An API powered by Google's AI technology allows you to accurately convert speech into text. You can accurately caption your content, provide a better user experience with products using voice commands, and gain insight from customer interactions to improve your service. Google's deep learning neural network algorithms are the most advanced in automatic speech recognition (ASR). Speech-to-Text allows for experimentation, creation, management, and customization of custom resources. You can deploy speech recognition wherever you need it, whether it's in the cloud using the API or on-premises using Speech-to-Text O-Prem. You can customize speech recognition to translate domain-specific terms or rare words. Automated conversion of spoken numbers into addresses, years and currencies. Our user interface makes it easy to experiment with your speech audio.
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Jama Connect®, a product development platform, uniquely creates Living Requirements™. This digital thread is created through siloed, test, and risk activities to provide end to end compliance, risk mitigation, process improvement, and compliance. Companies creating complex products, systems, and software can now define, align, and execute on what they need. This reduces the time and effort required to prove compliance and saves on rework. You can be sure of success by choosing a solution that is easy-to-use, flexible, and offers support and services that are adoption-oriented.
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Elements Contrast Clearance Analysis
Brainlab's Elements Contrast Clearance Analysis is an MRI-based technique aimed at distinguishing areas of contrast clearance and accumulation within brain tumor imaging datasets. This advanced high-resolution analysis enhances the understanding required for ongoing evaluations and decision-making across various clinical fields, including radiosurgery, radiation oncology, neurosurgery, neuro-oncology, and neuroradiology. The methodology entails capturing two standard 3D T1-weighted MRIs; the first scan is taken around 5 minutes after administering a standard dose of contrast agent, while the second scan occurs 60 to 105 minutes later. By subtracting the initial series from the subsequent one, volumetric maps are created that clearly identify zones of contrast clearance (shown in blue) against those of contrast accumulation (illustrated in red). These findings empower clinicians to better evaluate the effects of radiation treatment in contrast to potential tumor regrowth, allowing for more educated decisions regarding both initial and subsequent treatment plans. As a result, this analysis not only aids in clinical assessments but also enhances the overall management of patient care in complex cases.
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Elements Spine SRS
Brainlab's Elements Spine Stereotactic Radiosurgery (SRS) represents a cutting-edge software solution aimed at refining the management of spinal metastases. This innovative workflow features automation at each phase, encompassing anatomical mapping, curvature adjustments, and target identification, which guarantees precision and uniformity down to submillimetric levels. A distinctive algorithm addresses variations in spinal curvature, thereby improving the accuracy of image fusion. The software employs automatic segmentation of spinal anatomy through a patented synthetic tissue model that effectively identifies and labels different spine levels for accurate dose calculations. Tools for outlining spinal tumors are included for Gross Tumor Volume (GTV) contouring, along with automatic suggestions for the Clinical Target Volume (CTV) and a cropped spinal canal object in alignment with International Spine Consortium Guidelines. Furthermore, the integration of AI-driven contouring solutions allows for the swift and dependable delineation of over 200 structures, including lymph nodes. This advancement not only streamlines the treatment process but also enhances overall patient outcomes in spinal cancer management.
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