Image-based deep learning in diagnosing mycoplasma pneumonia on pediatric chest X-rays.

Background: Correctly diagnosing and accurately distinguishing mycoplasma pneumonia in children has consistently posed a challenge in clinical practice, as it can directly impact the prognosis of affected children. To address this issue, we analyzed chest X-rays (CXR) using various deep learning models to diagnose pediatric mycoplasma pneumonia.

Methods: We collected 578 cases of children with mycoplasma infection and 191 cases of children with virus infection, with available CXR sets.

Factors Interfering with Delineation on MRCP of Pancreaticobiliary Maljunction in Paediatric Patients.

Background: The aim of this study was to assess factors for delineating the pancreaticobiliary junction in the presence of pediatric congenital choledochal cysts (CCC) using Magnetic resonance cholangiopancreatography (MRCP).

Methods: Retrospective review of medical records for 48 patients with CCC was conducted, including demographics, biliary amylase and MRCP findings if available. With univariate and multivariate logistic regression, we measured significant factors affecting pancreaticobiliary maljunction(PBM) diagnoses by MRCP.