Head Trauma

Ultra-Low-Dose CTPA Using Sparse Sampling CT Combined with the U-Net for Deep Learning-Based Artifact Reduction: An Exploratory Study.

This retrospective study evaluates U-Net-based artifact reduction for dose-reduced sparse-sampling CT (SpSCT) in terms of image quality and diagnostic performance using a reader study and automated detection. CT pulmonary angiograms from 89 patients were used to generate SpSCT data with 16 to 512 views. Twenty patients were reserved for a reader study and test set, the remaining 69 were used to tr...

Artificial Intelligence for Quantifying Endoscopic Mucosal Ulceration in Crohn's Disease.

BACKGROUND & AIMS: Endoscopic scoring of Crohn's disease (CD) is challenging, as mucosal disease is patchy with highly variable morphology, size, and severity. Computer vision may help quantify disease activity with similar performance as standard instruments like the Simple Endoscopic Score for Crohn's Disease (SES-CD). METHODS: Colonoscopy videos from the STARDUST and SEAVUE phase 3 clinical tri...

Detecting Artificial Intelligence-Generated Text in Personal Statements of Adult Reconstruction Fellowship Applicants.

BACKGROUND: Artificial intelligence (AI), particularly large language models such as Chat Generative Pre-Trained Transformer (ChatGPT), has expanded a...

Post-hoc eXplainable AI methods for analyzing medical images of gliomas (- A review for clinical applications).

Deep learning (DL) has shown promise in glioma imaging tasks using magnetic resonance imaging (MRI) and histopathology images, yet their complexity de...

Predicting SARS-CoV-2-specific CD4 and CD8 T-cell responses elicited by inactivated vaccines in healthy adults using machine learning models.

The ongoing evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants highlights the importance of monitoring immune response...

Benchmarking Ensemble Models to Predict Prolonged Hospital Stay in Traumatic Brain Injury: A Single-Institution Study.

INTRODUCTION: Prolonged length of stay (PLOS) in hospitals is a critical metric representing quality and efficiency of care, especially for patients w...

Early transcriptional responses reveal cell type-specific vulnerability and neuroprotective mechanisms in the neonatal ischemic hippocampus.

Neonatal hypoxic-ischemic (H-I) brain injury, a leading cause of neurodevelopmental disabilities, severely affects the metabolically active and neurog...

3BTRON: A Blood-Brain Barrier Recognition Network.

The blood-brain barrier (BBB) plays a crucial role in maintaining brain homeostasis. During ageing, the BBB undergoes structural alterations. Electron...

Characteristics of brain network connectome and connectome-based efficacy predictive model in bipolar depression.

Aberrant functional connectivity (FC) between brain networks has been indicated closely associated with bipolar disorder (BD). However, the previous f...

Optimizing Vital Signs in Patients With Traumatic Brain Injury: Reinforcement Learning Algorithm Development and Validation.

BACKGROUND: Traumatic brain injury (TBI) is a critically ill disease with a high mortality rate, and clinical treatment is committed to continuously o...

Beyond Recanalization: Machine Learning-Based Insights into Post-Thrombectomy Vascular Morphology in Stroke Patients.

Many stroke patients have poor outcomes despite successful endovascular therapy (EVT). We hypothesized that machine learning (ML)-based analysis of va...

Multimodal nomogram integrating deep learning radiomics and hemodynamic parameters for early prediction of post-craniotomy intracranial hypertension.

To evaluate the effectiveness of deep learning radiomics nomogram in distinguishing early intracranial hypertension (IH) following primary decompressi...

Deep learning strategies for semantic segmentation of pediatric brain tumors in multiparametric MRI.

Automated segmentation of pediatric brain tumors (PBTs) can support precise diagnosis and treatment monitoring, but it is still poorly investigated in...

Parsimonious and explainable machine learning for predicting mortality in patients post hip fracture surgery.

Hip fractures among the elderly population continue to present significant risks and high mortality rates despite advancements in surgical procedures....

Personalized prediction model generated with machine learning for kidney function one year after living kidney donation.

Living kidney donors typically experience approximately a 30% reduction in kidney function after donation, although the degree of reduction varies amo...

Auto-Segmentation via deep-learning approaches for the assessment of flap volume after reconstructive surgery or radiotherapy in head and neck cancer.

Reconstructive flap surgery aims to restore the substance and function losses associated with tumor resection. Automatic flap segmentation could allow...

Machine learning based prediction of geotechnical parameters affecting slope stability in open-pit iron ore mines in high precipitation zone.

Rainfall and its interaction with soil, rock, and environmental factors such as soil moisture content, temperature variations, groundwater levels, and...

Automatic recognition and differentiation of pulmonary contusion and bacterial pneumonia based on deep learning and radiomics.

BACKGROUND: In clinical work, there are difficulties in distinguishing pulmonary contusion(PC) from bacterial pneumonia(BP) on CT images by the naked ...

h-calibration: Rethinking Classifier Recalibration with Probabilistic Error-Bounded Objective.

Deep neural networks have demonstrated remarkable performance across numerous learning tasks but often suffer from miscalibration, resulting in unreli...

A model developed by machine learning and principal component analysis for predicting bronchiolitis obliterans syndrome after allogeneic HSCT in a Chinese population with hematologic malignancies.

Bronchiolitis obliterans syndrome (BOS) is a severe pulmonary complication following allogeneic hematopoietic stem cell transplantation (allo-HSCT), w...