Erik Ridley

Erik is senior editor at AuntMinnie.com and focuses on coverage of artificial intelligence and imaging informatics. He joined the website in 2000 and has 24 years of radiology journalism experience, including previous stints with Diagnostic Imaging magazine, Diagnostic Imaging Scan newsletter, and PACS and Networking News. Erik holds a bachelor's degree in journalism from the University of Connecticut.

Articles by this author
AI can enhance evaluation of cancer response over time
September 18, 2020 -- Radiology reports created with help from artificial intelligence (AI) are more accurate and faster to produce than current reporting methods in evaluating the response of advanced cancer to treatment over time, according to research presented at the recent Conference on Machine Intelligence in Medical Imaging.  Discuss
C-MIMI: AI peer review can spot missed lung cancer
September 15, 2020 -- A peer review process that's driven by artificial intelligence (AI) can identify lung nodules that were overlooked by radiologists on CT exams, potentially enabling early detection of cancers that would have otherwise been missed, according to a Monday talk at the Conference on Machine Intelligence in Medical Imaging (C-MIMI).  Discuss
C-MIMI: Use of AI in radiology is evolving
September 14, 2020 -- The use of artificial intelligence (AI) in radiology to aid in image interpretation tasks is evolving, but many of the old factors and concepts from the computer-aided detection era still remain, according to a Sunday talk at the Conference on Machine Intelligence in Medical Imaging (C-MIMI).  Discuss
Does experience matter in chest x-rays for COVID-19?
September 14, 2020 -- A group of Italian researchers found the performance of radiologists for detecting COVID-19 pneumonia on chest x-rays grew over time as they gained experience -- a finding that even applied to those with less experience reading x-rays. They shared their results online September 10 in the European Journal of Radiology.  Discuss
CT radiomics predicts esophageal cancer outcomes
September 11, 2020 -- Machine-learning models that assess both peritumoral and intratumoral radiomics features on pretreatment CT images can yield a promising level of performance for predicting treatment outcomes in patients with esophageal squamous cell carcinoma, according to research published online September 10 in JAMA Network Open.  Discuss
3D printing enhances planning of TAVR procedures
September 8, 2020 -- 3D-printed soft aortic root models with integrated electronic sensor arrays can improve the planning of transcatheter aortic valve replacement (TAVR) procedures in patients with aortic stenosis, potentially decreasing the risk of complications, according to research published online August 28 in Science Advances.  Discuss
Can AI diagnose heart failure on chest x-rays?
September 8, 2020 -- An artificial intelligence (AI) algorithm can provide a promising level of accuracy for diagnosing heart failure on chest radiographs, according to a presentation from Japanese researchers at the recent European Society of Cardiology virtual congress.  Discuss
AI can spot smokers at high risk of cancer on x-rays
September 4, 2020 -- By analyzing chest radiographs and a patient's electronic medical record (EMR), a deep-learning algorithm was able to identify more high-risk smokers who could benefit from CT lung cancer screening than current Medicare eligibility criteria, according to research published September 1 in the Annals of Internal Medicine.  Discuss
Virtual imaging trials could optimize COVID-19 imaging
September 2, 2020 -- Researchers have created computerized models of lung abnormalities on CT scans of patients with COVID-19, according to an August 28 study in the American Journal of Roentgenology. The virtual imaging models can be used like computerized phantoms for research into the use of imaging to detect and monitor the disease.  Discuss
Radiologists, AI make a great team in screening mammo
August 27, 2020 -- The combination of an artificial intelligence (AI)-based computer-aided detection algorithm with radiologist interpretation can detect more cases of breast cancer on screening mammograms than double reading by radiologists, according to research published online August 27 in JAMA: Oncology.  Discuss