Potentials and caveats of AI in Hybrid Imaging

State-of-the-art patient management frequently mandates the investigation of both anatomy and physiology of the patients. Hybrid imaging modalities such as the PET/MRI, PET/CT and SPECT/CT have the ability to provide both structural and functional information of the investigated tissues in a single examination. With the introduction of such advanced hardware fusion, new problems arise such as the exceedingly large amount of multi-modality data that requires novel approaches of how to extract a maximum of clinical information from large sets of multi-dimensional imaging data. Artificial intelligence (AI) has emerged as one of the leading technologies that has shown promise in facilitating highly integrative analysis of multi-parametric data. Specifically, the usefulness of AI algorithms in the medical imaging field has been heavily investigated in the realms of (1) image acquisition and reconstruction, (2) post-processing and (3) data mining and modelling. Here, we aim to provide an overview of the challenges encountered in hybrid imaging and discuss how AI algorithms can facilitate potential solutions. In addition, we highlight the pitfalls and challenges in using advanced AI algorithms in the context of hybrid imaging and provide suggestions for building robust AI solutions that enable reproducible and transparent research

A method to detect and represent temporal patterns from time series data and its application for analysis of physiological data streams

In critical care, complex systems and sensors continuously monitor patients??? physiological features such as heart rate, respiratory rate thus generating significant amounts of data every second. This results to more than 2 million records generated per patient in an hour. It???s an immense challenge for anyone trying to utilize this data when making critical decisions about patient care. Temporal abstraction and data mining are two research fields that have tried to synthesize time oriented data to detect hidden relationships that may exist in the data. Various researchers have looked at techniques for generating abstractions from clinical data. However, the variety and speed of data streams generated often overwhelms current systems which are not designed to handle such data. Other attempts have been to understand the complexity in time series data utilizing mining techniques, however, existing models are not designed to detect temporal relationships that might exist in time series data (Inibhunu & McGregor, 2016). To address this challenge, this thesis has proposed a method that extends the existing knowledge discovery frameworks to include components for detecting and representing temporal relationships in time series data. The developed method is instantiated within the knowledge discovery component of Artemis, a cloud based platform for processing physiological data streams. This is a unique approach that utilizes pattern recognition principles to facilitate functions for; (a) temporal representation of time series data with abstractions, (b) temporal pattern generation and quantification (c) frequent patterns identification and (d) building a classification system. This method is applied to a neonatal intensive care case study with a motivating problem that discovery of specific patterns from patient data could be crucial for making improved decisions within patient care. Another application is in chronic care to detect temporal relationships in ambulatory patient data before occurrence of an adverse event. The research premise is that discovery of hidden relationships and patterns in data would be valuable in building a classification system that automatically characterize physiological data streams. Such characterization could aid in detection of new normal and abnormal behaviors in patients who may have life threatening conditions

Integrated spatial decision support system for precision agriculture

Excessive application of plant nutrients and pesticides on agricultural land has resulted in both environmental degradation and economic loss to the farming community. Agricultural non-point source pollution was cited as the primary source of the water quality problems in many areas of the United States. Environmental concerns resulting from agricultural non-point source pollution has placed demands on farmers and ranchers to implement the best management practices (BMPs) to reduce the delivery of pollutants to streams and aquifers. Precision agriculture, a relatively recent crop production and agricultural management strategy holds great promise to minimize environmental pollution while to maximize economic productivity and profitability. It has benefited from rapidly evolving geospatial information technologies, such as global positing systems (GPS), geographic information systems (GIS), remote sensing (RS), and electronic sensors and intelligent controllers. However, the complexity of making routine, coherent, and cost-effective farm management decisions presents a formidable challenge to farmers. What is lacking in precision agriculture is an analytical tool that integrates these component technologies with biophysical and economic models for tactical, strategic, and policy-level decision make. In this dissertation, a decision support system called IDSSPA is developed to include modules for evaluating crop yield and chemical losses in response to site-specific management of agricultural inputs. Using this system, not only can users store, visualize, manipulate, and analyze spatial/non-spatial field experiment data, but they also can do various simulations through the easy-operated biophysical models, which take field spatial variability into account. In the system, the functionalities of the traditional models and analysis methods have been enhanced by coupling them with each other and with ArcView GIS. Uniquely designed GIS-based interfaces enable the lumped biophysical models to incorporate and represent field spatial variability. Statistical and data mining tools are also included in the system to improve analysis of field measured data and to further enhance interpretation of model simulation results. Other components incorporated into the system are as follows: The CERES-Maize plant growth model seamlessly integrated with RZWQM to provide an alternative phonologically based model for predicting growth and yield of maize (corn), and several tools for evaluating economic and ecologic risks of precision agriculture implementation. The application examples indicated that IDSSPA is a useful research and decision make tool for precision agriculture at field and watershed scales