Fireside Chat – The Power of AI in Robotic Surgeries
This Fireside Chat was held on Tuesday September 26, 2023 Guest speaker: Simone Crivellaro, MD, MHA UIC, Urology Dept, Vice Chair, Lawrence S. Ross, Professor
Tag: Urology
AI-Enabled Urology – A Clinician’s Perspective
This webinar was held on Wednesday April 19, 2023 Guest speaker: Michael Gorin, M.D. Associate Professor of Urology at Icahn School of Medicine at Mount
AI for Benign Prostatic Hyperplasia BPH
How can Artificial Intelligence benefit urology patients, in particular those affected by prostate BPH (Benign Prostatic Hyperplasia)? AI-based solutions for urology applications are under development
Announcement – New Urological AI Tool for 3D Reconstruction of the Ureter
RSIP Vision Presents New Urological AI Tool for 3D Reconstruction of the Ureter Improving Urological Procedures Innovative technology utilizes 2D fluoroscopic images and reconstructs an
Stone tracking during kidney stone removal
Urolithiasis, or kidney stones, is a common pathology affecting nearly 10% of the population in the USA. There are various treatment options for urolithiasis. The
AI-Assisted Tissue Sparing in Urology
Tissue sparing is a common practice during surgeries. This approach aims to remove as little as possible of the surrounding tissue during a procedure. Studies
Announcement – New AI Tool for Prostate MRI Analysis to Support PI-RADS Scoring
RSIP Vision Presents New AI Tool for Prostate MRI Analysis to Support PI-RADS Scoring Innovative technology performs automatic segmentation and lesion detection in prostate MRI
PCNL – Planning and real-time navigation
Urolithiasis, or kidney stones, is a common pathology affecting nearly 10% of the population in the USA. Percutaneous Nephrolithotomy (PCNL) is a minimally invasive urology
Implementing AI to Improve PI-RADS Scoring
Prostate Cancer and PI-RADS scoring Prostate cancer is the most common male cancer in the USA. When diagnosed early, mortality rates are very low, therefore,
Image Analysis and Artificial Intelligence in Urology
Artificial intelligence (AI) and deep learning play an increasingly crucial role in medical imaging in general, and in the field of urology particularly. The applications of AI in urology are numerous, starting with accurate diagnosis (using image segmentation and abnormality detection), continuing with biopsy and operative procedures (using tools for assisted navigation and robotic guidance), and ending in treatment assessment (using tools similar to those used in diagnosis in order to assess the response to treatment).
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AI and Robotic Surgery for Renal Cancer
Image analysis techniques and artificial intelligence are leading to radical innovations in renal cancer diagnosis and treatment. In particular, renal cancer robotic surgery. Advanced AI algorithms and computer vision assist in detecting and classifying all kinds of renal diseases, using segmentation and contour detection. This results in improved diagnostic accuracy and enhanced personalized treatment for patients. Moreover, robotic assistance in renal surgeries has gained increased traction in both complete and partial nephrectomies. Surgical planning and 3D reconstruction based on CT and MRI images play vital roles in successful robotic-assisted kidney-related procedures
AI and Deep Learning for Prostate Cancer
Recent developments in the field of deep learning and artificial intelligence (AI) are moving the needle in prostate cancer healthcare. More specifically, it is now possible to use state-of-the-art AI and Deep Learning for prostate cancer detection and treatment. Also prostatectomy, a common treatment of prostate cancer, can benefit from the use of these advanced algorithms to increase procedural success. RSIP Vision’s algorithms provide a solution that can be integrated into all steps of prostate cancer care, thus improving patient outcome.
Image Analysis and AI for BPH
Recent developments in the field of deep learning and artificial intelligence can aid in BPH detection, classification and treatment. Analyzing ultrasound and MRI images, and using deep-learning segmentation tools to process them, gives a baseline for severity classification by the physician. Follow-up scans can be accurately compared to baseline scans for optimal treatment decision. Real-time tracking, 3D image reconstruction, and fusion can all provide better guidance during stent placement and urinary tract dilation. Prostatectomy procedure can be kept within boundaries at all times.
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Improving Urolithiasis Healthcare Using AI and Image Analysis
Deep learning and artificial intelligence solutions have recently been developed to improve urolithiasis detection and treatment, leading to enhancing the clinical outcome. Utilizing convolutional neural networks provides accurate stone recognition and segmentation. Automatic Neural-Networks or Support Vector Machine (SVM) classifiers on kidney stone CT data classify the stones into their subtypes with notable accuracy, assisting and speeding treatment selection. Throughout the full cycle of detection and treatment of urolithiasis, RSIP Vision’s custom AI image analysis algorithms significantly improve urolithiasis procedures and outcome.
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Prostate segmentation in MR images
Prostate cancer is the second most common cancer among American men, with more than 200,000 new cases diagnosed every year and about 1 man in 7 diagnosed during his lifetime. Volume is a key indicator of the health of the prostate, revealing key information about the stage of the cancer, the probable prognosis and viable treatment. The rich experience of RSIP Vision enables us to recommend an approach based on a semi-automatic prostate segmentation to give a precise estimate of the prostate volume.
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Kidney Segmentation
The most dramatically common kidney diseases are: kidney cancer, hitting 50,000 new patients every year only in the U.S.; and kidney failures, which leave the organ unable to remove wastes. Laparoscopic partial nephrectomy operations remove or reduce kidney tumors and some renal malfunctions. We at RSIP Vision help by providing a semi-automatic and very accurate kidney segmentation technique, built on deep learning and neural networks to create a kidney model which would be specific for each patient.
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