Rostad Bradley,  Richer Edward,  Riedesel Erica,  Alazraki Adina

Final Pr. ID: Paper #: 026

Foreign body ingestion is common in children. Timely diagnosis of the nature and location of the foreign body is important. A button battery which lodges in the esophagus can quickly cause severe esophageal and mediastinal injury. Machine learning that can detect anatomical regions of interest is an important step in computerized foreign body localization and may result in prioritization of radiographs with mediastinal foreign bodies. The purpose of this study is to develop a machine learning model to identify anatomical regions of interest on pediatric foreign body series radiographs. Read More

Authors:  Rostad Bradley , Richer Edward , Riedesel Erica , Alazraki Adina

Keywords:  Machine learning, Artificial intelligence, Foreign body

Rostad Bradley,  Richer Edward,  Riedesel Erica,  Alazraki Adina

Final Pr. ID: Paper #: 025

Timely diagnosis of foreign body ingestion in children is important, particularly in the case of an ingested button battery. A button battery which lodges in the esophagus can quickly cause severe esophageal and mediastinal injury. It is also important to distinguish an ingested button battery from a coin; a button battery may be misdiagnosed a coin because of its similar radiographic appearance. The purpose of this study is to develop a machine learning model to identify button batteries and coins on pediatric foreign body series radiographs. Read More

Authors:  Rostad Bradley , Richer Edward , Riedesel Erica , Alazraki Adina

Keywords:  Machine learning, Artificial intelligence, Button battery

Meda Karthik,  Milla Sarah,  Rostad Bradley

Final Pr. ID: Paper #: 024

Machine learning that can identify and localize objects in an image using a labeled bounding box is called object detection. The purpose of this study is to demonstrate object detection in identifying rickets on pediatric wrist radiographs. Read More

Authors:  Meda Karthik , Milla Sarah , Rostad Bradley

Keywords:  Machine learning, Artificial intelligence, rickets

Otjen Jeffrey,  Stanescu A. Luana,  Ansdell David,  Alessio Adam,  Parisi Marguerite

Final Pr. ID: Poster #: SCI-023

Pelvic ultrasound is commonly used to detect ovarian torsion, but the diagnosis remains challenging as there is no single pathognomonic feature. This retrospective case-controlled study aims to identify an algorithm to detect torsion based on common ultrasound imaging features. Read More

Authors:  Otjen Jeffrey , Stanescu A. Luana , Ansdell David , Alessio Adam , Parisi Marguerite

Keywords:  torsion, ultrasound, machine learning

Satoor Vamsish,  Marine Megan

Final Pr. ID: Poster #: SCI-034

Background/Objective:
Rib fractures are one of the most specific fractures in child abuse and are among the most common identified. Diagnosis of an unsuspected rib fracture in a young child or infant is highly concerning for child abuse. Given rib fractures, particularly acute rib fractures, can be subtle and difficult for even experienced radiologists to identify, a screening diagnostic tool to improve the detection accuracy would provide significant value. The objective of this investigation is to create a machine learning algorithm with the ability to recognize the presence or absence of rib fractures on chest radiographs in pediatric patients less than 3 years old. Read More

Authors:  Satoor Vamsish , Marine Megan

Keywords:  Machine learning, Chest x-ray, Pediatric

Holroyd Alexandria,  Bai Harrison,  Liu Shixin,  Xiao Yanhe,  Liu Yalin,  Wu Jing,  States Lisa

Final Pr. ID: Paper #: 156

Neuroblastoma is a clinically heterogeneous pediatric malignancy, varying in location, histopathologic appearance, and biologic characteristics. Genetics plays an important role in the prognosis. Amplification of the MYC family member, MYCN, is found in 25% of cases and correlates with high-risk disease and poor prognosis. However, genetic information can only be obtained via surgery or biopsy with concurrent morbidity and sampling variability associated with biopsy. The ability to detect MYCN amplification from routine pre-operative imaging can stratify neuroblastoma risk groups and affect clinical decision making. The purpose of this study was to predict the patient's MYCN status based on radiomics analysis of the magnetic resonance imaging (MRI) characteristics in patients with neuroblastoma. Read More

Authors:  Holroyd Alexandria , Bai Harrison , Liu Shixin , Xiao Yanhe , Liu Yalin , Wu Jing , States Lisa

Keywords:  Machine learning, Radiomics, Neuroblastoma