Indications for chest X-rays in children and how to obtain and interpret them

Morgan, Hannah; Pettet, Garry; Reed, Michele; Paul, Siba Prosad

doi:10.7748/ncyp.2018.e1141

Cited by 8 publications

(2 citation statements)

References 17 publications

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“…All CXRs obtained in ITIP were supine anteroposterior images as this is the standard approach for imaging children at KCH. Supine CXR images can limit the detection of small pleural effusions, and while erect posterioanterior CXR images are preferred, they are not commonly obtained in young children due to challenges with compliance [ 40 ]. In addition, the absence of pleural effusions on CXR in our study may also be due to the changing epidemiology of pneumonia post-pneumococcal and Hib conjugate vaccine introduction [ 10 ].…”

Section: Discussionmentioning

confidence: 99%

Chest radiography in children aged 2–59 months enrolled in the Innovative Treatments in Pneumonia (ITIP) project in Lilongwe Malawi: a secondary analysis

et al. 2022

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Background Pneumonia is the leading infectious cause of death in children aged under 5 years in low- and middle-income countries (LMICs). World Health Organization (WHO) pneumonia diagnosis guidelines rely on non-specific clinical features. We explore chest radiography (CXR) findings among select children in the Innovative Treatments in Pneumonia (ITIP) project in Malawi in relation to clinical outcomes. Methods When clinically indicated, CXRs were obtained from ITIP-enrolled children aged 2 to 59 months with community-acquired pneumonia hospitalized with treatment failure or relapse. ITIP1 (fast-breathing pneumonia) and ITIP2 (chest-indrawing pneumonia) trials enrolled children with non-severe pneumonia while ITIP3 enrolled children excluded from ITIP1 and ITIP2 with severe pneumonia and/or selected comorbidities. A panel of trained pediatricians classified the CXRs using the standardized WHO CXR research methodology. We analyzed the relationship between CXR classifications, enrollee characteristics, and outcomes. Results Between March 2016 and June 2018, of 114 CXRs obtained, 83 met analysis criteria with 62.7% (52/83) classified as having significant pathology per WHO standardized interpretation. ITIP3 (92.3%; 12/13) children had a higher proportion of CXRs with significant pathology compared to ITIP1 (57.1%, 12/21) and ITIP2 (57.1%, 28/49) (p-value = 0.008). The predominant pathological CXR reading was “other infiltrates only” in ITIP1 (83.3%, 10/12) and ITIP2 (71.4%, 20/28), while in ITIP3 it was “primary endpoint pneumonia”(66.7%, 8/12,; p-value = 0.008). The percent of CXRs with significant pathology among children clinically cured (60.6%, 40/66) vs those not clinically cured (70.6%, 12/17) at Day 14 was not significantly different (p-value = 0.58). Conclusions In this secondary analysis we observed that ITIP3 children with severe pneumonia and/or selected comorbidities had a higher frequency of CXRs with significant pathology, although these radiographic findings had limited relationship to Day 14 outcomes. The proportion of CXRs with “primary endpoint pneumonia” was low. These findings add to existing data that additional diagnostics and prognostics are important for improving the care of children with pneumonia in LMICs. Trial registration ITIP1, ITIP2, and ITIP3 were registered with ClinicalTrials.gov (NCT02760420, NCT02678195, and NCT02960919, respectively).

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Section: Discussionmentioning

confidence: 99%

Chest radiography in children aged 2–59 months enrolled in the Innovative Treatments in Pneumonia (ITIP) project in Lilongwe Malawi: a secondary analysis

et al. 2022

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“…One of the reasons that make radiological image evaluation difficult is artifacts. These are image fragments that are not anatomical structures caused by hardware defects, including software error, operator error, or foreign bodies (e.g., ICDs [ 8 ], ECG leads, jewelry [ 9 ], endotracheal tubes, cardiac clips [ 10 ], coins [ 11 ], or silicone breast implants [ 12 ]) inside or outside the patient’s body, as well as patient movement during the examination (motion artifacts). A separate radiographic imaging method should be chosen for each foreign body, depending on the material and its depth and location in the body.…”

Section: Introductionmentioning

confidence: 99%

Chest X-ray Foreign Objects Detection Using Artificial Intelligence

Kufel,

Bargieł-Łączek,

Koźlik

et al. 2023

JCM

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Diagnostic imaging has become an integral part of the healthcare system. In recent years, scientists around the world have been working on artificial intelligence-based tools that help in achieving better and faster diagnoses. Their accuracy is crucial for successful treatment, especially for imaging diagnostics. This study used a deep convolutional neural network to detect four categories of objects on digital chest X-ray images. The data were obtained from the publicly available National Institutes of Health (NIH) Chest X-ray (CXR) Dataset. In total, 112,120 CXRs from 30,805 patients were manually checked for foreign objects: vascular port, shoulder endoprosthesis, necklace, and implantable cardioverter-defibrillator (ICD). Then, they were annotated with the use of a computer program, and the necessary image preprocessing was performed, such as resizing, normalization, and cropping. The object detection model was trained using the You Only Look Once v8 architecture and the Ultralytics framework. The results showed not only that the obtained average precision of foreign object detection on the CXR was 0.815 but also that the model can be useful in detecting foreign objects on the CXR images. Models of this type may be used as a tool for specialists, in particular, with the growing popularity of radiology comes an increasing workload. We are optimistic that it could accelerate and facilitate the work to provide a faster diagnosis.

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Clinical Rotation Tips/Practical Tips for Ordering Imaging Tests: Plain X-Ray, Ultrasound, CT, MRI

Patella¹

2021

The Radiology Survival Kit

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Indications for chest X-rays in children and how to obtain and interpret them

Cited by 8 publications

References 17 publications

Chest radiography in children aged 2–59 months enrolled in the Innovative Treatments in Pneumonia (ITIP) project in Lilongwe Malawi: a secondary analysis

Chest radiography in children aged 2–59 months enrolled in the Innovative Treatments in Pneumonia (ITIP) project in Lilongwe Malawi: a secondary analysis

Chest X-ray Foreign Objects Detection Using Artificial Intelligence

Clinical Rotation Tips/Practical Tips for Ordering Imaging Tests: Plain X-Ray, Ultrasound, CT, MRI

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