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DeepFLAIR: A neural network approach to mitigate signal and contrast loss in temporal lobes at 7 Tesla FLAIR images.
Magnetic resonance imaging
Apr 15, 2024
BACKGROUND AND PURPOSE: Higher magnetic field strength introduces stronger magnetic field inhomogeneities in the brain, especially within temporal lobes, leading to image artifacts. Particularly, T2-weighted fluid-attenuated inversion recovery (FLAIR...
vEpiNet: A multimodal interictal epileptiform discharge detection method based on video and electroencephalogram data.
Neural networks : the official journal of the International Neural Network Society
Apr 14, 2024
To enhance deep learning-based automated interictal epileptiform discharge (IED) detection, this study proposes a multimodal method, vEpiNet, that leverages video and electroencephalogram (EEG) data. Datasets comprise 24 931 IED (from 484 patients) a...
Deep learning for the automatic detection and segmentation of parotid gland tumors on MRI.
Oral oncology
Apr 13, 2024
OBJECTIVES: Parotid gland tumors (PGTs) often occur as incidental findings on magnetic resonance images (MRI) that may be overlooked. This study aimed to construct and validate a deep learning model to automatically identify parotid glands (PGs) with...
Weight gained during treatment predicts 6-month body mass index in a large sample of patients with anorexia nervosa using ensemble machine learning.
The International journal of eating disorders
Apr 12, 2024
OBJECTIVE: This study used machine learning methods to analyze data on treatment outcomes from individuals with anorexia nervosa admitted to a specialized eating disorders treatment program.
Histological interpretation of spitzoid tumours: an extensive machine learning-based concordance analysis for improving decision making.
Histopathology
Apr 12, 2024
The histopathological classification of melanocytic tumours with spitzoid features remains a challenging task. We confront the complexities involved in the histological classification of these tumours by proposing machine learning (ML) algorithms tha...
Toward Precision Diagnosis: Machine Learning in Identifying Malignant Orbital Tumors With Multiparametric 3 T MRI.
Investigative radiology
Apr 11, 2024
BACKGROUND: Orbital tumors present a diagnostic challenge due to their varied locations and histopathological differences. Although recent advancements in imaging have improved diagnosis, classification remains a challenge. The integration of artific...
Impact of COVID-19 on arthritis with generative AI.
International immunopharmacology
Apr 11, 2024
OBJECTIVE: The study aims to examine the effects of the COVID-19 pandemic on the prevalence of arthritis in the US using a specific generative AI tool.
Artificial intelligence for detection of effusion and lipo-hemarthrosis in X-rays and CT of the knee.
European journal of radiology
Apr 10, 2024
BACKGROUND: Traumatic knee injuries are challenging to diagnose accurately through radiography and to a lesser extent, through CT, with fractures sometimes overlooked. Ancillary signs like joint effusion or lipo-hemarthrosis are indicative of fractur...
Knee Joint
Exudates and Transudates
Knee Injuries
Hemarthrosis
Male
Middle Aged
Adolescent
Radiographic Image Interpretation, Computer-Assisted
Algorithms
Retrospective Studies
Tomography, X-Ray Computed
Female
Aged
Aged, 80 and over
Young Adult
Adult
Sensitivity and Specificity
Humans
Artificial Intelligence
Intelligent classification of major depressive disorder using rs-fMRI of the posterior cingulate cortex.
Journal of affective disorders
Apr 9, 2024
Major Depressive Disorder (MDD) is a widespread psychiatric condition that affects a significant portion of the global population. The classification and diagnosis of MDD is crucial for effective treatment. Traditional methods, based on clinical asse...
Predicting post-treatment symptom severity for adults receiving psychological therapy in routine care for generalised anxiety disorder: a machine learning approach.
Psychiatry research
Apr 9, 2024
Approximately half of generalised anxiety disorder (GAD) patients do not recover from first-line treatments, and no validated prediction models exist to inform individuals or clinicians of potential treatment benefits. This study aimed to develop and...
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