Artificial intelligence-based rapid brain volumetry substantially improves differential diagnosis in dementia.

Introduction: This study evaluates the clinical value of a deep learning-based artificial intelligence (AI) system that performs rapid brain volumetry with automatic lobe segmentation and age- and sex-adjusted percentile comparisons.

Methods: Fifty-five patients-17 with Alzheimer's disease (AD), 18 with frontotemporal dementia (FTD), and 20 healthy controls-underwent cranial magnetic resonance imaging scans. Two board-certified neuroradiologists (BCNR), two board-certified radiologists (BCR), and three radiology residents (RR) assessed the scans twice: first without AI support and then with AI assistance.

Nonradiology Health Care Professionals Significantly Benefit From AI Assistance in Emergency-Related Chest Radiography Interpretation.

Background: Chest radiographs (CXRs) are still of crucial importance in primary diagnostics, but their interpretation poses difficulties at times.

Research Question: Can a convolutional neural network-based artificial intelligence (AI) system that interprets CXRs add value in an emergency unit setting?

Study Design And Methods: A total of 563 CXRs acquired in the emergency unit of a major university hospital were retrospectively assessed twice by three board-certified radiologists, three radiology residents, and three emergency unit-experienced nonradiology residents (NRRs). They used a two-step reading process: (1) without AI support; and (2) with AI support providing additional images with AI overlays.

Automated MRI Lung Segmentation and 3D Morphologic Features for Quantification of Neonatal Lung Disease.

Purpose: To analyze the performance of deep learning (DL) models for segmentation of the neonatal lung in MRI and investigate the use of automated MRI-based features for assessment of neonatal lung disease.

Materials And Methods: Quiet-breathing MRI was prospectively performed in two independent cohorts of preterm infants (median gestational age, 26.57 weeks; IQR, 25.

Pelvic inlet area is associated with birth mode.

Introduction: To determine whether a pelvis is wide enough for spontaneous delivery has long been the subject of obstetric research. A number of variables have been proposed as predictors, all with limited accuracy. In this study, we use a novel three-dimensional (3D) method to measure the female pelvis and assess which pelvic features influence birth mode.

Clinically focused multi-cohort benchmarking as a tool for external validation of artificial intelligence algorithm performance in basic chest radiography analysis.

Artificial intelligence (AI) algorithms evaluating [supine] chest radiographs ([S]CXRs) have remarkably increased in number recently. Since training and validation are often performed on subsets of the same overall dataset, external validation is mandatory to reproduce results and reveal potential training errors. We applied a multicohort benchmarking to the publicly accessible (S)CXR analyzing AI algorithm CheXNet, comprising three clinically relevant study cohorts which differ in patient positioning ([S]CXRs), the applied reference standards (CT-/[S]CXR-based) and the possibility to also compare algorithm classification with different medical experts' reading performance.

Immuno-histochemical correlation of fibrosis-related markers with the desmoplastic reaction of the mesentery in small intestine neuroendocrine neoplasms.

Introduction: Small intestine neuroendocrine neoplasms (siNENs) will attain more importance due to their increasing incidence. Moreover, siNENs might lead to a desmoplastic reaction (DR) of the mesentery causing severe complications and deteriorating prognosis. The expression of fibrosis-related proteins appears to be the key mechanisms for the development of this desmoplastic reaction.

The role of vagal innervation on the early development of postoperative ileus in mice.

Background: Postoperative ileus (POI) involves an intestinal inflammatory response that is modulated by afferent and efferent vagal activation. We aimed to identify the potential influence of the vagus nerve on POI by tracking central vagal activation and its role for peripheral inflammatory changes during the early hours after surgery.

Methods: C57BL6 mice were vagotomized (V) 3-4 days prior to experiments, while control animals received sham vagotomy (SV).

Interpretation of Thoracic Radiography Shows Large Discrepancies Depending on the Qualification of the Physician-Quantitative Evaluation of Interobserver Agreement in a Representative Emergency Department Scenario.

(1) Background: Chest radiography (CXR) is still a key diagnostic component in the emergency department (ED). Correct interpretation is essential since some pathologies require urgent treatment. This study quantifies potential discrepancies in CXR analysis between radiologists and non-radiology physicians in training with ED experience.

Artificial Intelligence in Chest Radiography Reporting Accuracy: Added Clinical Value in the Emergency Unit Setting Without 24/7 Radiology Coverage.

Objectives: Chest radiographs (CXRs) are commonly performed in emergency units (EUs), but the interpretation requires radiology experience. We developed an artificial intelligence (AI) system (precommercial) that aims to mimic board-certified radiologists' (BCRs') performance and can therefore support non-radiology residents (NRRs) in clinical settings lacking 24/7 radiology coverage. We validated by quantifying the clinical value of our AI system for radiology residents (RRs) and EU-experienced NRRs in a clinically representative EU setting.