![]() The tested hypotheses are AUC of multi modal AI is superior to single modal AI systems, AUC of the combination AI Radiologists is superior to the AUC of radiologists alone reading DM. ![]() Performances were compared using receiver operating characteristic analysis ( and their area under the curve. Multi modal AI is computed by averaging normalized scores of the two single modal AI systems. A subcohort of mammograms was selected for an observer study with 2 radiologists 7 and 10 years of experience) all 42 malignant cases and a total of 30 benign and 80 normal cases randomly selected.ĪI for mammograms (AI DM) Transpara™ (v 1 5 0 ScreenPoint Medical) AI for automated 3 D breast ultrasound (AI ABUS) QVCAD 3 4 Qview Medical Inc Los Altos, California, USAīased on automatically detected suspicious findings, each AI system assigns a continuous score to each examination representing the likelihood that cancer is present 114 w/ benign lesions, 274 normal with 1 year follow up, 73 BI RADS breast density C or D. Average age 48 years old (range 30 70), 42 w/ biopsy proven malignant lesion. ![]() ![]() To assess the combined performance of artificial intelligence ( detection systems for mammography and automated 3 D breast ultrasound) and their value to improve radiologists’ performance in detecting breast cancer in dense breastsĤ30 women with paired DM and ABUS examinations from an Asian population with predominantly dense breasts, with the following characteristics. Tao Tan, Alejandro Rodriguez Ruiz, Nico Karssemeijer, Ritse M. Multimodal artificial intelligence for breast cancer detection in a population of women with dense breasts ![]()
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