FDA Clears First AI Solution for 2D and 3D Mammography
ScreenPoint Medical receives greenlight for Transpara™ 1.6.0. to reduce read times.
ScreenPoint Medical received clearance Monday from the U.S. Food & Drug Administration (FDA) for the first artificial-intelligence solution functional for both 2D and 3D mammography.
The greenlight for the solution, Transpara™ 1.6.0, expands ScreenPoint’s existing global marekt for Transpara 3D technology.
“This is a major breakthrough from ScreenPoint Medical, and it means that our new and existing U.S. customers now have access to the power of Transpara 3D for the first time,” ScreenPoint Chief Executive Officer Nico Karssemeijer said in a statement. “Multiple independent peer-reviewed publications have already demonstrated that Transpara significantly improves accuracy in 2D mammography, now the same applies for 3D.”
The company earned FDA clearance by demonstrating, through a clinical reader study, that radiologist accuracy improved when they read 3D mammograms while using Transpara. Additionally, according to study results, their reading time fell to approximately 35 seconds, roughly the same as the time needed for reading 2D mammograms.
Alongside reduced turn-around time, ScreenPoint officials also said the Transpara™ 1.6.0. interface was re-designed, using the company’s patented co-registration slice technology, to, potentially, maximize reading workflow.
Based on company information, Transpara has clinical installations in 15 countries, and it will be marketed in the United States by ScreenPoint Medical Inc., Siemens Healthineers USA, and Volpara Solutions.
Considering Breast- and Lesion-Level Assessments with Mammography AI: What New Research Reveals
June 27th 2025While there was a decline of AUC for mammography AI software from breast-level assessments to lesion-level evaluation, the authors of a new study, involving 1,200 women, found that AI offered over a seven percent higher AUC for lesion-level interpretation in comparison to unassisted expert readers.
Can CT-Based Deep Learning Bolster Prognostic Assessments of Ground-Glass Nodules?
June 19th 2025Emerging research shows that a multiple time-series deep learning model assessment of CT images provides 20 percent higher sensitivity than a delta radiomic model and 56 percent higher sensitivity than a clinical model for prognostic evaluation of ground-glass nodules.
