Rad Tech CE, ASRT, ARRT® CE, Category A Credits | Radiology Continuing Education

Approvals/Requirements Satisfied by eRADIMAGING Courses

  • ASRT approval for ARRT Category A credit
  • All Courses eligible of international radiographers' CPD requirements
  • ASRT and MDCB are approved continuing education providers of ARRT and all courses are accepted by ARRT
  • California CE requirements met for all radiography courses
  • NMTCB accepted (All Courses)
  • All Courses available for RRAs
  • ARMRIT accepted (All MRI Courses)
  • MDCB approval by the Medical Dosimetrist Certification (Selected Courses)
  • Florida approval for all courses 1 credit or more
  • ARDMS accepted (All Courses)
  • CAMRT and Sonography Canada recognize the ASRT approval (All Courses)
  • Approval: This course is approved by ASRT - an approved continuing education provider of ARRT.
  • Release Date: 6/5/2020
  • Expiration Date: 7/1/2023
  • Credit Hours: 1 Credit
  • Course Description and objectives:

    Course Description
    The use of artificial intelligence (AI) in radiology has been evolving toward the use of deep learning with convolutional neural networks (CNNs), which are better equipped to utilize data that are presented in the form of pixelated images. Such CNNs can assist radiologists with the task of interpreting the vast quantities of digital data housed in picture archiving and communication systems (PACS). In addition, complex qualitative and quantitative analyses can be performed using CNNs that would otherwise not be possible. Regardless of the clinical question, a series of steps are typically performed, including identifying an appropriate dataset, labeling that dataset, and sampling the dataset to include training, validation, and testing subsets. Currently, common clinical applications include detection, characterization, and segmentation of lesions. There are hundreds of recent publications demonstrating application of deep learning and CNNs in the analysis of pathology in the brain, spine, chest, abdomen and pelvis, breast, and musculoskeletal system. The American College of Radiology has launched the AI-LAB™, a toolkit to assist radiologists with incorporation of AI into their clinical practice, and to promote collaboration and sharing of best practices. Limitations and challenges exist to adoption of AI in clinical radiology, but with sufficient training and support from a multidisciplinary team, application of deep learning with CNNs is poised to transform how radiologists practice their trade.

    Learning Objectives
    After reading this article, the participant should be able to:

    • Explain the differences between artificial intelligence, machine learning, and deep learning.
    • Review the steps in creating and implementing a convolutional neural network (CNN) for radiologic application.
    • Summarize published research findings using CNNs for a variety of clinical applications.
    • Describe the limitations and challenges that exist for implementation of CNNs into clinical radiology practice.

    Categories: Technology, Computed Tomography (CT), magnetic resonance imaging (MRI), Nuclear Medicine, Digital Radiography

  • CE Information:

    CE Information
    In order to receive CE credit, you must first complete the activity content. When completed, go to the “Take CE Test!” link to access the post-test.

    Submit the completed answers to determine if you have passed the post-test assessment. You must answer 6 out of 8 questions correctly to receive the CE credit. You will have no more than 3 attempts to successfully complete the post-test.

    Participants successfully completing the activity content and passing the post-test will receive 1.0 ARRT Category A credits or 1.0 AMA PRA Category 1 Credit(s)™.

    Approved by the American Society of Radiologic Technologists for ARRT Category A credit.

    Approved by the state of Florida for ARRT Category A credit.

    Texas direct credit.

    This activity may be available in multiple formats or from different sponsors. ARRT does not allow CE activities such as Internet courses, home study programs, or directed readings to be repeated for CE credit in the same biennium.

    TARGET AUDIENCE
    This activity is designed to meet the needs of Radiologic Technologists and Physicians, specifically Radiologists.

    This activity is provided by AKH Inc., Advancing Knowledge in Healthcare, for physician credit.

