CT-Based Body Composition Measures and Systemic Disease: A Population-Level Analysis Using Artificial Intelligence Tools in Over 100,000 Patients.

CT-based abdominal body composition measures have shown associations with important health outcomes. Artificial intelligence (AI) advances now allow deployment of tools that measure body composition in large patient populations. To assess associations of age, sex, and common systemic diseases on CT-based body composition measurements derived using a panel of fully automated AI tools in a population-level adult patient sample.

Leveraging Virtual Containers for High-Powered, Collaborative AI Research in Radiology.

Numerous obstacles confront radiologists interested in the use of artificial intelligence (AI) models within the field of radiology. For example, discrepancies between the radiologist's and an AI developer's hardware and software specifications pose a substantial hindrance to using AI models. Additionally, accessing and using GPU computers can lead to compatibility issues and add to these challenges.

BAE-ViT: An Efficient Multimodal Vision Transformer for Bone Age Estimation.

This research introduces BAE-ViT, a specialized vision transformer model developed for bone age estimation (BAE). This model is designed to efficiently merge image and sex data, a capability not present in traditional convolutional neural networks (CNNs). BAE-ViT employs a novel data fusion method to facilitate detailed interactions between visual and non-visual data by tokenizing non-visual information and concatenating all tokens (visual or non-visual) as the input to the model.

Physically activated modes of attentional control.

As we navigate through the day, our attentional control processes are constantly challenged by changing sensory information, goals, expectations, and motivations. At the same time, our bodies and brains are impacted by changes in global physiological state that can influence attentional processes. Based on converging lines of evidence from brain recordings in physically active humans and nonhumans, we propose a new framework incorporating at least two physically activated modes of attentional control in humans: altered gain control and differential neuromodulation of control networks.

Comparison of sequential multi-detector CT and cone-beam CT perfusion maps in 39 subjects with anterior circulation acute ischemic stroke due to a large vessel occlusion.

Purpose: The critical time between stroke onset and treatment was targeted for reduction by integrating physiological imaging into the angiography suite, potentially improving clinical outcomes. The evaluation was conducted to compare C-Arm cone beam CT perfusion (CBCTP) with multi-detector CT perfusion (MDCTP) in patients with acute ischemic stroke (AIS).

Approach: Thirty-nine patients with anterior circulation AIS underwent both MDCTP and CBCTP.