Use of Lung Ultrasound in Cystic Fibrosis: Is It a Valuable Tool?

Cystic fibrosis (CF) is a multisystem disorder characterized by progressive respiratory deterioration, significantly impacting both quality of life and survival. Over the years, lung ultrasound (LUS) has emerged as a promising tool in pediatric respiratory due to its safety profile and ease at the bedside. In the era of highly effective CF modulator therapies and improved life expectancy, the use of non-ionizing radiation techniques could become an integral part of CF management, particularly in the pediatric population.

Machine Learning Analysis in Diffusion Kurtosis Imaging for Discriminating Pediatric Posterior Fossa Tumors: A Repeatability and Accuracy Pilot Study.

: Differentiating pediatric posterior fossa (PF) tumors such as medulloblastoma (MB), ependymoma (EP), and pilocytic astrocytoma (PA) remains relevant, because of important treatment and prognostic implications. Diffusion kurtosis imaging (DKI) has not yet been investigated for discrimination of pediatric PF tumors. Estimating diffusion values from whole-tumor-based (VOI) segmentations may improve diffusion measurement repeatability compared to conventional region-of-interest (ROI) approaches.

M-mode ultrasound for assessment of the "tethered fat sign" in children: an easily performed way to certify a dynamic process as a still picture.

Background: Omental infarction is a rare pediatric disease. Ultrasound is a useful modality for a non-invasive pre-operative differential diagnosis between inflammatory conditions (as appendicitis) and omental infarction, especially by detecting immobility of the omentum adhered to the abdominal wall ("tetherd fat sign"). However, this is a dynamic sign that cannot be documented in a static image with B-mode technique.

Machine Learning Algorithm: Texture Analysis in CNO and Application in Distinguishing CNO and Bone Marrow Growth-Related Changes on Whole-Body MRI.

Objective: The purpose of this study is to analyze the texture characteristics of chronic non-bacterial osteomyelitis (CNO) bone lesions, identified as areas of altered signal intensity on short tau inversion recovery (STIR) sequences, and to distinguish them from bone marrow growth-related changes through Machine Learning (ML) and Deep Learning (DL) analysis.

Materials And Methods: We included a group of 66 patients with confirmed diagnosis of CNO and a group of 28 patients with suspected extra-skeletal systemic disease. All examinations were performed on a 1.