Publications by authors named "Vivek S Yedavalli"

4 Publications

  • Page 1 of 1

Leveraging artificial intelligence in ischemic stroke imaging.

J Neuroradiol 2021 May 11. Epub 2021 May 11.

Department of Radiology, University of Alabama at Birmingham, 619 19th St S, Birmingham, AL 35294, USA. Electronic address:

Artificial intelligence (AI) is having a disruptive and transformative effect on clinical medicine. Prompt clinical diagnosis and imaging are critical for minimizing the morbidity and mortality associated with ischemic strokes. Clinicians must understand the current strengths and limitations of AI to provide optimal patient care. Ischemic stroke is one of the medical fields that have been extensively evaluated by artificial intelligence. Presented herein is a review of artificial intelligence applied to clinical management of stroke, geared toward clinicians. In this review, we explain the basic concept of AI and machine learning. This review is without coding and mathematical details and targets the clinicians involved in stroke management without any computer or mathematics' background. Here the AI application in ischemic stroke is summarized and classified into stroke imaging (automated diagnosis of brain infarction, automated ASPECT score calculation, infarction segmentation), prognosis prediction, and patients' selection for treatment.
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http://dx.doi.org/10.1016/j.neurad.2021.05.001DOI Listing
May 2021

Artificial intelligence in stroke imaging: Current and future perspectives.

Clin Imaging 2021 Jan 21;69:246-254. Epub 2020 Sep 21.

Department of Radiology, Alberta Children's Hospital Research Institute, Hotchkiss Brain Institute Cumming School of Medicine, University of Calgary, HSC Building, Room 2913, 3330 Hospital Drive NW, Calgary, AB T2N 4N1, Canada; Department Clinical Neurosciences, Alberta Children's Hospital Research Institute, Hotchkiss Brain Institute Cumming School of Medicine, University of Calgary, HSC Building, Room 2913, 3330 Hospital Drive NW, Calgary, AB T2N 4N1, Canada. Electronic address:

Artificial intelligence (AI) is a fast-growing research area in computer science that aims to mimic cognitive processes through a number of techniques. Supervised machine learning, a subfield of AI, includes methods that can identify patterns in high-dimensional data using labeled 'ground truth' data and apply these learnt patterns to analyze, interpret, or make predictions on new datasets. Supervised machine learning has become a significant area of interest within the medical community. Radiology and neuroradiology in particular are especially well suited for application of machine learning due to the vast amount of data that is generated. One devastating disease for which neuroimaging plays a significant role in the clinical management is stroke. Within this context, AI techniques can play pivotal roles for image-based diagnosis and management of stroke. This overview focuses on the recent advances of artificial intelligence methods - particularly supervised machine learning and deep learning - with respect to workflow, image acquisition and reconstruction, and image interpretation in patients with acute stroke, while also discussing potential pitfalls and future applications.
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http://dx.doi.org/10.1016/j.clinimag.2020.09.005DOI Listing
January 2021

Amyotrophic Lateral Sclerosis and its Mimics/Variants: A Comprehensive Review.

J Clin Imaging Sci 2018 6;8:53. Epub 2018 Dec 6.

Department of Radiology, Advocate Illinois Masonic Medical Center, Chicago, Illinois, USA.

Motor neuron diseases (MNDs) are a debilitating subset of diseases, which result in progressive neuronal destruction and eventual loss of voluntary muscular function. These entities are often challenging to distinguish and accurately diagnose given overlapping clinical pictures and overall rarity. This group of diseases has a high morbidity and mortality rate overall and delineating each type of disease can help guide appropriate clinical management and improve quality of life for patients. Of all MNDs, amyotrophic lateral sclerosis (ALS) is by far the most common comprising 80%-90% of cases. However, other mimics and variants of ALS can appear similar both clinically and radiographically. In this review, we delve into the epidemiological, physiological, neuroimaging, and prognostic characteristics and management of ALS and its most common MND mimics/variants. In doing so, we hope to improve accuracy in diagnosis and potential management for this rare group of diseases.
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http://dx.doi.org/10.4103/jcis.JCIS_40_18DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6302559PMC
December 2018

Residents' Perceptions of Usage of the Current Alumni and Attending Network for a Formal Mentorship Program in an Academic Affiliated Community Hospital Radiology Residency.

Curr Probl Diagn Radiol 2019 Mar - Apr;48(2):105-107. Epub 2018 Jan 31.

Advocate Illinois Masonic Medical Center, Chicago, IL; Department of Radiology, Advocate Illinois Masonic Medical Center, Chicago, IL.

Mentor-mentee relationships within radiology residencies can add significant value to a resident's overall experience. Studies demonstrate that mentorship programs can increase satisfaction for residents and faculty alike by reducing stress, easing career related decisions, increasing involvement with research, improving teaching and communication skills, and finally increasing leadership roles. In a survey of radiology program directors, 85% of program directors find such a program beneficial but only 57% have a formal program in place. Totally, 42% of program directors believe a structured mentorship program is necessary. Studies have also shown that female residents prefer female mentors. Alumni serve as an ideal group for resident mentorship as they do not face the pressures of internal faculty. No study to date in diagnostic radiology literature uses an alumni network in establishing a formal mentorship program. The objective of this study is to implement a formal mentorship program within an academic affiliated radiology residency by using program alumni and internal attending physicians for potentially increasing faculty engagement, improving resident morale, research opportunities, and networking for fellowship and job opportunities.
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http://dx.doi.org/10.1067/j.cpradiol.2018.01.006DOI Listing
March 2019
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