RSIP Vision Learns

How Can AI Assist in Single-port Robotic Surgery?

The use by the surgical community of single-port as a surgical approach is increasing in frequency. A port is the entrance gate of the tools

AI-Assisted Brain Surgery

Brain surgeries are very complex: they need to be extremely accurate, since you want to spare healthy tissue; planning is very thorough, as the surgeon

Visualization Modalities for Surgery with AI

Our team has followed with deep attention the recent Hamlyn Symposium. In particular, we have enjoyed the great presentation by Professor Laura Marcu from UC

AI for Structural Heart Procedures

Structural heart diseases include structural deformation of the heart, like valve leakage: the blood flows in two directions and the patient is losing efficiency of

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

Trimming and Anonymizing Surgical Videos

As the use of robotic assisted surgeries (RAS) increases, so does the amount of recording cameras in the operating room (OR). The data from these

Data Generation in Robotic Assisted Surgeries (RAS)

Every deep learning based system requires sufficient data for proper training and reliable testing. Therefore, data collection and annotation are the first and foremost required

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,

One-click segmentation of medical images

In the medical field, image analysis plays a crucial role in both diagnosis and treatment. Its central tool is segmentation, which involves partitioning an image

Image Analysis and AI in Diagnostic ERCP

Recent developments in the field of medical image analysis and artificial intelligence (AI) are used to improve the procedural outcomes of ERCP (Endoscopic retrograde cholangiopancreatography). Here is how RSIP Vision develops AI for diagnostic ERCP. Read what we do for enabling 3D Image Reconstruction and Image Registration/Fusion. Learn how strictures detection and classification can provide the physicians with classification scoring and, sometimes, help them avoid unnecessary biopsies during ERCP.

Enhanced ERCP tumor assessment using AI

ERCP involves both endoscopy and fluoroscopy on a region with limited access. Accordingly, it poses several challenges for the gastroenterologist performing the procedure: imaging and artificial intelligence are key technologies to solve these challenges. They are used to reconstruct multiangle 2D X-ray images into a 3D image that will help the gastroenterologists in real-time navigation, reducing any complications involved in the navigation process. AI can be trained to accurately classify each tumor found by ERCP and check whether it is cancerous or not.

AI in ERCP gallstones and strictures treatment

When performing ERCP for stone removal and stricture treatment, the gastroenterologist must overcome several challenges, namely navigation in the region of interest and the choice of the most fitting treatment that must be selected and implemented. AI enables to reconstructing a 3D image from multi-angle X-ray images or ultrasound slices. It is also trained to accurately classify the type of blockage that necessitated the ERCP procedure in the first place, resulting in quicker and more efficient ERCP procedures.

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.

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.

AI for Gastric Cancer Detection

Upper gastrointestinal cancers, including esophageal cancer and gastric cancer, are among the most common cancers worldwide. However, a lack of endoscopists with colonoscopy skills has been identified and solutions are critically needed. The development of a real-time robust detection system for colorectal neoplasms is needed to significantly reduce the risk of missed lesions during colonoscopy.

Real time SLAM in Endoscopy Applications

A family of algorithms called simultaneous localization and mapping (SLAM) are able, in real time, to create a 3D map of a scene captured by a camera and calculate with very high accuracy the location of the camera in the scene. As a result, RSIP Vision’s engineers are able to create a precise 3D model of the endoscope environment and calculate its exact location in that model.

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