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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.

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.

Deep Learning for Cardiac Ultrasound (Echocardiography)

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.

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.

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.

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.

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.

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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blog

Image Analysis and AI in Diagnostic ERCP

Enhanced ERCP tumor assessment using AI

AI in ERCP gallstones and strictures treatment

AI and Robotic Surgery for Renal Cancer

AI and Deep Learning for Prostate Cancer

Image Analysis and AI for BPH

Improving Urolithiasis Healthcare Using AI and Image Analysis

AI for Gastric Cancer Detection

Real time SLAM in Endoscopy Applications

Deep Learning for Cardiac Ultrasound (Echocardiography)

Deep Learning for Cardiac Ultrasound (Echocardiography)

AI in Cardiac MRI Segmentation

AI in Cardiac CT angiography

Endoscopic Procedures using Intravenous OCT

Using AI to Analyse Intravenous Ultrasound Images

Segmentation in Orthopedics with Deep Learning

Surgical Instrument Segmentation

AI-based Surgical Workflow Analysis, another big step towards the future of robotic surgery

Challenges and AI Solutions for Endoscopy

Sensors for ADAS Technology: 3. RADARs

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