Possible Bias in Supervised Deep Learning Algorithms for CT Lung Nodule Detection and Classification.

Artificial Intelligence (AI) algorithms for automatic lung nodule detection and classification can assist radiologists in their daily routine of chest CT evaluation. Even though many AI algorithms for these tasks have already been developed, their implementation in the clinical workflow is still largely lacking. Apart from the significant number of false-positive findings, one of the reasons for that is the bias that these algorithms may contain.

Facilitating standardized COVID-19 suspicion prediction based on computed tomography radiomics in a multi-demographic setting.

Objective: To develop an automatic COVID-19 Reporting and Data System (CO-RADS)-based classification in a multi-demographic setting.

Methods: This multi-institutional review boards-approved retrospective study included 2720 chest CT scans (mean age, 58 years [range 18-100 years]) from Italian and Russian patients. Three board-certified radiologists from three countries assessed randomly selected subcohorts from each population and provided CO-RADS-based annotations.

AI-Driven Model for Automatic Emphysema Detection in Low-Dose Computed Tomography Using Disease-Specific Augmentation.

The objective of this study is to evaluate the feasibility of a disease-specific deep learning (DL) model based on minimum intensity projection (minIP) for automated emphysema detection in low-dose computed tomography (LDCT) scans. LDCT scans of 240 individuals from a population-based cohort in the Netherlands (ImaLife study, mean age ± SD = 57 ± 6 years) were retrospectively chosen for training and internal validation of the DL model. For independent testing, LDCT scans of 125 individuals from a lung cancer screening cohort in the USA (NLST study, mean age ± SD = 64 ± 5 years) were used.

In-room ultrasound fusion combined with fully compatible 3D-printed holding arm - rethinking interventional MRI.

There is no real need to discuss the potential advantages - mainly the excellent soft tissue contrast, nonionizing radiation, flow, and molecular information - of magnetic resonance imaging (MRI) as an intraoperative diagnosis and therapy system particularly for neurological applications and oncological therapies. Difficult patient access in conventional horizontal-field superconductive magnets, very high investment and operational expenses, and the need for special nonferromagnetic therapy tools have however prevented the widespread use of MRI as imaging and guidance tool for therapy purposes. The interventional use of MRI systems follows for the last 20+ years the strategy to use standard diagnostic systems and add more or less complicated and expensive components (eg, MRI-compatible robotic systems, specially shielded in-room monitors, dedicated tools and devices made from low-susceptibility materials, etc) to overcome the difficulties in the therapy process.

Advanced inside-out tracking approach for real-time combination of MRI and US images in the radio-frequency shielded room using combination markers.

For the real-time fusion of different modalities, a variety of tracking methods are available including the optical, electromagnetic (EM) and image-based tracking. But as a drawback optical tracking suffers from line of sight issues and EM tracking requires the manual referencing for the fusion procedure and is not usable in Magnetic Resonance Imaging (MRI) environment. To avoid these issues, we propose a real-time setup containing a camera capable of inside-Out tracking using combined circular markers attached to Ultrasound (US) probe and a suitable platform for automatic overlay of MRI and US image using markers.