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
RSIP Vision Learns
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,
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
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
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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