Medicine and Health Sciences Commons^™

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 …

Incorporation Of A Machine Learning Algorithm With Object Detection Within The Thyroid Imaging Reporting And Data System Improves The Diagnosis Of Genetic Risk., Shuo Wang, Jiajun Xu, Aylin Tahmasebi, Kelly Daniels, Ji-Bin Liu, Joseph Curry, Elizabeth Cottrill, Andrej Lyshchik, John R Eisenbrey

Department of Radiology Faculty Papers

Background: The role of next generation sequencing (NGS) for identifying high risk mutations in thyroid nodules following fine needle aspiration (FNA) biopsy continues to grow. However, ultrasound diagnosis even using the American College of Radiology's Thyroid Imaging Reporting and Data System (TI-RADS) has limited ability to stratify genetic risk. The purpose of this study was to incorporate an artificial intelligence (AI) algorithm of thyroid ultrasound with object detection within the TI-RADS scoring system to improve prediction of genetic risk in these nodules.

Methods: Two hundred fifty-two nodules from 249 patients that underwent ultrasound imaging and ultrasound-guided FNA with NGS with …

Characterization Of Indeterminate Breast Lesions On B-Mode Ultrasound Using Automated Machine Learning Models, Shuo Wang, Sihua Niu, Enze Qu, Flemming Forsberg, Annina Wilkes, Alexander Sevrukov, Kibo Nam, Robert F. Mattrey, Haydee Ojeda-Fournier, John R. Eisenbrey

Department of Radiology Faculty Papers

Purpose: While mammography has excellent sensitivity for the detection of breast lesions, its specificity is limited. Adjunct screening with ultrasound may partially alleviate this issue, but also increases false positives, resulting in unnecessary biopsies. This study investigated the use of Google AutoML Vision (Mountain View, CA), a commercially available machine learning service, to both identify and characterize indeterminate breast lesions on ultrasound.

Methods: B-mode images from 253 independent cases of indeterminate breast lesions scheduled for core biopsy were used for model creation and validation. The performances of two sub-models from AutoML Vision, the image classification model and object detection model …

Artificial Intelligence In Ultrasound Imaging: Current Research And Applications, Shuo Wang, Bs, Ji-Bin Liu, Md, Ziyin Zhu, Md, John Eisenbrey, Phd

Department of Radiology Faculty Papers

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent software or system based on big data information, machine learning and deep learning technologies. The rapid development of science and technology as well as internet communication has enabled AI and big data to gradually apply to many fields of health care. The modern imaging medicine is one of the first areas that AI can play an important role and applications. As cross-sectional imaging, ultrasound (US) is well suitable for AI technology to standardize imaging protocols and improve diagnostic accuracy. This article reviews current AI technology …