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Type 4 cholangiocarcinoma

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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ERCP Gallstones and strictures

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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Robotic Surgery

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

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Segmented prostate gland from MRI scan

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.
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Segmented prostate gland

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.

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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.

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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.

Read More
ERCP Gallstones and strictures

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.

Read More
Robotic Surgery

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

Read More
Segmented prostate gland from MRI scan

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.
Read More
Segmented prostate gland

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.

Read More

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.

Read More

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.

Read More

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

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