January 3rd 2025
Adjunctive AI offered greater than seven percent increases in sensitivity, specificity, and accuracy for ultrasound detection of ovarian cancer in comparison to unassisted clinicians who lacked ultrasound expertise, according to findings from new international multicenter research.
Can Deep Learning Ultrasound Assessment be a Viable Option for Diagnosing Ovarian Cancer?
April 20th 2022A new study suggests that deep learning algorithms with multimodal ultrasound have comparable specificity and sensitivity to subjective expert assessment and use of the O-RADS classification to distinguish between benign and malignant ovarian tumors.
Could an Emerging Classification Scheme Streamline Ovarian Cancer Detection on Pelvic Ultrasound?
March 22nd 2022Distinguishing between classic and non-classic presentations of isolated adnexal lesions on pelvic ultrasound reportedly had a high specificity and sensitivity in diagnosing ovarian cancer in women at average risk.
Is Handheld Ultrasound the 'New Stethoscope'?
March 2nd 2022In combination with cloud-based data infrastructure and artificial intelligence (AI) applications, handheld ultrasound devices may have the potential to significantly enhance efficiency for providers and contribute to improved patient outcomes.
COVID Vaccine Side Effects and Mammograms: What a New Study Reveals
February 8th 2022Emerging research suggests that mammograms should not be delayed for women who develop unilateral lymphadenopathy after receiving COVID-19 vaccines, and that follow-up imaging is unnecessary for patients who have no history of cancer or suspicious concurrent symptoms.
Proposed Algorithm for Axillary Ultrasound Evaluation in Breast Cancer Patients Seeks Middle Ground
November 5th 2021Investigators at Massachusetts General Hospital proposed an algorithm for appropriate axillary nodal imaging to better align with changes in treatment algorithms for axillary nodal disease in patients with breast cancer.