Approach to Pediatric Chest Radiograph

Jana, Manisha; Bhalla, Ashu Seith; Gupta, Arun Kumar

doi:10.1007/s12098-015-1980-3

Cited by 8 publications

(6 citation statements)

References 6 publications

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“…Thymic hypoplasia and aplasia indicate a small or absent thymus (Figure 8 ) seen in immune deficiencies[ 15 ]. These terms are most commonly used with DiGeorge syndrome.…”

Section: Small Thymusmentioning

confidence: 99%

Imaging of the pediatric thymus: Clinicoradiologic approach

Manchanda¹,

Bhalla²,

Jana³

et al. 2017

WJCP

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The thymus is a lymphatic organ that undergoes dynamic changes with age and disease. It is important to be familiar with these physiological changes in the thymus gland to be able to identify pathology and make an accurate diagnosis. The thymus may be involved in multisystem disorders or show focal isolated lesions. The aim of this article is to review the radiological anatomy of the thymus, normal variants, and pathology including hyperplasia and benign/malignant lesions involving the thymus gland in the pediatric age group. We also propose an algorithmic approach for imaging evaluation of a suspected thymic mass on the basis of morphologic features.

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“…Thymic hypoplasia and aplasia indicate a small or absent thymus (Figure 8 ) seen in immune deficiencies[ 15 ]. These terms are most commonly used with DiGeorge syndrome.…”

Section: Small Thymusmentioning

confidence: 99%

Imaging of the pediatric thymus: Clinicoradiologic approach

Manchanda¹,

Bhalla²,

Jana³

et al. 2017

WJCP

Self Cite

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“…Another challenge is the need for the integration of clinical data. For example, neonatal pneumonia and meconium aspiration syndrome can have similar radiographic observations, and only knowledge of a specific patient’s clinical data makes differentiation possible [ 89 ]. An additional concern is a fact that specific pediatric age groups have differing disease predispositions.…”

Section: Discussionmentioning

confidence: 99%

Pediatric chest radiograph interpretation: how far has artificial intelligence come? A systematic literature review

et al. 2022

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“…The pCXR diagnosis of pneumonia present was based on the standardized World Health Organization definitions and included several radiological patterns, such as endpoint consolidation (presence of a dense, fluffy parenchymal opacity that obscures the underlying vasculature and occupies a portion or whole of a lobe, that may or not contain air bronchograms), and patchy pneumonic infiltrates that are of insufficient magnitude to constitute consolidation but are routinely diagnosed as pneumonia and treated with antibiotics. 20,21 If changes on the pCXR were indeterminate for pneumonia versus atelectasis, these cases were classified as pneumonia and study participants were informed to classify these ambiguous cases as pneumonia. We did not exclude these cases because it is a common conundrum and important for maintaining ecological validity of our sample.…”

Section: Definitionsmentioning

confidence: 99%

Image interpretation: Learning analytics–informed education opportunities

Thau

Pérez

Pusic

et al. 2021

AEM Education and Training

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Objectives: Using a sample of pediatric chest radiographs (pCXR) taken to rule out pneumonia, we obtained diagnostic interpretations from physicians and used learning analytics to determine the radiographic variables and participant review processes that predicted for an incorrect diagnostic interpretation.Methods: This was a prospective cross-sectional study. A convenience sample of frontline physicians with a range of experience levels interpreted 200 pCXR presented using a customized online radiograph presentation platform. Participants were asked to determine absence or presence (with respective location) of pneumonia. The pCXR were categorized for specific image-based variables potentially associated with interpretation difficulty. We also generated heat maps displaying the locations of diagnostic error among normal pCXR. Finally, we compared image review processes in participants with higher versus lower levels of clinical experience. Results:We enrolled 83 participants (20 medical students, 40 postgraduate trainees, and 23 faculty) and obtained 12,178 case interpretations. Variables that predicted for increased pCXR interpretation difficulty were pneumonia versus no pneumonia (β = 8.7, 95% confidence interval [CI] = 7.4 to 10.0), low versus higher visibility of pneumonia (β = -2.2, 95% CI = -2.7 to -1.7), nonspecific lung pathology (β = 0.9, 95% CI = 0.40 to 1.5), localized versus multifocal pneumonia (β = -0.5, 95% CI = -0.8 to -0.1), and one versus two views (β = 0.9, 95% CI = 0.01 to 1.9). A review of diagnostic errors identified that bony structures, vessels in the perihilar region, peribronchial thickening, and thymus were often mistaken for pneumonia. Participants with lower experience were less accurate when they reviewed one of two available views (p < 0.0001), and accuracy of those with higher experience increased with increased confidence in their response (p < 0.0001). Conclusions:Using learning analytics, we identified actionable learning opportunities for pCXR interpretation, which can be used to allow for a customized weighting of which cases to practice. Furthermore, experienced-novice comparisons revealed image review processes that were associated with greater diagnostic accuracy, providing additional insight into skill development of image interpretation.

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Approach to Pediatric Chest Radiograph

Cited by 8 publications

References 6 publications

Imaging of the pediatric thymus: Clinicoradiologic approach

Imaging of the pediatric thymus: Clinicoradiologic approach

Pediatric chest radiograph interpretation: how far has artificial intelligence come? A systematic literature review

Image interpretation: Learning analytics–informed education opportunities

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