Integrative Medicine Commons^™

Artificial Intelligence For Detecting Non-Alcoholic Steatohepatitis (Nash), Muhammad Wildan Gifari, Yogi Ramadhani, Dhadhang Wahyu Kurniawan

Pharmaceutical Sciences and Research

Non-alcoholic steatohepatitis (NASH), an inflammatory disease of the liver, has recently raised concern among healthcare professionals worldwide due to its asymptomatic features, making early diagnosis challenging. If left unnoticed, NASH often progresses to lethal diseases such as liver fibrosis or hepatocellular carcinoma. Recent developments in the field of artificial intelligence (AI) might facilitate the early diagnosis of NASH in a more efficient manner, forming a promising strategy to diagnose patients. In simple terms, AI is any machine that is capable of human-level intelligence, including visual perception, speech recognition, or decision making. A subclass of AI, which particularly deals with knowledge-based …

Machine Learning-Based Classification Of Chronic Traumatic Brain Injury Using Hybrid Diffusion Imaging, Jennifer Muller, Ruixuan Wang, Devon Middleton, Mahdi Alizadeh, Kichang Kang, Ryan Hryczyk, George Zabrecky, Chloe Hriso, Emily Navarreto, Nancy Wintering, Anthony J. Bazzan, Chengyuan Wu, Daniel A. Monti, Xun Jiao, Qianhong Wu, Andrew B. Newberg, Feroze Mohamed

Marcus Institute of Integrative Health Faculty Papers

BACKGROUND AND PURPOSE: Traumatic brain injury (TBI) can cause progressive neuropathology that leads to chronic impairments, creating a need for biomarkers to detect and monitor this condition to improve outcomes. This study aimed to analyze the ability of data-driven analysis of diffusion tensor imaging (DTI) and neurite orientation dispersion imaging (NODDI) to develop biomarkers to infer symptom severity and determine whether they outperform conventional T1-weighted imaging.

MATERIALS AND METHODS: A machine learning-based model was developed using a dataset of hybrid diffusion imaging of patients with chronic traumatic brain injury. We first extracted the useful features from the hybrid diffusion imaging …

Identification Of Chronic Mild Traumatic Brain Injury Using Resting State Functional Mri And Machine Learning Techniques, Faezeh Vedaei, Najmeh Mashhadi, George Zabrecky, Daniel A. Monti, Emily Navarreto, Chloe Hriso, Nancy Wintering, Andrew B. Newberg, Feroze Mohamed

Department of Radiology Faculty Papers

Mild traumatic brain injury (mTBI) is a major public health concern that can result in a broad spectrum of short-term and long-term symptoms. Recently, machine learning (ML) algorithms have been used in neuroscience research for diagnostics and prognostic assessment of brain disorders. The present study aimed to develop an automatic classifier to distinguish patients suffering from chronic mTBI from healthy controls (HCs) utilizing multilevel metrics of resting-state functional magnetic resonance imaging (rs-fMRI). Sixty mTBI patients and forty HCs were enrolled and allocated to training and testing datasets with a ratio of 80:20. Several rs-fMRI metrics including fractional amplitude of low-frequency …

Conference Proceedings: Aurora Scientific Day 2020

Journal of Patient-Centered Research and Reviews

Abstracts published in this supplement were among those presented at the 46th annual Aurora Scientific Day research symposium, held virtually on May 20, 2020. The symposium provides a forum for describing research studies conducted by faculty, fellows, residents, and allied health professionals affiliated with Wisconsin-based Aurora Health Care, a part of the Advocate Aurora Health health system, which publishes the Journal of Patient-Centered Research and Reviews.