Sperling Prostate Center

Category: Artificial Intelligence

Artificial Intelligence in Medicine: Ultra-Fast MRI Accurately Detects Prostate Cancer

Can MRI Plus Artificial Intelligence Diagnose Prostate Cancer?
Today’s multiparametric MRI (mpMRI) has revolutionized the prostate cancer (PCa) world. Thanks to its high resolution, 3-dimensional imaging done by a powerful 3 Tesla (3T) magnet, experienced radiologists are able to identify areas that are suspicious for clinically significant PCa (csPCa). This helps avoid unnecessary biopsies, but if a biopsy is indicated, the area can keep reading

Artificial Intelligence in Medicine: AI Improves Identification of Extracapsular Extension Before Surgery

Can MRI Plus Artificial Intelligence Diagnose Prostate Cancer?
We’ve posted numerous blogs on the contributions of Artificial Intelligence (AI) to various aspects of MRI detection and diagnosis of prostate cancer (PCa). We’ve reported AI’s ability to predict metastasis, assign Gleason scores and PI-RADS scores, pinpoint tumor margins for focal therapy, rule out need for biopsy, predict PCa metastasis, and more—showing the accuracy and keep reading

Artificial Intelligence in Medicine: Multimodal Deep Learning and Risk Stratification

Can MRI Plus Artificial Intelligence Diagnose Prostate Cancer?
A groundbreaking study reveals how artificial intelligence outperforms traditional methods in prostate cancer risk stratification. By leveraging deep learning and digital biopsy data, AI enhances accuracy, reduces errors, and improves treatment planning for better outcomes. Learn how this innovative technology is transforming prostate cancer diagnosis and care. keep reading

Artificial Intelligence in Medicine: The Lead is Narrowing in the Radiologist vs. AI Race

No radiologist was born knowing how to interpret MRI scans of the prostate gland. They must be trained to interpret images, and the rate at which they improve is called their learning curve. According to Rosencrantz et al. (2017), At a basic level, by seeing many cases, radiologists experience innate self-directed visual learning, even without keep reading
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