Artificial Intelligence for Detecting Acute Fractures in Patients Admitted to an Emergency Department: Real-Life Performance of Three Commercial Algorithms.

Rationale And Objectives: Interpreting radiographs in emergency settings is stressful and a burden for radiologists. The main objective was to assess the performance of three commercially available artificial intelligence (AI) algorithms for detecting acute peripheral fractures on radiographs in daily emergency practice.

Materials And Methods: Radiographs were collected from consecutive patients admitted for skeletal trauma at our emergency department over a period of 2 months.

Application of artificial intelligence to imaging interpretations in the musculoskeletal area: Where are we? Where are we going?

The interest of researchers, clinicians and radiologists, in artificial intelligence (AI) continues to grow. Deep learning is a subset of machine learning, in which the computer algorithm itself can determine the optimal imaging features to answer a clinical question. Convolutional neural networks are the most common architecture for performing deep learning on medical images.

Artificial Intelligence for Classification of Soft-Tissue Masses at US.

Purpose: To train convolutional neural network (CNN) models to classify benign and malignant soft-tissue masses at US and to differentiate three commonly observed benign masses.

Materials And Methods: In this retrospective study, US images obtained between May 2010 and June 2019 from 419 patients (mean age, 52 years ± 18 [standard deviation]; 250 women) with histologic diagnosis confirmed at biopsy or surgical excision ( = 227) or masses that demonstrated imaging characteristics of lipoma, benign peripheral nerve sheath tumor, and vascular malformation ( = 192) were included. Images in patients with a histologic diagnosis ( = 227) were used to train and evaluate a CNN model to distinguish malignant and benign lesions.

Does Magnetic Resonance Imaging After Diagnostic Ultrasound for Soft Tissue Masses Change Clinical Management?

Objectives: To evaluate whether a follow-up magnetic resonance imaging (MRI) scan performed after initial ultrasound (US) to evaluate soft tissue mass (STM) lesions of the musculoskeletal system provides additional radiologic diagnostic information and alters clinical management.

Methods: A retrospective chart review was performed of patients undergoing initial US evaluations of STMs of the axial or appendicular skeleton between November 2012 and March 2019. Patients who underwent US examinations followed by MRI for the evaluation of STM lesions were identified.

CT-guided radiofrequency ablation for osteoid osteomas: a systematic review.

Objectives: CT-guided radiofrequency ablation (CT-RFA) is considered to be the gold standard for treatment of osteoid osteoma (OO) yet treatment failures (TFs) continue to be reported. This systematic review was conducted to evaluate factors associated with TF, such as ablation time, lesion location, and patient age as well as evaluating how TF has trended over time.

Methods: Original studies reporting on patients undergoing CT-RFA of OO published between 2002 and 2019 were identified.

Hepatic involvement in HELLP syndrome: an update with emphasis on imaging features.

HELLP syndrome, which consists of hemolysis, elevated liver enzymes, and low platelet count is an unusual complication of pregnancy that is observed in only 10% to 15% of women with preeclampsia. Hepatic involvement in HELLP syndrome may present with various imaging features depending on the specific condition that includes nonspecific abnormalities such as perihepatic free fluid, hepatic steatosis, liver enlargement, and periportal halo that may precede more severe conditions such as hepatic hematoma and hepatic rupture with hemoperitoneum. Maternal clinical symptoms may be nonspecific and easily mistaken for a variety of other conditions that should be recognized.