Application of deep learning image reconstruction algorithm to improve image quality in CT angiography of children with Takayasu arteritis

Real-World Biomarkers for Pediatric Takayasu Arteritis

Childhood-onset Takayasu arteritis (TA) is a rare, heterogeneous disease with limited diagnostic markers. Our objective was to identify and classify all candidates for biomarkers of TA diagnosis in children reported in the literature. A systematic literature review (PRISMA) of MEDLINE, EMBASE, Wiley Cochrane Library, ClinicalTrias.gov, and WHO ICTRP for articles related to TA in the pediatric age group between January 2000 and August 2023 was performed. Data on demographics, clinical features, laboratory measurements, diagnostic imaging, and genetic analysis were extracted. We identified 2026 potential articles, of which 52 studies (81% case series) met inclusion criteria. A total of 1067 TA patients were included with a peak onset between 10 and 15 years. Childhood-onset TA predominantly presented with cardiovascular, constitutional, and neurological symptoms. Laboratory parameters exhibited a low sensitivity and specificity. Imaging predominantly revealed involvement of the abdominal aorta and renal arteries, with magnetic resonance angiography (MRA) being the preferred imaging modality. Our review confirms the heterogeneous presentation of childhood-onset TA, posing significant challenges to recognition and timely diagnosis. Collaborative, multinational efforts are essential to better understand the natural course of childhood-onset TA and to identify accurate biomarkers to enhance diagnosis and disease management, ultimately improving patient outcomes.

Artificial Intelligence for Radiation Dose Optimization in Pediatric Radiology: A Systematic Review

Radiation dose optimization is particularly important in pediatric radiology, as children are more susceptible to potential harmful effects of ionizing radiation. However, only one narrative review about artificial intelligence (AI) for dose optimization in pediatric computed tomography (CT) has been published yet. The purpose of this systematic review is to answer the question “What are the AI techniques and architectures introduced in pediatric radiology for dose optimization, their specific application areas, and performances?” Literature search with use of electronic databases was conducted on 3 June 2022. Sixteen articles that met selection criteria were included. The included studies showed deep convolutional neural network (CNN) was the most common AI technique and architecture used for dose optimization in pediatric radiology. All but three included studies evaluated AI performance in dose optimization of abdomen, chest, head, neck, and pelvis CT; CT angiography; and dual-energy CT through deep learning image reconstruction. Most studies demonstrated that AI could reduce radiation dose by 36–70% without losing diagnostic information. Despite the dominance of commercially available AI models based on deep CNN with promising outcomes, homegrown models could provide comparable performances. Future exploration of AI value for dose optimization in pediatric radiology is necessary due to small sample sizes and narrow scopes (only three modalities, CT, positron emission tomography/magnetic resonance imaging and mobile radiography, and not all examination types covered) of existing studies.