AIMC Topic: Adult

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Predicting Epidural Hematoma Expansion in Traumatic Brain Injury: A Machine Learning Approach.

The neuroradiology journal
IntroductionTraumatic brain injury (TBI) is a leading cause of disability and mortality worldwide, with epidural hematoma (EDH) being a severe consequence. This study focuses on identifying factors predicting EDH volume changes in TBI patients and de...

Pulmonary Xe MRI: CNN Registration and Segmentation to Generate Ventilation Defect Percent with Multi-center Validation.

Academic radiology
RATIONALE AND OBJECTIVES: Hyperpolarized Xe MRI quantifies ventilation-defect-percent (VDP), the ratio of Xe signal-void to the anatomic H MRI thoracic-cavity-volume. VDP is associated with airway inflammation and disease control and serves as a trea...

Leveraging AI models for lesion detection in osteonecrosis of the femoral head and T1-weighted MRI generation from radiographs.

Journal of orthopaedic research : official publication of the Orthopaedic Research Society
This study emphasizes the importance of early detection of osteonecrosis of the femoral head (ONFH) in young patients on long-term glucocorticoid therapy, including those with acute lymphoblastic leukemia, lupus, and other diagnoses. While X-ray and ...

Neural correlates of empathy in donation decisions: Insights from EEG and machine learning.

Neuroscience
Empathy is central to individual and societal well-being. Numerous studies have examined how trait of empathy affects prosocial behavior. However, little studies explored the psychological and neural mechanisms by which different dimensions of trait ...

Differential impact of CD34+ cell dose for different age groups in allogeneic hematopoietic cell transplantation for acute leukemia: a machine learning-based discovery.

Experimental hematology
Allogeneic hematopoietic cell transplantation (allo-HCT) presents a potentially curative treatment for hematologic malignancies yet carries associated risks and complications. Continuous research focuses on predicting outcomes and identifying risk fa...

Convolutional neural network classification of ultrasound parametric images based on echo-envelope statistics for the quantitative diagnosis of liver steatosis.

Journal of medical ultrasonics (2001)
PURPOSE: Early detection and quantitative evaluation of liver steatosis are crucial. Therefore, this study investigated a method for classifying ultrasound images to fatty liver grades based on echo-envelope statistics (ES) and convolutional neural n...

Generative modeling of the Circle of Willis using 3D-StyleGAN.

NeuroImage
The circle of Willis (CoW) is a network of cerebral arteries with significant inter-individual anatomical variations. Deep learning has been used to characterize and quantify the status of the CoW in various applications for the diagnosis and treatme...

Exploratory study on the enhancement of O-RADS application effectiveness for novice ultrasonographers via deep learning.

Archives of gynecology and obstetrics
PURPOSE: The study aimed to create a deep convolutional neural network (DCNN) model based on ConvNeXt-Tiny to identify classic benign lesions (CBL) from other lesions (OL) within the Ovarian-Adnexal Reporting and Data System (O-RADS), enhancing the s...

Accelerated Spine MRI with Deep Learning Based Image Reconstruction: A Prospective Comparison with Standard MRI.

Academic radiology
RATIONALE AND OBJECTIVES: To evaluate the performance of deep learning (DL) reconstructed MRI in terms of image acquisition time, overall image quality and diagnostic interchangeability compared to standard-of-care (SOC) MRI.