Yacoub Daniel,  Wang Kuan Chung,  Shah Prakeshkumar,  Moineddin Rahim,  Doria Andrea

Final Pr. ID: Poster #: SCI-063

Increased fracture risk is a complication that occurs in the context of primary bone diseases such as osteogenesis imperfecta (OI). Despite being considered as the reference-standard, the use of dual-energy X-ray absorptiometry (DXA) to evaluate fragility fractures in OI has not been validated by prior systemic review. Identifying patients at greatest risk for bone fragility fractures and determining skeletal health markers that can monitor bone mass concerning response to bone-active treatments are important issues for clinicians. In this systematic review we assessed the clinical utility of DXA for evaluating osteoporotic bone in OI pediatric patients according to the U.S. Preventive Services Task Force guidelines. Read More

Authors:  Yacoub Daniel , Wang Kuan Chung , Shah Prakeshkumar , Moineddin Rahim , Doria Andrea

Keywords:  Dual-energy X-ray absorptiometry (DXA), Osteogenesis imperfecta, Systematic review, Fracture, Clinimetric property

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

Kumar Soryan,  Sollee John,  Choi Una,  Lin Cheng Ting,  Bai Harrison,  Jiao Zhicheng

Final Pr. ID: Poster #: SCI-037

The purpose of this study is to develop a deep learning algorithm for detecting COVID-19 in chest x-rays of pediatric patients. Read More

Authors:  Kumar Soryan , Sollee John , Choi Una , Lin Cheng Ting , Bai Harrison , Jiao Zhicheng

Keywords:  AI, X-Ray, Chest

Mahomed Nasreen

Final Pr. ID: Poster #: EDU-010

Abstract: HIV is a global pandemic. According to the UNAIDS Report on the Global Aids Epidemic 2013, approximately 3.3 million children under the age of 15 years are living with HIV infection globally. Sub-Saharan Africa has the highest burden of disease with 2.9 million of HIV-infected children. HIV has affected the epidemiology of childhood pneumonia, changing the spectrum of pathogens, antimicrobial susceptibility of bacteria and prognostic outcome. More than 70% of HIV-infected children will suffer at least one episode of a pulmonary infection in the course of their illness. The pneumococcal conjugate vaccine (PCV) demonstrated vaccine efficacy of 20% in HIV-uninfected children and 13% in HIV-infected children in South African using WHO standardized chest X-ray interpretation criteria. The chest X-ray remains the most readily available and the commonest imaging modality for childhood pneumonia. A combination of clinical findings with pattern recognition on chest X-ray narrows the differential diagnosis. We present a pictorial review of chest X-ray findings in HIV-infected children due to infectious causes: pulmonary TB, bacterial pneumonia, Pneumocystis jiroveci pneumonia, viral pneumonia and non- infectious causes: immune reconstitution inflammatory syndrome (IRIS), lymphocytic interstitial pneumonia (LIP) and lymphoma.
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Authors:  Mahomed Nasreen

Keywords:  chest X-rays, HIV-infected children