Development and validation of an AI-enabled digital breast cancer assay to predict early-stage breast cancer recurrence within 6 years.

Background: Breast cancer (BC) grading plays a critical role in patient management despite the considerable inter- and intra-observer variability, highlighting the need for decision support tools to improve reproducibility and prognostic accuracy for use in clinical practice. The objective was to evaluate the ability of a digital artificial intelligence (AI) assay (PDxBr) to enrich BC grading and improve risk categorization for predicting recurrence.

Methods: In our population-based longitudinal clinical development and validation study, we enrolled 2075 patients from Mount Sinai Hospital with infiltrating ductal carcinoma of the breast.

Use of distributed acoustic sensing for Doppler tracking of moving sources.

Distributed acoustic sensing (DAS) via fiber-optic reflectometry techniques is finding more and more applications in recent years. In many of these applications, the position of detected acoustic or seismic sources is defined with a single longitudinal coordinate which specifies the distance between the detection point in the fiber to the DAS interrogator. In this paper we describe a DAS system which is intended to operate in a fluid (air or water) and to detect and localize moving objects, with three spatial coordinates, using the acoustic waves they generate or reflect and their Doppler shifts.