Success Rating in Robotic Assisted Surgeries
Success Rating and Dynamic Feedback Minimally invasive surgeries (MIS), specifically robotic assisted surgeries (RAS), generally have an improved outcome compared with standard surgeries. However, they
RSIP Vision Learns
Tissue Sparing in Robotic Assisted Orthopedic Surgeries
Orthopedic surgeries such as hip or knee replacement are performed via an incision which often compromises the surrounding tissue. The assimilation of RAS into these
Procedural Planning in Urology
Challenges in Biopsies There are various types of urological cancers, the most common ones being prostate and kidney cancer. In cancer, early and accurate detection
Radiation Reduction in Robotic Assisted Surgeries (RAS) Using AI
Fluoroscopy is an extremely useful imaging tool in surgical procedures in orthopedics, cardiology, GI, etc. In some cases, it is used as a real-time modality
Hyperspectral Imaging for Robotic Assisted Surgery
Standard imaging techniques make use of the visible light spectrum. As is known, the visible light is divided into three bands – red, green, and
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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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.
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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.
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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.
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Deep Learning for Cardiac Ultrasound (Echocardiography)
Despite the importance of echocardiography in the diagnosis and treatment of serious cardiac illness, this imaging technology faces two main challenges: Image quality and image assessment. RSIP Vision uses deep learning to enhance both, making it easier for physicians and researchers to interpret findings. As a result, our method resolves user variability, accuracy and efficiency in cardiac ultrasound with advanced, deep learning neural networks. Learn how we do it on our software.
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AI in Cardiac MRI Segmentation
Cardiac magnetic resonance (CMR) imaging plays a critical role in the assessment and management of patients with coronary artery disease (CAD), a leading cause of
AI in Cardiac CT angiography
Coronary computed tomography angiography (CCTA) is an efficient and non-invasive imaging modality with widespread clinical implementation in the identification of coronary artery disease (CAD). With
Endoscopic Procedures using Intravenous OCT
Recently, OCT has emerged as an alternative modality that provides high resolution images. While ultrasound imaging cannot be replaced, adding Intravenous Optical Coherence Tomography (IVOCT) to endoscopy procedures significantly improves image resolution and increases the ability to detect plaque and segment it.
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Using AI to Analyse Intravenous Ultrasound Images
Intravenous ultrasound (IVUS) has been used for many years in the diagnosis of cardiovascular diseases. The recent use of deep learning based on convolutional neural networks has shown improved accuracy, and has also enabled additional applications such as plaque detection.
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Segmentation in Orthopedics with Deep Learning
Segmentation is highly important both for examination and planning of knee replacement, hip replacement, shoulder surgery, lesion detection, osteotomy and many other orthopedic procedures. Deep Learning is repeatedly being proven to be the most powerful framework for various tasks, and segmentation in orthopedics is no exception. RSIP Vision’s CTO Ilya Kovler explains how to improve the segmentation in orthopedics thanks to AI and deep learning.
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Surgical Instrument Segmentation
Using computer vision to identify tools being employed at different stages of a procedure is not only another step toward robotic surgery, it’s a simple, yet very useful tool to streamline and safeguard the surgical process. Surgical instrument (tool) segmentation and classification is a computer vision algorithm that complements workflow analysis. It automatically detects and identifies tools used during the procedure, and assess whether they are used by the surgeon correctly.
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AI-based Surgical Workflow Analysis, another big step towards the future of robotic surgery
Surgical workflow analysis is an important safety guard for the surgeon: with it, a computer is able to scan a video of a surgery, either offline after it has already been performed or online during the surgery itself, and automatically identify at what stage the surgery is at. Read about RSIP Vision’s approach, built on many years of experience in the development of practical applications.
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Challenges and AI Solutions for Endoscopy
As endoscopic and microscopic image processing, and surgical vision are evolving as necessary tools for computer assisted interventions (CAI), researchers have recognized the need for
Sensors for ADAS Technology: 3. RADARs
This is a series of three articles about sensors used by the automotive industry to allow perception on autonomous vehicles and increase security for all. Read about RGB cameras, LiDARs and Radars. RSIP Vision and its engineers have a rich experience in sensors for ADAS systems and for autonomous driving.
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