- AI
- Molecular Imaging
- CT
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- Ultrasound
- MRI
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- Mammography
Ultrasound-Derived Fibroid Mapping Technology Gets FDA Nod
The newly FDA-cleared Fibroid Mapping Reviewer Application, which allows the creation of 3D uterus models from ultrasound images, reportedly facilitates diagnosis and interventional procedures for patients with uterine fibroids.
Current estimates suggest that one in four women develop uterine fibroids that can lead to pelvic pain, excessive menstrual bleeding, and infertility. However, emerging ultrasound-derived technology may bolster diagnostic accuracy and aid in the planning of interventional procedures in this patient population.
The Fibroid Mapping Reviewer Application (FMRA) (Nesa Medtech) has garnered 510(k) clearance from the Food and Drug Administration (FDA). Based on ultrasound images, the FMRA enables clinicians to generate three-dimensional (3D) models of the uterus that may facilitate more targeted interventional procedures for women with uterine fibroids.
Ensuring accuracy in ascertaining the location and measurements of uterine fibroids is essential to achieving optimal outcomes for these patients, according to Nesa Medtech.
“While uterine fibroids are extremely common, for some patients, they can be extremely painful and require intervention,” noted John Petrozza, M.D., an assistant professor at Harvard Medical School and co-director of the Minimally Invasive Gynecologic Surgery Program at Massachusetts General Hospital in Boston. “Nesa’s FMRA technology allows physicians to determine fibroid location and dimensions, critical details for effective minimally invasive image-guided treatment planning.”
(Editor’s note: For related content, see “What a Transvaginal Ultrasound Study Reveals About Fibroid Prevalence in Minority Women” and “Uterine Fibroid Embolization: Past, Present, and Future.”)
Study: AI Boosts Ultrasound AUC for Predicting Thyroid Malignancy Risk by 34 Percent Over TI-RADS
February 17th 2025In a study involving assessment of over 1,000 thyroid nodules, researchers found the machine learning model led to substantial increases in sensitivity and specificity for estimating the risk of thyroid malignancy over traditional TI-RADS and guidelines from the American Thyroid Association.
The Reading Room: Racial and Ethnic Minorities, Cancer Screenings, and COVID-19
November 3rd 2020In this podcast episode, Dr. Shalom Kalnicki, from Montefiore and Albert Einstein College of Medicine, discusses the disparities minority patients face with cancer screenings and what can be done to increase access during the pandemic.
