Use of novel artificial intelligence computer-assisted detection (AI-CAD) for screening mammography: an analysis of 17,884 consecutive two-view full-field digital mammography screening exams.

Background: Novel artificial intelligence computer-assisted detection (AI-CAD) systems based on deep learning (DL) promise to support screen reading.

Purpose: To test a DL-AI-CAD system compared to human reading on consecutive screening mammograms.

Material And Methods: In this retrospective study, 17,884 consecutive anonymized screening mammograms, double-read from January to November 2018, were processed by the DL-AI-CAD system.

Use of ultrasound-guided percutaneous vacuum-assisted breast biopsy for selected difficult indications.

To assess ultrasound-guided vacuum-assisted biopsy (US-VAB) for selected problem cases and to report experiences with two different biopsy systems. Fifty-one lesions have been biopsied using the Mammotome (n = 24) or the Vacora (n = 27) system. Main indications: lesion in scarring (n = 5), complex cystic >or=8 mm (n = 7), increase in size (n = 10), architectural distortion (n = 4), uncharacteristic palpable abnormality (2), small size (n = 22), regional microcalcifications (n = 1).