AI for Urology

Urology is one of the many medical fields that are being revolutionized by Artificial Intelligence. Prostate, bladder, urethra, kidneys and more key organs are already benefitting from impressive breakthroughs brought about by new technologies. AI is now able to contribute to more accurate and faster detection, segmentation, classification and diagnose for many urology disfunctions and diseases, including cancer, BPH and urolithiasis. Thanks to the latest generation of AI and computer vision techniques (deep learning in particular), AI provides now a precious support to the medical team in the operating room as well as during all stages of treatment, drastically improving the quality of therapy and reducing recovery time. You can find below some of RSIP Vision's projects and pioneering research in the field of AI for urology.

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). Read more...

Image Analysis and AI for Benign Prostatic Hyperplasia

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. Read more...

Urolithiasis Healthcare Using AI

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 outcomeRead more...

Renal Cancer treatment with Robotic Surgery and AI

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. Read more...

Automated Prostate Cancer Detection

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. Read more...