Jeff Hall

Offering access to over 110 AI applications, the enterprise imaging platform enables radiologists to test, deploy and monitor the use of AI technologies.

Additional carcinoma in the ipsilateral breast was detected on preoperative MRI exams in 24 out of 102 women prior to lumpectomy and mastectomy procedures, according to new study findings presented at the 2024 American Society of Clinical Oncology (ASCO) Annual Meeting in Chicago.

The AI-powered Heuron ICH software reportedly has an 86 percent sensitivity rate for diagnosing intracranial hemorrhage on computed tomography (CT) scans.

An AI model that includes extracted radiomic features from CT scans more than doubled the sensitivity rate for preoperative prediction of lung cancer recurrence in comparison to traditional TNM staging, according to study findings to be presented at the 2024 American Society of Clinical Oncology (ASCO) Annual Meeting in Chicago.

In comparison to pure solid nodules in patients with non-small cell lung cancer (NSCLC), nodules with a minor ground glass opacity component were associated with over a 38 percent higher rate of recurrence-free survival.

In addition to detecting missed lung nodules on X-rays, the AI-powered Qure.ai lung cancer continuum platform reportedly automates lung nodule measurement on CT scans and facilitates multimodality reporting.

The second-generation version of the VUZE System reportedly offers expanded functionality and incorporation of varied sources of 3D imaging data, including cone-beam CT scans obtained in the OR.

One deep learning model had a 72.4 percent accuracy rate for differentiating between benign and malignant solid pulmonary nodules on non-contrast CT while another deep learning model demonstrated an 87.1 percent AUC for differentiating benign and inflammatory findings.

Developed by an extended prostate cancer working group of the European Society of Urogenital Radiology (ESUR), the updated PI-QUAL scoring system emphasizes a simplified scale of image parameters that applies to prostate MRI scans with and without intravenous contrast use.

Emphasizing restriction spectrum imaging (RSI), the recently launched prostate MRI software OnQ Prostate may enhance PI-RADS assessments and workflow efficiency.

PSMA PET/CT demonstrated an 83 percent pooled detection rate for primary or metastatic renal cell carcinoma (RCC), and an 87 percent detection rate for restaging of metastatic or recurrent RCC, according to a nine-study meta-analysis.

For patients with no history of coronary artery disease (CAD), new research shows the use of CT-derived fractional flow reserve (FFR-CT) guided revascularizations after elective vascular surgery reduced myocardial infarction and all-cause death by 20 percent in comparison to standard care.

Image quality, sharpness, and contrast with AI-based denoising were significantly enhanced for neck CT in comparison to conventional CT image reconstruction at 100 percent and 50 percent mAs, according to newly published research.

In a study of over 2,000 women with dense breasts and average breast cancer risk, abbreviated MRI (AB-MR) demonstrated an 18.9 per 1000 cancer detection rate (CDR) in baseline exams, and all cancers detected with baseline or subsequent AB/MR exams were stage 0 or 1.

New prospective research examining the utility of 18F-PSMA-1007 PET/CT revealed comparable sensitivity to mpMRI for detecting clinically significant prostate cancer and a 17 percent higher specificity rate.

Researchers also noted that mammography-based AI software was associated with over a threefold higher likelihood of false-positive risk scores in patients 61 to 70 years of age in comparison to women 51 to 60 years of age.

Incorporating dynamic contrast-enhanced MRI, a deep learning model demonstrated a 20 percent higher AUC in external validation testing than clinical factors alone and over a 17 percent higher AUC than radiological factors alone in predicting proliferative hepatocellular carcinoma (HCC).

In recognition of National Women’s Health Month, Dana Bonaminio, MD, Amy Patel, MD, and Stacy Smith-Foley, MD, shared their thoughts and perspectives on the recently updated breast cancer screening recommendations from the United States Preventive Services Task Force (USPSTF).

Reportedly trained on thousands of computed tomography scans, the e-Lung software utilizes machine learning to detect and assess the progression of features associated with interstitial lung diseases.

The package of point-of-care ultrasound (POCUS) applications can reportedly facilitate rapid implementation of POCUS into imaging workflows.

The second version of the device reportedly detects T2/T3 fetal ultrasound views with 93.7 percent sensitivity and enables 63 percent of AIUM exam protocols without manual annotation.

Combining clinical and CT features, adjunctive use of a classification and regression tree (CART) diagnostic model demonstrated AUCs for detecting clear cell renal cell carcinoma (ccRCC) that were 15 to 22 percent higher than unassisted radiologist assessments.

An emerging deep learning algorithm had a lower AUC and sensitivity than urological radiologists for differentiating between small renal masses on computed tomography (CT) scans but had a 21 percent higher sensitivity rate than non-urological radiologists, according to new research.

The artificial intelligence (AI)-powered module provides a prostate segmentation tool for MRI-guided transurethral ultrasound ablation (TULSA) procedures in patients with prostate cancer.

While acknowledging variable accuracy overall with CT-derived fractional flow reserve (FFR-CT) values, researchers found that the accuracy rate increased to 90 percent for FFR-CT values greater > 0.90 and < 0.49.

In a recent lecture at the 2024 ARRS Annual Meeting, Jordana Phillips, MD, discussed the role of contrast-enhanced mammography in staging breast cancer, evaluating response to neoadjuvant chemotherapy and recalls from screening.

Using a learning network model to discuss challenges and share insights among radiology departments from five different organizations, researchers noted that 87 percent of audited prostate MRI exams had PI-QUAL scores > 4 at the conclusion of the collaborative program.

In a recent interview, Stacy Smith-Foley, M.D., shared her perspective on the shortcomings of the recently issued breast cancer screening recommendations from the United States Preventive Services Task Force (USPSTF).

In a study involving over 1,000 visible prostate lesions on biparametric MRI, a deep learning algorithm detected 96 percent of clinically significant prostate cancer (csPCa) in comparison to a 98 percent detection rate for an expert genitourinary radiologist.

A point-based model that incorporates prostate MRI findings offered a sensitivity rate of 89.5 percent for detecting clinically significant prostate cancer and could prevent over 20 percent of biopsies, according to new research.