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Bladder AI
Bladder AI is a standalone software application designed to aid healthcare professionals in evaluating bladder ultrasound images. It uses machine learning to simplify workflow and improve accuracy. Developed by Exo Imaging and cleared by the FDA, it’s intended to quantify bladder volume in two and older patients.
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MOZI Treatment Planning System (MOZI TPS)
The MOZI Treatment Planning System (MOZI TPS) is a sophisticated standalone software for planning radiotherapy for both malignant and benign conditions. It utilizes advanced technologies such as Monte Carlo methods and deep learning for enhanced precision and efficiency.
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ECHELON Synergy MRI System
The ECHELON Synergy MRI System, manufactured by FUJIFILM Healthcare Corporation and cleared by the FDA, is a Class II device that uses a 1.5 Tesla superconducting magnet for advanced magnetic resonance imaging. Its design and technology enhance clinical utility, providing high-quality images for medical diagnostics without ionizing radiation.
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Spine Auto Views
Spine Auto Views is medical software developed by GE Healthcare that automates the generation of focused spinal images via CT scans, enhancing diagnostic efficiency and accuracy. The FDA cleared it for use in various medical settings to support detailed anatomical imaging, and it must be operated by trained professionals.
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Brainlab Elements Suite
The Brainlab Elements suite, version 6.0, comprises a range of software applications tailored for image-guided surgery and radiation treatment planning, including modules like Contouring, Image Fusion, and Fibertracking. These applications are designed to aid medical professionals by processing medical image data for precise surgical interventions and treatment planning, supported by clinical validations to ensure safety…
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scDrugPrio: Revolutionizing Autoimmune Disease Treatment By Creating Digital Twins
Researchers at Karolinska Institutet have developed scDrugPrio, a computational model for creating a “digital twin” of autoimmune diseases, enabling personalized medication choices by analyzing cellular interactions and drug effects. This innovative approach, validated in studies on mice and patients with inflammatory bowel disease, promises to enhance treatment efficacy and reduce healthcare costs by selecting the…
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HLA Inception: A Convolutional Neural Network To Predict MHC-I Peptide Bindings
Arizona State University scientists developed “HLA Inception,” a groundbreaking AI tool using convolutional neural networks to predict MHC-1 peptide bindings. This tool, leveraging molecular electrostatics, offers rapid, proteome-scale predictions, transforming complex analysis processes from days to seconds and significantly impacting patient care in oncology.
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BioSUM: Advanced Sensors To Monitor Postoperative Gastrointestinal Leaks
Researchers have developed “BioSUM,” a groundbreaking bioresorbable device designed to detect postoperative leaks in gastrointestinal surgery through real-time monitoring of pH changes in deep tissues. This innovative solution, capable of adapting to different pH levels across gastrointestinal organs, aims to significantly improve patient outcomes by allowing early intervention in cases of leaks, which are critical…
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