Radiology leaders share 5 pearls of wisdom for navigating burnout

Radiology leaders share 5 pearls of wisdom for navigating burnout

COVID-19 has “without a doubt” amplified burnout, according to Cheri Canon, MD, a radiologist at the University of Alabama at Birmingham. 

And while most rads were spared direct contact with COVID-19 patients, abrupt changes to everyday life outside of work have also contributed to burnout. But healthcare providers often come together during crises, and Canon says she is energized by seeing the remarkable care from providers around her.

Read more about ways that radiologists can manage burnout during these times: https://www.healthimaging.com/topics/practice-management/radiology-navigating-burnout

New deep learning method boosts MRI results without requiring new data

A team of researchers from Washington University in St. Louis has found a new deep learning method that can minimize artifacts and other noise in MRI images that come from movement and a short image-acquisition time.

Deep learning learns directly from the training data how to determine the signal from artifacts and noise, or variations in signal intensity in an image. Many existing deep learning-based MRI reconstruction methods are able to remove artifacts and noise but they learn from a ground truth reference, which can be difficult to obtain.

Read more: https://source.wustl.edu/2021/09/new-deep-learning-method-boosts-mri-results-without-requiring-new-data/

Deep learning model automates brain tumor classification

Recently, scientists from the US developed a model capable of classifying numerous intracranial tumour types without the need for a scalpel. The model, called a convolutional neural network (CNN), uses deep learning – a type of machine learning algorithm found in image recognition software – to recognize these tumours in MR images, based on hierarchical features such as location and morphology. The team’s CNN could accurately classify several brain cancers with no manual interaction.

This network is the first step toward developing an artificial intelligence-augmented radiology workflow that can support image interpretation by providing quantitative information and statistics

Read more:  https://physicsworld.com/a/deep-learning-model-automates-brain-tumour-classification/

Are X-rays after knee replacement unnecessary?

More than 99% of X-rays after total knee replacement surgery appear to be unnecessary, costing the healthcare system millions, according to a new analysis.

Gathering radiographs following this operation is common practice, but there is little data on the usefulness of these exams, experts wrote in The Knee. Wanting to better understand this practice, Brigham and Women’s scientists analyzed postoperative data across two level 1 trauma centers.

Their results were eye opening: Almost 100% of scans after total knee arthroplasty had no impact on clinical management after the procedure. Yet, these routine radiographs cost some $1 million while administering 22.92 mSV of radiation to patients who didn’t require such imaging.

Read more: https://www.radiologybusiness.com/topics/quality/x-rays-after-knee-replacement-unnecessary-value

Portable MRI device offers key data for stroke treatment

Yale University has found in a study that a portable magnetic resonance imaging (MRI) device could aid in detecting intracranial hemorrhages associated with stroke.

The Portable Point-of-Care MRI system provides potentially life-saving data, especially in areas or cases where access to advanced brain imaging scans is limited.

The team is currently examining the ability of portable MRI technologies in detecting and monitoring head trauma and brain tumors. They will also investigate the use of such devices in evaluating brain health in individuals with risk factors, including high blood pressure.

Read more: https://www.medicaldevice-network.com/news/hyperfines-mri-device-offers-key-data-for-stroke-treatment-study-finds/

Cancer radiation treatment at high-volume centers associated with improved survival

Treatment at high-volume radiation therapy facilities correlated with improved survival among select patients with common malignancies, according to study results published in Cancer.
Patients who receive radiation therapy in the definitive setting without surgery may benefit most from treatment at high-volume centers, researchers wrote.

The researchers called for further research into why patients treated at high-volume radiation centers have better outcomes.

Read more: https://www.healio.com/news/hematology-oncology/20210903/cancer-radiation-treatment-at-highvolume-centers-associated-with-improved-survival

The future of vascular imaging technology

Researchers from Kyoto University Hospital a describe the future of vascular imaging technology, including how photoacoustic imaging targets tumour vessels in breast cancer.

By visualizing the state of blood vessels within a disease, it may be possible to detect the disease at an early stage and monitor the effects of treatment.

Photoacoustic imaging may be a breakthrough technology to solve these issues. It enables us to depict fine vessels without radiation exposure and the use of contrast media. Furthermore, photoacoustic imaging is also capable of visualizing intravascular oxygen saturation in the 3D view as well. The photoacoustic imaging technology will contribute widely to the diagnosis of disease.

Read more: https://www.openaccessgovernment.org/the-future-of-vascular-imaging-technology/118836/

Emerging AI application in diagnostic imaging

AI is a boon to the medical and healthcare industry. Right from diagnostics to surgeries and medical equipment, artificial intelligence is supporting the healing processes of many human lives. 

There are several use cases for AI and automation in the medical device industry. Companies are using machine learning to monitor patients using sensors and automating medicine delivery via connected apps, integrating AI-driven platforms in medical scanning devices to improve the clarity of images and screening, and utilizing IoT to improve patient monitoring and clinical outcomes.

Read the full article: https://www.analyticsinsight.net/ai-in-medical-devices-these-are-the-emerging-industry-application/