Latest AI and machine learning research in pancreatic diseases for healthcare professionals.
Age prediction using brain imaging, such as MRIs, has achieved promising results, with several studies identifying the model's residual as a potential biomarker for chronic disease states. In this study, we developed a brain age predictive model using a dataset of 1,220 U.S. veterans (18--80 years) and convolutional neural networks (CNNs) trained on two-dimensional slices of axial T2-weighted fa...
With over 85 million CT scans performed annually in the United States, creating tumor-related reports is a challenging and time-consuming task for radiologists. To address this need, we present RadGPT, an Anatomy-Aware Vision-Language AI Agent for generating detailed reports from CT scans. RadGPT first segments tumors, including benign cysts and malignant tumors, and their surrounding anatomical...
Building trusted datasets is critical for transparent and responsible Medical AI (MAI) research, but creating even small, high-quality datasets can ...
Pancreatic cancer (PC) patient-derived organoids (PDOs) faithfully recapitulate therapeutic responses but face clinical translation barriers, includin...
In computational modeling, Bounded Linear Temporal Logic (BLTL) is a valuable formalism for describing and verifying the temporal behavior of biologic...
Bionic implants are increasingly used to restore neural function yet achieving a chronically stable neural interface remains challenging. Biohybrid ne...
While extensive efforts have characterized lymphoid populations that contribute to pancreatic ‘insulitis’ in type 1 diabetes, significant gaps remain ...
Examining DNA in a liquid biopsy for non-invasive cancer detection relies on identifying dilute signal in a high background. This study aims to identi...
Type 1 diabetes mellitus (T1DM) is the most common severe chronic disease in children and adolescents and requires life-long exogenous insulin treatme...
Drug discovery is being transformed by artificial intelligence, which enables the exploration of vast chemical spaces and the generation of novel comp...
Machine learning approaches have advanced the identification of neural signatures of substance use, particularly through case-control comparisons and ...
The prefrontal cortex (PFC) is one of the last brain regions to fully mature, making it particularly sensitive to drug use early in life. Both human a...
Spot-based spatial transcriptomics (ST) technologies like 10x Visium quantify genome-wide gene expression and preserve spatial tissue organization. Ho...
Understanding the effects of individual biological factors from single cell-resolved epigenomic data is hindered by multicollinearity, particularly in...
Primary and metastatic brain tumors are among the deadliest and treatment-resistant cancers, mainly because of their inherent resistance to chemoradia...
Traditional machine learning approaches for text or sequence classification rely on converting textual data into numerical representations. In this st...
Tertiary lymphoid structures (TLS) have been observed in solid tumors and have been associated with better outcomes in patients treated with immunothe...
Complex multilineage organoid systems lack quantitative phenotyping methods preserving spatial architecture at high throughput. Current approaches com...
We present an AI-assisted pipeline for disease-specific drug landscape analysis. Given a disease name, the system assembles a comprehensive, evidence-...
Pancreatic ductal adenocarcinoma (PDAC) lacks reliable prognostic biomarkers. RNA-based signatures suffer from poor reproducibility due to batch effec...