CME ExaminationCME activityContinuing Medical Education Exam: May 2021
Section snippets
Instructions:
The GIE: Gastroinintestinal Endoscopy CME Activity can now be completed entirely online. To complete do the following:
- 1.
Read the CME articles in this issue carefully and complete the activity:
Bang CS, Lee JJ, Baik GH. Computer-aided diagnosis of esophageal cancer and neoplasms in endoscopic images: a systematic review and meta-analysis of diagnostic test accuracy. Gastrointest Endosc 2021;93:1006-15.
McCarty TR, Paleti S, Rustagi T. Molecular analysis of EUS-acquired pancreatic cyst fluid for KRAS
Target Audience
This activity is designed for physicians who are involved with providing patient care and who wish to advance their current knowledge of clinical medicine.
Learning Objectives
Upon completion of this educational activity, participants will be able to:
- 1.
Describe the diagnostic test accuracy of deep machine-based learning algorithms for esophageal neoplasms using endoscopic images.
- 2.
Evaluate the molecular analysis of EUS-acquired pancreatic cyst fluid for K-ras and G-nas mutations for diagnosis of intraductal papillary mucinous neoplasia and mucinous cystic lesions.
- 3.
Explain the EUS-guided gastroenterostomy learning curve and its metrics.
- 4.
Identify the effect of endoscopic
Continuing Medical Education
The American Society for Gastrointestinal Endoscopy (ASGE) is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.
The ASGE designates this Journal-based CME activity for a maximum of 1.0 AMA PRA Category 1 CreditTM. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
Activity Start Date: May 1, 2021
Activity Expiration Date: May 31, 2022
Disclosures
Disclosure information for authors of the articles can be found with the article in the abstract section. All disclosure information for GIE editors can be found online at http://www.giejournal.org/content/conflictofinterest. CME editors, and their disclosures, are as follows:
Prasad G. Iyer, MD (Associate Editor for Journal CME)
Consulting/Advisory/Speaking: Olympus; Research Support: Takeda Pharma
Amit Rastogi, MD (Associate Editor for Journal CME)
Consulting/Advisory/Speaking: Olympus
Karthik
Question 1 objective:
Describe the diagnostic test accuracy of deep machine-based learning algorithms for esophageal neoplasms using endoscopic images.
Rationale for correct response:
Although squamous cell esophageal cancer has constituted the majority of esophageal cancer cases worldwide, the proportion of adenocarcinomas has continued to increase in recent years. Consequently, much focus has been placed on the identification and surveillance of Barrett’s esophagus to identify early stage cancer and improve outcomes.1 However, the efficacy of endoscopic surveillance in Barrett’s has proven to be somewhat disappointing.2 As a result, many esophageal neoplastic lesions are