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Preliminary study on the ability of the machine learning models based on F-FDG PET/CT to differentiate between mass-forming pancreatic lymphoma and pancreatic carcinoma.

European journal of radiology
PURPOSE: The objective of this study was to preliminarily assess the ability of metabolic parameters and radiomics derived from F-fluorodeoxyglucose positron emission tomography/computed tomography (F-FDG PET/CT) to distinguish mass-forming pancreati...

Deep-learning segmentation to select liver parenchyma for categorizing hepatic steatosis on multinational chest CT.

Scientific reports
Unenhanced CT scans exhibit high specificity in detecting moderate-to-severe hepatic steatosis. Even though many CTs are scanned from health screening and various diagnostic contexts, their potential for hepatic steatosis detection has largely remain...

Intelligent prediction of Alzheimer's disease via improved multifeature squeeze-and-excitation-dilated residual network.

Scientific reports
This study aimed to address the issue of larger prediction errors existing in intelligent predictive tasks related to Alzheimer's disease (AD). A cohort of 487 enrolled participants was categorized into three groups: normal control (138 individuals),...

Intelligent wearable olfactory interface for latency-free mixed reality and fast olfactory enhancement.

Nature communications
Olfaction feedback systems could be utilized to stimulate human emotion, increase alertness, provide clinical therapy, and establish immersive virtual environments. Currently, the reported olfaction feedback technologies still face a host of formidab...

Automated glioblastoma patient classification using hypoxia levels measured through magnetic resonance images.

BMC neuroscience
INTRODUCTION: The challenge of treating Glioblastoma (GBM) tumors is due to various mechanisms that make the tumor resistant to radiation therapy. One of these mechanisms is hypoxia, and therefore, determining the level of hypoxia can improve treatme...

Neuroimaging Insights: Structural Changes and Classification in Ménière's Disease.

Ear and hearing
OBJECTIVES: This study aimed to comprehensively investigate the neuroanatomical alterations associated with idiopathic Ménière's disease (MD) using voxel-based morphometry and surface-based morphometry techniques. The primary objective was to explore...

Revolutionizing Schedules: The Power of AI in Physician Practices.

Frontiers of health services management
Since the early 2000s, artificial intelligence (AI) has raised concerns regarding its use in healthcare to manage vast amounts of patient data, ensure proper handling, and maintain robust security measures. Nevertheless, contemporary healthcare organ...

Personalized Composite Dosimetric Score-Based Machine Learning Model of Severe Radiation-Induced Lymphopenia Among Patients With Esophageal Cancer.

International journal of radiation oncology, biology, physics
PURPOSE: Radiation-induced lymphopenia (RIL) is common among patients undergoing radiation therapy (RT)' Severe RIL has been linked to adverse outcomes. The severity and risk of RIL can be predicted from baseline clinical characteristics and dosimetr...

Enhancing Precision in Cardiac Segmentation for Magnetic Resonance-Guided Radiation Therapy Through Deep Learning.

International journal of radiation oncology, biology, physics
PURPOSE: Cardiac substructure dose metrics are more strongly linked to late cardiac morbidities than to whole-heart metrics. Magnetic resonance (MR)-guided radiation therapy (MRgRT) enables substructure visualization during daily localization, allowi...

Prediction of 30-Day Mortality Following Revision Total Hip and Knee Arthroplasty: Machine Learning Algorithms Outperform CARDE-B, 5-Item, and 6-Item Modified Frailty Index Risk Scores.

The Journal of arthroplasty
BACKGROUND: Although risk calculators are used to prognosticate postoperative outcomes following revision total hip and knee arthroplasty (total joint arthroplasty [TJA]), machine learning (ML) based predictive tools have emerged as a promising alter...