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Improve Brain Oxygenation Analysis with AI Solutions
We’re looking for a driven student, passionate about innovation in healthcare, ready to take on exciting challenges in brain imaging and AI.
Near-infrared spectroscopy (NIRS) is widely utilized in clinical and research settings to measure tissue oxygenation (StO₂), especially in the brain. While NIRS offers a non-invasive approach to understanding brain oxygenation, measurements in adults are often impacted by extracerebral tissues, like the skull and skin, which can interfere with obtaining an accurate value for brain oxygenation. Traditionally, NIRS data is analyzed through iterative algorithms based on diffuse optical models, which require substantial computational resources. In this project, we explore the potential of artificial neural networks (ANNs) to replace or complement these traditional analytical methods. ANNs, through their adaptive learning, may offer faster, efficient solutions while maintaining or even improving accuracy, thereby opening up new possibilities in brain oxygenation monitoring.
Join an innovative project to advance brain oxygenation measurement through cutting-edge neural network technology. You'll work on implementing and training artificial neural networks (ANNs) to improve both the speed and accuracy of reconstructing optical properties of the adult head. This involves enhancing the measurement of brain oxygenation by accurately differentiating between tissue layers, such as skin, skull, and brain, in NIRS data. Dive into computational models and machine learning to shape the future of non-invasive brain monitoring techniques.
Join an innovative project to advance brain oxygenation measurement through cutting-edge neural network technology. You'll work on implementing and training artificial neural networks (ANNs) to improve both the speed and accuracy of reconstructing optical properties of the adult head. This involves enhancing the measurement of brain oxygenation by accurately differentiating between tissue layers, such as skin, skull, and brain, in NIRS data. Dive into computational models and machine learning to shape the future of non-invasive brain monitoring techniques.
Develop a neural network approach for faster and more accurate brain oxygenation measurement in adults using NIRS data.
Develop a neural network approach for faster and more accurate brain oxygenation measurement in adults using NIRS data.
Please submit a short motivation letter and/or CV to: emanuele.russomanno@usz.ch or martin.wolf@usz.ch
Please submit a short motivation letter and/or CV to: emanuele.russomanno@usz.ch or martin.wolf@usz.ch
