Main Logo

Society for Pediatric Radiology – Poster Archive

Ingrid Camelo

Is this you? Register and claim your profile. Then, you can add your biography and additional Information.

Showing 3 Abstracts.

Pneumonia is a leading cause of pediatric morbidity and mortality worldwide. In 2017, 15% of under-5 mortalities were due to pneumonia. Children in sub-Saharan Africa are disproportionately affected. Chest radiography (CXR) is currently the reference standard for imaging diagnosis of pediatric lung diseases. However, radiographic equipment is not available in many clinical settings, particularly in low and middle-income countries. In these scenarios, point-of-care lung ultrasound (LUS) is much more readily accessible. Thus, it is important to understand the US appearance of both interstitial and bacterial pneumonias and how they correlate with CXR findings. In this pictorial essay, we will discuss the US appearance of common lower respiratory tract infections such as RSV, COVID-19, and bacterial pneumonia using images obtained from patients ages 1 month to 5 years with symptomatic respiratory illness in Lusaka, Zambia. All images were obtained by a technologist with a Butterfly portable ultrasound probe connected to an iPad. Images were obtained in the anterior, lateral, and posterior lung fields bilaterally. US images will be correlated with CXR findings. The following examples of LUS findings will be discussed: 1) Normal LUS: The pleural line is thin and smooth with normal lung sliding. A-lines are present, and B-lines are limited to less than three in each field of view. 2) Abnormal B-lines: When three or more B-lines are seen in a single field of view, there is an abnormal increase in interstitial fluid. A focal B line is an abnormally thickened B-line and likely represents a confluence of multiple B-lines. 3) White lung: Confluent echogenicity involving two or more rib interspaces. 4) Pleural irregularity: The pleural line is jagged or fragmented and may also appear thickened with small sub-centimeter subpleural consolidations. 5) Pleural effusion: Well-defined fluid above the diaphragm. In a simple transudative effusion, the fluid appears anechoic. In a complex exudative effusion, the fluid may have loculations, septations, and/or internal echogenic floating debris. 6) Consolidation: Poorly defined, tissue-like region within the lung, usually seen adjacent to the pleural line. 7) Lung necrosis or abscess: Well-defined, hypoechoic region within an area of consolidation. By understanding the US appearance of lung pathologies, LUS can be used to diagnose pediatric lung diseases in areas where CXRs are currently unavailable. Read More

Meeting name: SPR 2022 Annual Meeting & Postgraduate Course , 2022

Authors: Chang Hailey, Gill Christopher, Setty Bindu, Castro-aragon Ilse, Camelo Ingrid, Etter Lauren, Pieciak Rachel, Thompson Russell, Wang Kaihong, Li Jason

Keywords: Lung Ultrasound, RSV, Pneumonia

Point of Care Lung ultrasound has proven in multiple studies to be superior to CXR to diagnose pneumonia in children especially in limited resource settings. This non-radiating, portable and adaptable technique, brings an opportunity to detect pneumonia with higher accuracy than CXR. Ultrasound imaging interpretation is challenging. To deal with this complexity, we created a "brightness profiles" data reduction technique to identify specific anatomical structures identified by lung ultrasound using artificial intelligence. We use this technique to demonstrate how data reduction can help identify common anatomical landmarks and abnormal findings, and aid in the interpretation of ultrasound diagnosed pediatric pneumonia. Read More

Meeting name: SPR 2022 Annual Meeting & Postgraduate Course , 2022

Authors: Li Jason, Betke Margaret, Gill Christopher, Thompson Russell, Wang Kaihong, Etter Lauren, Camelo Ingrid, Chang Hailey, Setty Bindu, Castro Ilse, Pieciak Rachel

Keywords: Artificial intelligence, pneumonia, ultrasound

CXR is the most common imaging method to diagnose pneumonia in children in limited-resource settings. There is a need to simplify and expedite its interpretation. By using a machine learning model to first classify and interpret the pneumonia images and then incorporate those characteristic imaging findings patterns into a simulated mobile app, health care workers can use their mobile devices to interpret those findings based on preloaded images built into their mobile devices corresponding to pneumonia. Read More

Meeting name: SPR 2022 Annual Meeting & Postgraduate Course , 2022

Authors: Thompson Russell, Pieciak Rachel, Gill Christopher, Li Jason, Wang Kaihong, Etter Lauren, Camelo Ingrid, Castro-aragon Ilse, Setty Bindu, Chang Hailey, Betke Margaret

Keywords: Artificial intelligence, pneumonia, CXR