Quibim Webinar | Advancements in prostate cancer diagnosis: The role of medical imaging and AI скачать в хорошем качестве

Quibim Webinar | Advancements in prostate cancer diagnosis: The role of medical imaging and AI

Prostate cancer is the second most common cancer in men worldwide and the second cause of death. The global burden of prostate cancer is expected to grow by over two million cases by 2040 due to the aging population and the increasing number of men diagnosed with this disease. Despite the progress made in diagnosing and treating this disease, prostate cancer remains a significant medical issue for men. Magnetic resonance imaging (MRI), as a diagnostic tool, is still regarded as the most accurate method for identifying prostate cancer. Compared to a systematic biopsy, this method significantly increases the number of cancer cases detected. To address the complexity of prostate cancer diagnosis, Quibim brings together international experts to discuss the use of MRI as a diagnostic tool and the potential applications of Artificial Intelligence (AI) in the detection and drug discovery process. 0:00 Introduction 2:11 Intervention of Rajan T. Gupta, M.D, Chief of Abdominal Imaging from Duke University Medical Center 2:34 Current State of Affairs 6:00 Role of MRI 9:37 New challenges and new frontiers 15:32 Intervention of Juan Morote, M.D, PHD Professor of Urology at Vall d'Hebron Hospital and School of Medicine 16:31 Early detection of PCa 19:17 Current Decision-Making of prostate biopsy 28:22 Future directions for csPCa early detection 33: 14 Advancements in prostate cancer diagnosis: the role of medical imaging and AI by Dr. Holskin 33:27 Why focus on prostate cancer? 36:10 Efficiency and precision throughput the prostate workflow - QP Prostate 44:00 Questions 45:06 Question: Why do we want to decrease diagnoses for low-grade prostate cancer? 47:34 Question: 10 years ago without MRI, would you have gone for a biopsy to identify a PI-RADS II lesion? 50:05 Question: Would you trust AI technologies to make a therapeutic decision if AI is based on a black-box approach? What would increase your trust of a predictive AI model? 52:30 Question: Which sequences are used for analysis on QP-Prostate? 54:30 Question: How many b-values do you use on DWI? Is it PI-RADS compliant? Does it require its own specific acquisition to implement on each scanner? 56:40 Question: Do you as a radiologist feel threatened by AI?