Radiology

A stepwise strategy integrating dynamic stress CT myocardial perfusion and deep learning-based FFR in the work-up of stable coronary artery disease.

OBJECTIVES: To validate a novel stepwise strategy in which computed tomography-derived fractional flow reserve (FFR) is restricted to intermediate stenosis on coronary computed tomography angiography (CCTA) and computed tomography myocardial perfusion imaging (CT-MPI) was reserved for vessels with gray zone FFR values.

Impact of deep learning-based multiorgan segmentation methods on patient-specific internal dosimetry in PET/CT imaging: A comparative study.

PURPOSE: Accurate and fast multiorgan segmentation is essential in image-based internal dosimetry in nuclear medicine. While conventional manual PET image segmentation is widely used, it suffers from both being time-consuming as well as subject to human error. This study exploited 2D and 3D deep learning (DL) models. Key organs in the trunk of the body were segmented and then used as a reference f...

Analysis of neural networks for routine classification of sixteen ultrasound upper abdominal cross sections.

PURPOSE: Abdominal ultrasound screening requires the capture of multiple standardized plane views as per clinical guidelines. Currently, the extent of...

Beyond the Echo: The Evolution and Revolution of Ultrasound in Anesthesia.

This article explores the evolving role of ultrasound technology in anesthesia. Ultrasound emerged decades ago, offering clinicians noninvasive, econo...

Deep learning-based cerebral aneurysm segmentation and morphological analysis with three-dimensional rotational angiography.

BACKGROUND: The morphological assessment of cerebral aneurysms based on cerebral angiography is an essential step when planning strategy and device se...

Comparing deep learning and handcrafted radiomics to predict chemoradiotherapy response for locally advanced cervical cancer using pretreatment MRI.

Concurrent chemoradiotherapy (CRT) is the standard treatment for locally advanced cervical cancer (LACC), but its responsiveness varies among patients...

MRI-Based Kinetic Heterogeneity Evaluation in the Accurate Access of Axillary Lymph Node Status in Breast Cancer Using a Hybrid CNN-RNN Model.

BACKGROUND: Accurate evaluation of the axillary lymph node (ALN) status is needed for determining the treatment protocol for breast cancer (BC). The v...

Diagnostic accuracy of an artificial intelligence algorithm versus radiologists for fracture detection on cervical spine CT.

OBJECTIVES: To compare diagnostic accuracy of a deep learning artificial intelligence (AI) for cervical spine (C-spine) fracture detection on CT to at...

Artificial Intelligence for Breast Cancer Detection on Mammography: Factors Related to Cancer Detection.

RATIONALE AND OBJECTIVES: Little is known about the factors affecting the Artificial Intelligence (AI) software performance on mammography for breast ...

A Deep-Learning Model for Diagnosing Fresh Vertebral Fractures on Magnetic Resonance Images.

BACKGROUND: The accurate diagnosis of fresh vertebral fractures (VFs) was critical to optimizing treatment outcomes. Existing studies, however, demons...

Artificial intelligence-based speckle featurization and localization for ultrasound speckle tracking velocimetry.

Deep learning-based super-resolution ultrasound (DL-SRU) framework has been successful in improving spatial resolution and measuring the velocity fiel...

Comparative effectiveness of standard vs. AI-assisted PET/CT reading workflow for pre-treatment lymphoma staging: a multi-institutional reader study evaluation.

BACKGROUND: Fluorine-18 fluorodeoxyglucose (FDG)-positron emission tomography/computed tomography (PET/CT) is widely used for staging high-grade lymph...

Inter-fractional portability of deep learning models for lung target tracking on cine imaging acquired in MRI-guided radiotherapy.

MRI-guided radiotherapy systems enable beam gating by tracking the target on planar, two-dimensional cine images acquired during treatment. This study...

Enhancing paranasal sinus disease detection with AutoML: efficient AI development and evaluation via magnetic resonance imaging.

PURPOSE: Artificial intelligence (AI) in the form of automated machine learning (AutoML) offers a new potential breakthrough to overcome the barrier o...

A perspective on the evolution of semi-quantitative MRI assessment of osteoarthritis: Past, present and future.

OBJECTIVE: This perspective describes the evolution of semi-quantitative (SQ) magnetic resonance imaging (MRI) in characterizing structural tissue pat...

Strategies for deep learning-based attenuation and scatter correction of brain F-FDG PET images in the image domain.

BACKGROUND: Attenuation and scatter correction is crucial for quantitative positron emission tomography (PET) imaging. Direct attenuation correction (...

Automated digital templating of component sizing is accurate in robotic total hip arthroplasty when compared to predicate software.

Accurate pre-operative templating of prosthesis components is an essential factor in successful total hip arthroplasty (THA), including robotically-as...

Approaching expert-level accuracy for differentiating ACL tear types on MRI with deep learning.

Treatment for anterior cruciate ligament (ACL) tears depends on the condition of the ligament. We aimed to identify different tear statuses from preop...

Deep learning based automated left ventricle segmentation and flow quantification in 4D flow cardiac MRI.

BACKGROUND: 4D flow MRI enables assessment of cardiac function and intra-cardiac blood flow dynamics from a single acquisition. However, due to the po...

Fully automated artificial intelligence-based coronary CT angiography image processing: efficiency, diagnostic capability, and risk stratification.

OBJECTIVES: To prospectively investigate whether fully automated artificial intelligence (FAAI)-based coronary CT angiography (CCTA) image processing ...