Simulated learning environment for diagnosis of appendicitis and other causes of abdominal pain in pregnant patients using MRI

• Published in: Current problems in diagnostic radiology
• Main Authors: Ludwig, Daniel R., Strnad, Benjamin S., Shetty, Anup S., Tsai, Richard, Mellnick, Vincent M
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 04.10.2024
• Subjects: Appendicitis; Pacsbin; Pregnancy; Radiology education; Simulated learning; Pregnancy; Radiology education; CT; Simulated learning; PACS; Appendicitis; MRI; Pacsbin; HIPAA; DICOM
• ISSN: 0363-0188; 1535-6302; 1535-6302
• DOI: 10.1067/j.cpradiol.2024.10.005

Acute appendicitis is a common surgical condition which is usually diagnosed on CT in adult patients, though MRI is frequently used as a first-line diagnostic test in pregnant patients due to its lack of ionizing radiation and superior ability to visualize the appendix compared to ultrasound. Interpretation of abdominal MRI exams in pregnant patients with suspected appendicitis is an important skill in clinical practice, but one that is difficult to become proficient at due to its relative infrequence, even in a high-volume practice. We created a simulation-based platform built on an online radiology viewing platform (Pacsbin) for training residents and abdominal imaging fellows to interpret pregnant appendicitis MRI exams, which we made publicly available for use by trainees at any institution (forms.office.com/r/FYyq06rw0v). This platform was used to train our 2024-2025 abdominal imaging fellows (N=8), and we collected pre- and post-intervention survey data which included level of confidence (Likert scale,1-5) in approaching these studies. We discuss and illustrate the content of our case set, including various teaching points we emphasize throughout the exercise. Among our eight body imaging fellows, the level of confidence in approaching pregnant appendicitis MRI studies after the intervention increased from 2.4 ± 0.7 (range 1-3) to 3.6 ± 0.5 (range 3-4; p = 0.01). Simulation-based training sets such as this have the potential to supplement traditional approaches in radiology education across a broad range of radiology subspecialities and imaging modalities.