Desai Sudhen,  Steve Mccaulley,  Vaidya Vinay

Final Pr. ID: Poster #: EDU-041

Patients requiring enteral support typically have not had a primary provider for the maintenance of their enteral tubes and at many institutions are only seen on an as-needed basis. Specifically, patients with gastrojejunal feeding tubes tend to present acutely (e.g. tube occlusion, dysfunction or accidental removal) to Interventional Radiology (IR) departments with need for exchange. Standard patient presentation is via the ED or their GI offices after significant time investment on the part of the families and involved providers to arrange the IR visit. Read More

Authors:  Desai Sudhen , Steve Mccaulley , Vaidya Vinay

Keywords:  Data Management, AI, Gastrojejunal feeding tube

Alkhulaifat Dana,  Rafful Patricia,  Lopez Rippe Julian,  Khalkhali Vahid,  Welsh Michael,  Wieczkowski Sydney,  Reid Janet,  Sotardi Susan

Final Pr. ID: Poster #: EDU-047

Artificial intelligence (AI) applications for radiology have undergone exponential growth in recent years, owing to the development of large datasets for use in machine learning algorithms and technological advancements in the field of imaging informatics. However, the advancement of AI algorithms in pediatric radiology has lagged behind adult applications. Currently, only seven commercially-available AI algorithms have received FDA approval for use in the pediatric population [1]. One of the major factors limiting the use of AI in pediatric radiology is the lack of the requisite large pediatric imaging datasets.
In AI research and implementation, pediatric radiologists serve as stewards of imaging data. As such, pediatric radiologists should be trained in AI data management, including best practices for the selection, curation, de-identification, and storage of radiology data. Since a necessary first step in the development of AI algorithms requires the curation of large datasets, pediatric radiologists should have a basic understanding of how to archive imaging data for AI research and validation. However, few resources are currently available to provide targeted education for pediatric radiologists with respect to AI data curation..
The aim of this educational exhibit is to provide an educational resource specifically for pediatric radiologists which teaches best practices for data management in AI research, including the selection of patient cohorts, data anonymization techniques, image annotation and segmentation methods, and data storage tools. This exhibit integrates our professional experience, with a thorough literature review of prior AI research, into an educational resource to teach data science methodologies for the management of AI research and clinical implementation to the pediatric radiology community.

References
1. AI Central. https://aicentral.acrdsi.org/. Accessed 18 Oct 2022 Read More

Authors:  Alkhulaifat Dana , Rafful Patricia , Lopez Rippe Julian , Khalkhali Vahid , Welsh Michael , Wieczkowski Sydney , Reid Janet , Sotardi Susan

Keywords:  Artificial Intelligence, Education, Data Management

Zhu Xiaowei,  Whitaker Jayme,  Shellikeri Sphoorti,  Cahill Anne Marie

Final Pr. ID: Poster #: EDU-046

It is important for radiologists to recognize and discuss with patients and families the potential risks and clinical manifestations of high Peak Skin Dose. In children undergoing complex Interventional procedures accurate Peak Skin Dose estimates are complicated and time consuming despite reference point doses being available. The availability of the Dose Structure Report (SR) on modern fluoroscopic equipment allows such estimates to be timely and consistent. The process of creating a Peak Skin Dose estimate using a validated radiation data management system (RDMS), capable of collecting detailed acquisition data and modeling will be discussed. Read More

Authors:  Zhu Xiaowei , Whitaker Jayme , Shellikeri Sphoorti , Cahill Anne Marie

Keywords:  Peak Skin Dose Estimation, Interventional fluoroscopic, Radiation Data Management System