Clinicians often produce large amounts of data, from patient metrics to drug component analysis. Classical statistical analysis can provide a peek into data interactions, but in many cases, machine learning can provide additional insight into new features. Recently, with the boom of new artificial intelligence models, these clinicians are more…
Web-Based Segment Anything for Segmenting Medical Images
Segment Anything is a segmentation algorithm created by Meta Research. In order to try and make segmentation of medical images available to UK Hospital staff, a web interface which allows for the layperson to interact with segmentation should be utilized. Meta Research provided a sample web interface which precompiled…
Real-Time Visualization Interface for Smell Sensor
We developed a program that visualizes the raw input data and ML-based smell detection analysis of the SmartNanotubes Smell Inspector. The Smell Inspector is based on electronic nose (E-nose) technology that uses nanomaterial elements to detect odors or volatile organic compounds (VOCs). Classification of smells occurs through pattern recognition algorithms…
Library support for Philips iSyntax format
The Philips iSyntax format is not directly supported by common open source digital pathology libraries. Currently, in order to use iSyntax files outside of the Philips environment one must either use the iSyntax SDK or convert iSyntax files to a supported format such as TIFF. The iSyntax SDK Terms &…
Abnormality Detection in Mammograms Using Segment Anything
In early April 2023, Meta AI released Segment Anything (SAM), an machine-learning based segmentation model. The repository model of SAM operates on a very general image database, so we have been re-training SAM to specifically process mammograms and identify any abnormalities within. In 2020, it is estimated that there were…
Ultrasound trauma detection on edge device
We developed an AI model for detection of ultrasound image adequacy and positivity for FAST exam (Focused Assessment with Sonography in Trauma [1]). The results are accepted for publication in Journal of Trauma and Acute Care Surgery. We deployed the model (based on Densenet-121 [2]) on an edge device (Nvidia…
Time Series Forecasting
Time series forecasting is the process of analyzing historical data in order to draw conclusions and predict future outcomes. For this project, we explore, compare, and contrast different methods and models of forecasting to determine the advantages and disadvantages of these different systems. We explore, through research papers and code…
Genomic Pipeline Management System (GPMS)
Genomics Processing at University of Kentucky Healthcare With the recent development of next-generation sequencing (NGS,) more institutions are looking to leverage genomic sequencing for both academic research purposes as well as clinical cancer diagnostic assistance. In clinical applications, this can take the form of pipelines which translate some or all…
UK Digital Twin Environment to Transport Clinical Specimens using AI
Introduction: The Institute for Biomedical Informatics is always looking for ways to improve the efficiency of our hospital system. One way to do this is through the transportation of clinical specimens from Chandler Hospital to Shriner's Hospital and Good Samaritan Hospital using AI pathfinding and collision avoidance. By training a…
Survey of Machine Learning Techniques To Predict Heartbeat Arrhythmias
Abstract - Many works in biomedical computer science research use machine learning techniques to give accurate results. However, these techniques may not be feasible for real-time analysis of data pulled from live hospital feeds. In this project, different machine learning techniques are compared from various sources to find one that…