European recommendations on practices in pediatric neuroradiology: consensus document from the European Society of Neuroradiology (ESNR), European Society of Paediatric Radiology (ESPR) and European Union of Medical Specialists Division of Neuroradiology (UEMS)

Pediatric neuroradiology is a subspecialty within radiology, with possible pathways to train within the discipline from neuroradiology or pediatric radiology. Formalized pediatric neuroradiology training programs are not available in most European countries. We aimed to construct a European consensus document providing recommendations for the safe practice of pediatric neuroradiology. We particularly emphasize imaging techniques that should be available, optimal site conditions and facilities, recommended team requirements and specific indications and protocol modifications for each imaging modality employed for pediatric neuroradiology studies. The present document serves as guidance to the optimal setup and organization for carrying out pediatric neuroradiology diagnostic and interventional procedures. Clinical activities should always be carried out in full agreement with national provisions and regulations. Continued education of all parties involved is a requisite for preserving pediatric neuroradiology practice at a high level

Artificial intelligence-based detection of paediatric appendicular skeletal fractures: performance and limitations for common fracture types and locations

BACKGROUND Research into artificial intelligence (AI)-based fracture detection in children is scarce and has disregarded the detection of indirect fracture signs and dislocations. OBJECTIVE To assess the diagnostic accuracy of an existing AI-tool for the detection of fractures, indirect fracture signs, and dislocations. MATERIALS AND METHODS An AI software, BoneView (Gleamer, Paris, France), was assessed for diagnostic accuracy of fracture detection using paediatric radiology consensus diagnoses as reference. Radiographs from a single emergency department were enrolled retrospectively going back from December 2021, limited to 1,000 radiographs per body part. Enrolment criteria were as follows: suspected fractures of the forearm, lower leg, or elbow; age 0-18 years; and radiographs in at least two projections. RESULTS Lower leg radiographs showed 607 fractures. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were high (87.5%, 87.5%, 98.3%, 98.3%, respectively). Detection rate was low for toddler's fractures, trampoline fractures, and proximal tibial Salter-Harris-II fractures. Forearm radiographs showed 1,137 fractures. Sensitivity, specificity, PPV, and NPV were high (92.9%, 98.1%, 98.4%, 91.7%, respectively). Radial and ulnar bowing fractures were not reliably detected (one out of 11 radial bowing fractures and zero out of seven ulnar bowing fractures were correctly detected). Detection rate was low for styloid process avulsions, proximal radial buckle, and complete olecranon fractures. Elbow radiographs showed 517 fractures. Sensitivity and NPV were moderate (80.5%, 84.7%, respectively). Specificity and PPV were high (94.9%, 93.3%, respectively). For joint effusion, sensitivity, specificity, PPV, and NPV were moderate (85.1%, 85.7%, 89.5%, 80%, respectively). For elbow dislocations, sensitivity and PPV were low (65.8%, 50%, respectively). Specificity and NPV were high (97.7%, 98.8%, respectively). CONCLUSIONS The diagnostic performance of BoneView is promising for forearm and lower leg fractures. However, improvement is mandatory before clinicians can rely solely on AI-based paediatric fracture detection using this software

Associations between age and gray matter volume in anatomical brain networks in middle‐aged to older adults

Aging is associated with cognitive decline, diminished brain function, regional brain atrophy, and disrupted structural and functional brain connectivity. Understanding brain networks in aging is essential, as brain function depends on large-scale distributed networks. Little is known of structural covariance networks to study inter-regional gray matter anatomical associations in aging. Here, we investigate anatomical brain networks based on structural covariance of gray matter volume among 370 middle-aged to older adults of 45–85 years. For each of 370 subjects, we acquired a T1-weighted anatomical MRI scan. After segmentation of structural MRI scans, nine anatomical networks were defined based on structural covariance of gray matter volume among subjects. We analyzed associations between age and gray matter volume in anatomical networks using linear regression analyses. Age was negatively associated with gray matter volume in four anatomical networks (P < 0.001, corrected): a subcortical network, sensorimotor network, posterior cingulate network, and an anterior cingulate network. Age was not significantly associated with gray matter volume in five networks: temporal network, auditory network, and three cerebellar networks. These results were independent of gender and white matter hyperintensities. Gray matter volume decreases with age in networks containing subcortical structures, sensorimotor structures, posterior, and anterior cingulate cortices. Gray matter volume in temporal, auditory, and cerebellar networks remains relatively unaffected with advancing age

Parameters of glucose metabolism and the aging brain:a magnetization transfer imaging study of brain macro- and micro-structure in older adults without diabetes

Pathophysiology, epidemiology and therapy of agein

Association of MTI parameters of subcortical gray matter structures with chronological age.

<p>Values represent standardized Betas. P-values (p) are adjusted for sex and affiliation to the offspring or control group.</p><p>MTR, magnetization transfer ratio</p><p>Association of MTI parameters of subcortical gray matter structures with chronological age.</p

Characteristics of the study population.

<p>SD, standard deviation</p><p>Characteristics of the study population.</p

Voxel-based assessment of age-related changes of white matter magnetization transfer ratio (MTR).

<p>Fig. 1 shows results from the voxel-based assessment of age-related changes of white matter magnetization transfer ratio (MTR) in the whole study population using FSL-TBSS. Results are projected on the mean fractional anisotropy (FA) image of the whole study population which is derived from a diffusion tensor imaging (DTI) scan sequence. The mean white matter skeleton of the whole study population is shown in green color. Red color shows areas of statistically significant decrease of white matter MTR with increasing chronological age (p < 0.05).</p

Association of MTI parameters of cortical gray matter and white matter with chronological age.

<p>Values represent standardized Betas. P-values (p) are adjusted for sex and affiliation to the offspring or control group.</p><p>MTR, magnetization transfer ratio</p><p>Association of MTI parameters of cortical gray matter and white matter with chronological age.</p