detection in clinical applications is a complex and challenging task due to the intricate structures of the human brain. Magnetic Resonance (MR) imaging is widely preferred for this purpose because of its ability to provide detailed image...

Prediction of bone invasion of oral squamous cell carcinoma using a magnetic resonance imaging-based machine learning model.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
OBJECTIVES: Radiomics, a recently developed image-processing technology, holds potential in medical diagnostics. This study aimed to propose a machine-learning (ML) model and evaluate its effectiveness in detecting oral squamous cell carcinoma (OSCC)...

The potential of an artificial intelligence for diagnosing MRI images in rectal cancer: multicenter collaborative trial.

Journal of gastroenterology
BACKGROUND: An artificial intelligence-based algorithm we developed, mrAI, satisfactorily segmented the rectal tumor, rectum, and mesorectum from MRI data of rectal cancer patients in an initial study. Herein, we aimed to validate mrAI using an indep...

Evolution of white matter hyperintensity segmentation methods and implementation over the past two decades; an incomplete shift towards deep learning.

Brain imaging and behavior
This systematic review examines the prevalence, underlying mechanisms, cohort characteristics, evaluation criteria, and cohort types in white matter hyperintensity (WMH) pipeline and implementation literature spanning the last two decades. Following ...

Machine learning uncovers manganese as a key nutrient associated with reduced risk of steatotic liver disease.

Liver international : official journal of the International Association for the Study of the Liver
BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) affects approximately 20%-30% of the general population and is linked to high-caloric western style diet. However, there are little data that specific nutrients might help t...

AutoCorNN: An Unsupervised Physics-Aware Deep Learning Model for Geometric Distortion Correction of Brain MRI Images Towards MR-Only Stereotactic Radiosurgery.

Journal of imaging informatics in medicine
Geometric distortions in brain MRI images arising from susceptibility artifacts at air-tissue interfaces pose a significant challenge for high-precision radiation therapy modalities like stereotactic radiosurgery, necessitating sub-millimeter accurac...

Artificial T1-Weighted Postcontrast Brain MRI: A Deep Learning Method for Contrast Signal Extraction.

Investigative radiology
OBJECTIVES: Reducing gadolinium-based contrast agents to lower costs, the environmental impact of gadolinium-containing wastewater, and patient exposure is still an unresolved issue. Published methods have never been compared. The purpose of this stu...