Discover AI models, datasets, platforms, and computational tools advancing research across the Commonwealth.
Applied research projects across healthcare, agriculture, public health, and education.
LLM-powered robotic health assistant with smell sensing, fall detection, and conversational AI.
AI-powered patient-to-clinical-trial matching using reasoning models for eligibility decisions.
AI-powered synthetic personas for medical and social work education using interactive patient simulations.
Automated texting service for clinical TRE studies. 93.53% comprehension accuracy, 93% daily adherence.
Nutritional guidance platform predicting food health scores from text descriptions and suggesting healthier alternatives.
Oral history transcription and analysis system for thousands of hours of Kentucky recordings.
Pre-trained and fine-tuned models for computer vision, NLP, and medical AI.
DINOv2 ViT-Large finetuned on CT-RATE for chest CT feature extraction with anatomically-aware cropping.
Vision Transformer for neuropathology, pretrained using DinoMX (DINO + iBOT). Linear probe accuracy: 80.17%, KNN accuracy: 83.76%.
Curated datasets for training and evaluation.
Smell/VOC sensor data collected by a mobile robot over ~5 months. 64 smell channels, temperature, humidity. 4.35 GB.
728 pathological image ROIs with 1536-dim Prov-Gigapath features across 3 classes.
Statewide EMS opioid response data collected weekly since January 2018.
19,000+ audio and video recordings spanning Kentucky oral history.
Aggregated pathology slides from Kentucky healthcare sources. Over 500,000 slides.
114 annotated syringe deposit images for computer vision classification and counting.
Peer-reviewed papers and preprints from Kentucky researchers.
Abstract Nontuberculous mycobacteria (NTM) are ubiquitous bacteria that cause a spectrum of diseases, most notably pulmonary disease (NTMPD). The host factors contributing to the heightened susceptibility and severity of NTMPD…
Abstract Alzheimer's disease neuropathological changes (ADNC)-operationalized with semi-quantitative parameters-represent the consensus-based gold standard for diagnostic evaluation of disease severity. Although useful, ADNC diagnostic frameworks have limitations, particularly in advanced disease…
Abstract Coronary artery calcium (CAC) scoring is a key predictor of cardiovascular risk, but it relies on ECG-gated CT scans, restricting its use to specialized cardiac imaging settings. We introduce…
Abstract Automated radiology report generation from 3D computed tomography (CT) volumes is challenging due to extreme sequence lengths, severe class imbalance, and the tendency of large language models (LLMs) to…
Abstract Coronary artery disease (CAD), one of the leading causes of mortality worldwide, necessitates effective risk assessment strategies, with coronary artery calcium (CAC) scoring via computed tomography (CT) being a…
Abstract Histopathological image analysis plays a critical role in modern medical diagnostics, particularly in the detection and classification of various types of cancer. This study proposes a method called HistoDARE…
Federal and institutional grants supporting Kentucky research.
Agency: NIH/NIDAAward Number: 1U54DA058256-01Program: AppalTRuST Project 3
Agency: NSFAward Number: OCI-1246332
Agency: NIH/HHSAward Number: R01 DK124774
Expanding access to AI training resources across Kentucky through the NAIRR pilot program.
Self-service tools for researchers — no programming expertise required.
Secure, web-based AI transcription platform with speaker diarization, timestamping, and LLM-powered analysis.
No-code, web-based machine learning platform for training and evaluating classification models on tabular data.
User-friendly web platform for time series forecasting with multiple models and LLM-assisted interpretation.
Self-service platform for interacting with open-source large language models via web chat interface or OpenAI-compatible API.
Open-source automated protocol adherence platform using finite state machines and conversational AI for clinical research.
Platform for training and deploying foundational vision AI models using self-supervised learning on Vision Transformers.