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
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.
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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.
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Authors: Holroyd Alexandria , Bai Harrison , Liu Shixin , Xiao Yanhe , Liu Yalin , Wu Jing , States Lisa
Keywords: Machine learning, Radiomics, Neuroblastoma