Feasibility of a deep learning artificial intelligence model for the diagnosis of pediatric ileocolic intussusception with grayscale ultrasonography.

IF 2.4 3区医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Ultrasonography Pub Date : 2023-10-10 DOI:10.14366/usg.23153

Se Woo Kim, Jung-Eun Cheon, Young Hun Choi, Jae-Yeon Hwang, Su-Mi Shin, Yeon Jin Cho, Seunghyun Lee, Seul Bi Lee

引用次数: 0

Abstract

This study explored the feasibility of utilizing a deep learning artificial intelligence (AI) model to detect ileocolic intussusception on grayscale ultrasound images.

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利用灰度超声波检查诊断小儿回结肠肠套叠的深度学习人工智能模型的可行性。

本研究探讨了利用深度学习人工智能（AI）模型在灰度超声图像上检测回结肠肠套叠的可行性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Ultrasonography Medicine-Radiology, Nuclear Medicine and Imaging

CiteScore

5.10

自引率

6.50%

发文量

审稿时长

15 weeks

期刊介绍： Ultrasonography, the official English-language journal of the Korean Society of Ultrasound in Medicine (KSUM), is an international peer-reviewed academic journal dedicated to practice, research, technology, and education dealing with medical ultrasound. It is renamed from the Journal of Korean Society of Ultrasound in Medicine in January 2014, and published four times per year: January 1, April 1, July 1, and October 1. Original articles, technical notes, topical reviews, perspectives, pictorial essays, and timely editorial materials are published in Ultrasonography covering state-of-the-art content. Ultrasonography aims to provide updated information on new diagnostic concepts and technical developments, including experimental animal studies using new equipment in addition to well-designed reviews of contemporary issues in patient care. Along with running KSUM Open, the annual international congress of KSUM, Ultrasonography also serves as a medium for cooperation among physicians and specialists from around the world who are focusing on various ultrasound technology and disease problems and relevant basic science.