    Release Date: x/x/2019

    Expiration Date: x/x/2021

    Physicians
    This activity has been planned and implemented in accordance with the Essential Areas and policies of the Accreditation Council for Continuing Medical Education (ACCME) through the joint providership of AKH Inc., Advancing Knowledge in Healthcare, and eRADIMAGING. AKH Inc., Advancing Knowledge in Healthcare, is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for Physicians.

    AKH Inc., Advancing Knowledge in Healthcare, designates this enduring activity for a maximum of 1.0 AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

    Physician Assistants
    NCCPA accepts AMA PRA Category 1 Credit™ from organizations accredited by ACCME.

    If you have any questions relating to the CME accreditation of this activity, please contact AKH Inc. at jgoldman@akhcme.com.

    COMMERCIAL SUPPORT:
    This activity is NOT supported by a commercial educational grant.

    DISCLOSURE DECLARATION
    It is the policy of AKH Inc. to ensure independence, balance, objectivity, scientific rigor, and integrity in all of its continuing education activities. The faculty must disclose to the participants any significant relationships with commercial interests whose products or devices may be mentioned in the activity or with the commercial supporter of this continuing education activity. Identified conflict of interest is resolved by AKH prior to accreditation of the activity.

    DISCLOSURE OF UNLABELED USE AND INVESTIGATIONAL PRODUCTS
    This educational activity does not include discussion of uses of agents that are investigational and/or unapproved by the FDA. Please refer to the official prescribing information for each product for discussion of approved indications, contraindications, and warnings.

    DISCLAIMER
    AKH Inc.’s courses are designed solely to provide healthcare professionals with information to assist in their practice and professional development. The courses are researched thoroughly, utilizing current literature and including practical experiences. AKH’s courses are not to be considered a diagnostic tool to replace professional advice or treatment. The courses serve as a general guide to the healthcare professional, and therefore, they cannot be considered as giving legal, nursing, medical, or other professional advice in specific cases. AKH educational courses do not endorse commercial products. The author(s) and the publisher specifically disclaim responsibility for any adverse consequences resulting directly or indirectly from information in the courses. AKH further disclaims any responsibility for undetected errors, or from the reader’s misunderstanding of the course.


AI in Radiology: Evolving Toward Deep Learning Techniques for CT, MR, PET, and X-ray Imaging

Cindy Schultz, PhD*

*Monarch Medical Writing, Ferrisburgh, VT

Address correspondence to: Cindy Schultz, PhD. Email: cindyschultz@monarchmedicalwriting.com

Disclosure statement: The author reports having no significant financial or advisory relationships with corporate organizations related to this activity.

 

ABSTRACT

The use of artificial intelligence (AI) in radiology has been evolving toward the use of deep learning with convolutional neural networks (CNNs), which are better equipped to utilize data that are presented in the form of pixelated images. Such CNNs can assist radiologists with the task of interpreting the vast quantities of digital data housed in picture archiving and communication systems (PACS). In addition, complex qualitative and quantitative analyses can be performed using CNNs that would otherwise not be possible. Regardless of the clinical question, a series of steps are typically performed, including identifying an appropriate dataset, labeling that dataset, and sampling the dataset to include training, validation, and testing subsets. Currently, common clinical applications include detection, characterization, and segmentation of lesions. There are hundreds of recent publications demonstrating application of deep learning and CNNs in the analysis of pathology in the brain, spine, chest, abdomen and pelvis, breast, and musculoskeletal system. The American College of Radiology has launched the AI-LABTM, a toolkit to assist radiologists with incorporation of AI into their clinical practice, and to promote collaboration and sharing of best practices. Limitations and challenges exist to adoption of AI in clinical radiology, but with sufficient training and support from a multidisciplinary team, application of deep learning with CNNs is poised to transform how radiologists practice their trade.    

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Sample eRADIMAGING Course *

* This sample course is for reference purposes only. It is not currently available for earning CE credits. To earn ARRT CE credits please subscribe to eRADIMAGING where you will see a complete listing of all active and eligible CE courses.

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