Michal Meiri

AI
Operational applications drive AI adoption in radiology
When talking about artificial intelligence (AI) in radiology, one immediately thinks of the classic clinical use case of computer vision and powerful algorithms identifying abnormalities on imaging studies. Operational applications of AI, however, will likely be the first to be widely adopted.
March 16, 2020
2018 08 28 18 59 9066 Artificial Intelligence Ai 400
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At ARRS 2025, Dana Kong from Case Western Reserve University presented on breast cancer trends among women aged 40 years and younger. Among her team's findings, young women who had imaging prior to their cancer diagnosis had higher receptor status and underwent surgery more frequently than those with no prior imaging.
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Images show a palpable mass in the left breast in a 55-year-old woman. (A) Mammography shows a 26-mm ill-defined mass with fine linear calcifications in the central portion of the left breast (arrows). (B) ultrasound shows a 23-mm hypoechoic mass with calcifications in the corresponding area accompanying palpability (between the arrows). (C) An axial, fat-saturated, contrast-enhanced T1-weighted MRI scan shows an 18-mm irregular heterogeneous enhancing mass in the left breast (arrows). After breast-conserving surgery, the final pathologic report confirmed a grade II estrogen receptor–negative, progesterone receptor–negative, human epidermal growth factor receptor 2–positive invasive ductal carcinoma. There was no lymphovascular invasion, and the resection margins were clear. The patient declined both adjuvant chemotherapy and radiation therapy due to psychologic distress. (D) 11 months later, surveillance ultrasound helped to detect a new 25-mm irregular hypoechoic mass at the 12-o’clock position in the left breast (arrows). A subsequent ultrasound-guided biopsy confirmed it as a recurrence of grade II invasive ductal carcinoma, maintaining the same receptor statuses. Images courtesy of the RSNA.
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Representative images from diagnostic analysis of deep learning (DL)-based CT myocardial perfusion imaging (MPI). (A-D) Representative images in a 51-year-old woman with stable angina at presentation. (A) Curved planar reformation and (B) three-dimensional maximum intensity projection images show severe stenosis of the opening left anterior descending artery (arrow). (C) Fractional flow reserve (FFR) determined with invasive coronary angiography helps confirm that there was no hemodynamic significance of the opening left anterior descending artery lesion (arrow). (D) Dynamic CT MPI provided functional information to identify hemodynamically significant stenosis with reference to invasive FFR. Myocardial blood flow (MBF) derived from DL-based CT MPI revealed the absence of decreased myocardial perfusion of the left anterior descending artery territory (the areas outlined in black represent the myocardium subtended by the left anterior descending stenosis, and the color bar is a graphical scale that maps MBF ranges). (E-H) Representative images in a 62-year-old man with exertional chest pain at presentation. (E) Curved planar reformation and (F) three-dimensional maximum intensity projection images show moderate stenosis of the middle left anterior descending artery (arrow). (G) FFR determined at invasive coronary angiography helps confirm that the middle left anterior descending lesion was hemodynamically significant (arrow). (H) Dynamic CT MPI provided functional information to identify hemodynamically significant stenosis with reference to invasive FFR. DL-based CT MPI showed a reduction in MBF within the middle and basal anterior wall of the left anterior descending artery territory (the areas outlined in black represent the myocardium with decreased perfusion, and the color bar is a graphical scale that maps MBF ranges). MBFDL = MBF derived from the DL model.
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