Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook.

In the complex and multidimensional field of medicine, multimodal data are prevalent and crucial for informed clinical decisions. Multimodal data span a broad spectrum of data types, including medical images (eg, MRI and CT scans), time-series data (eg, sensor data from wearable devices and electronic health records), audio recordings (eg, heart and respiratory sounds and patient interviews), text (eg, clinical notes and research articles), videos (eg, surgical procedures), and omics data (eg, genomics and proteomics). While advancements in large language models (LLMs) have enabled new applications for knowledge retrieval and processing in the medical field, most LLMs remain limited to processing unimodal data, typically text-based content, and often overlook the importance of integrating the diverse data modalities encountered in clinical practice.

Evaluating segment anything model (SAM) on MRI scans of brain tumors.

Addressing the challenge of automatically segmenting anatomical structures from brain images has been a long-standing problem, attributed to subject- and image-based variations and constraints in available data annotations. The Segment Anything Model (SAM), developed by Meta, is a foundational model trained to provide zero-shot segmentation outputs with or without interactive user inputs, demonstrating notable performance on various objects and image domains without explicit prior training. This study evaluated SAM's performance in brain tumor segmentation using two publicly available Magnetic Resonance Imaging (MRI) datasets.

The Performance of Wearable AI in Detecting Stress Among Students: Systematic Review and Meta-Analysis.

Background: Students usually encounter stress throughout their academic path. Ongoing stressors may lead to chronic stress, adversely affecting their physical and mental well-being. Thus, early detection and monitoring of stress among students are crucial.

Wearable Artificial Intelligence for Detecting Anxiety: Systematic Review and Meta-Analysis.

Background: Anxiety disorders rank among the most prevalent mental disorders worldwide. Anxiety symptoms are typically evaluated using self-assessment surveys or interview-based assessment methods conducted by clinicians, which can be subjective, time-consuming, and challenging to repeat. Therefore, there is an increasing demand for using technologies capable of providing objective and early detection of anxiety.

Large Language Models in Medical Education: Opportunities, Challenges, and Future Directions.

The integration of large language models (LLMs), such as those in the Generative Pre-trained Transformers (GPT) series, into medical education has the potential to transform learning experiences for students and elevate their knowledge, skills, and competence. Drawing on a wealth of professional and academic experience, we propose that LLMs hold promise for revolutionizing medical curriculum development, teaching methodologies, personalized study plans and learning materials, student assessments, and more. However, we also critically examine the challenges that such integration might pose by addressing issues of algorithmic bias, overreliance, plagiarism, misinformation, inequity, privacy, and copyright concerns in medical education.

Targeted capillary photothrombosis via multiphoton excitation of Rose Bengal.

Microvascular stalling, the process occurring when a capillary temporarily loses perfusion, has gained increasing interest in recent years through its demonstrated presence in various neuropathologies. Studying the impact of such stalls on the surrounding brain tissue is of paramount importance to understand their role in such diseases. Despite efforts trying to study the stalling events, investigations are hampered by their elusiveness and scarcity.

Multimodal Autoencoder Predicts fNIRS Resting State From EEG Signals.

In this work, we introduce a deep learning architecture for evaluation on multimodal electroencephalographic (EEG) and functional near-infrared spectroscopy (fNIRS) recordings from 40 epileptic patients. Long short-term memory units and convolutional neural networks are integrated within a multimodal sequence-to-sequence autoencoder. The trained neural network predicts fNIRS signals from EEG, sans a priori, by hierarchically extracting deep features from EEG full spectra and specific EEG frequency bands.

A simulation study investigating potential diffusion-based MRI signatures of microstrokes.

Recent studies suggested that cerebrovascular micro-occlusions, i.e. microstokes, could lead to ischemic tissue infarctions and cognitive deficits.

A Deep Learning Framework for Spatiotemporal Ultrasound Localization Microscopy.

Ultrasound Localization Microscopy (ULM) can resolve the microvascular bed down to a few micrometers. To achieve such performance, microbubble contrast agents must perfuse the entire microvascular network. Microbubbles are then located individually and tracked over time to sample individual vessels, typically over hundreds of thousands of images.

Anatomical Modeling of Brain Vasculature in Two-Photon Microscopy by Generalizable Deep Learning.

. Segmentation of blood vessels from two-photon microscopy (2PM) angiograms of brains has important applications in hemodynamic analysis and disease diagnosis. Here, we develop a generalizable deep learning technique for accurate 2PM vascular segmentation of sizable regions in mouse brains acquired from multiple 2PM setups.

Automated Analysis of Brain Microvasculature: From Segmentation to Anatomical Modeling.

Two-photon microscopy (TPM) can provide a detailed microscopic information of cerebrovascular structures. Extracting anatomical vascular models from TPM angiograms remains a tedious task due to image degeneration associated with TPM acquisitions and the complexity of microvascular networks. Here, we propose a fully automated pipeline capable of providing useful anatomical models of vascular structures captured with TPM.

Laplacian Flow Dynamics on Geometric Graphs for Anatomical Modeling of Cerebrovascular Networks.

Generating computational anatomical models of cerebrovascular networks is vital for improving clinical practice and understanding brain oxygen transport. This is achieved by extracting graph-based representations based on pre-mapping of vascular structures. Recent graphing methods can provide smooth vessels trajectories and well-connected vascular topology.

A Pilot Study Investigating Changes in Capillary Hemodynamics and Its Modulation by Exercise in the APP-PS1 Alzheimer Mouse Model.

Dysfunction in neurovascular coupling that results in a mismatch between cerebral blood flow and neuronal activity has been suggested to play a key role in the pathogenesis of Alzheimer's disease (AD). Meanwhile, physical exercise is a powerful approach for maintaining cognitive health and could play a preventive role against the progression of AD. Given the fundamental role of capillaries in oxygen transport to tissue, our pilot study aimed to characterize changes in capillary hemodynamics with AD and AD supplemented by exercise.

Automatic Graph-Based Modeling of Brain Microvessels Captured With Two-Photon Microscopy.

Graph models of cerebral vasculature derived from two-photon microscopy have shown to be relevant to study brain microphysiology. Automatic graphing of these microvessels remain problematic due to the vascular network complexity and two-photon sensitivity limitations with depth. In this paper, we propose a fully automatic processing pipeline to address this issue.

Fully Convolutional DenseNets for Segmentation of Microvessels in Two-photon Microscopy.

Segmentation of microvessels measured using two-photon microscopy has been studied in the literature with limited success due to uneven intensities associated with optical imaging and shadowing effects. In this work, we address this problem using a customized version of a recently developed fully convolutional neural network, namely, FC-DensNets. To train and validate the network, manual annotations of 8 angiograms from two-photon microscopy was used.

Compromised microvascular oxygen delivery increases brain tissue vulnerability with age.

Despite the possible role of impaired cerebral tissue oxygenation in age-related cognition decline, much is still unknown about the changes in brain tissue pO with age. Using a detailed investigation of the age-related changes in cerebral tissue oxygenation in the barrel cortex of healthy, awake aged mice, we demonstrate decreased arteriolar and tissue pO with age. These changes are exacerbated after middle-age.