There is a lack of standardized approach and terminology to classify the diverse spectrum of manifestations in tuberculosis. It is important to recognize the different clinical and radiographic patterns to guide treatment. As a result of changing epidemiology, there is considerable overlap in the radiologic presentations of primary tuberculosis and post-primary tuberculosis. In this article we promote a standardized approach in clinical and radiographic classification for children suspected of having or diagnosed with childhood tuberculosis. We propose standardized terms to diminish confusion and miscommunication, which can affect management. In addition, we present pitfalls and limitations of imaging.
Tuberculosis in childhood is clinically challenging, but it is a preventable and treatable disease. Risk factors depend on age and immunity status. The most common form of pediatric tuberculosis is pulmonary disease, which comprises more than half of the cases. Other forms make up the extrapulmonary tuberculosis that involves infection of the lymph nodes, central nervous system, gastrointestinal system, hepatobiliary tree, and renal and musculoskeletal systems. Knowledge of the imaging characteristics of pediatric tuberculosis provides clues to diagnosis. This article aims to review the imaging characteristics of common sites for extrapulmonary tuberculous involvement in children.
Background: Ultrasound for developmental dysplasia of the hip (DDH) is challenging for nonexperts to perform and interpret. Recording "sweep" images allows more complete hip assessment, suitable for automation by artificial intelligence (AI), but reliability has not been established. We assessed agreement between readers of varying experience and a commercial AI algorithm, in DDH detection from infant hip ultrasound sweeps. Methods: We selected a full spectrum of poor-to-excellent quality images and normal to severe dysplasia, in 240 hips (120 single 2-dimensional images, 120 sweeps). For 12 readers (radiologists, sonographers, clinicians and researchers; 3 were DDH subspecialists), and a ultrasound-FDA-cleared AI software package (Medo Hip), we calculated interobserver reliability for alpha angle measurements by intraclass correlation coefficient (ICC 2,1 ) and for DDH classification by Randolph Kappa. Results: Alpha angle reliability was high for AI versus subspecialists (ICC = 0.87 for sweeps, 0.90 for single images). For DDH diagnosis from sweeps, agreement was high between subspecialists (kappa = 0.72), and moderate for nonsubspecialists (0.54) and AI (0.47). Agreement was higher for single images (kappa = 0.80, 0.66, 0.49). AI reliability deteriorated more than human readers for the poorest-quality images. The agreement of radiologists and clinicians with the accepted standard, while still high, was significantly poorer for sweeps than 2D images (P < 0.05). Conclusions: In a challenging exercise representing the wide spectrum of image quality and reader experience seen in realworld hip ultrasound, agreement on DDH diagnosis from easily obtained sweeps was only slightly lower than from single images, likely because of the additional step of selecting the best image. AI performed similarly to a nonsubspecialist human reader but was more affected by low-quality images.
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