Artificial polymer-based neural network. The strongly nonlinear behavior of these networks enables their use in reservoir computing.
CREDIT: TU Dresden
Artificial intelligence (AI) will fundamentally change medicine and healthcare:
Diagnostic patient data, e.g. from ECG, EEG or X-ray images, can be analyzed with the help of machine learning, so that diseases can be detected at a very early stage based on subtle changes. However, implanting AI within the human body is still a major technical challenge. TU Dresden scientists at the Chair of Optoelectronics have now succeeded for the first time in developing a bio-compatible implantable AI platform that classifies in real time healthy and pathological patterns in biological signals such as heartbeats. It detects pathological changes even without medical supervision. The research results have now been published in the journal ‘Science Advances’.
In this work, the research team led by Prof. Karl Leo, Dr. Hans Kleemann and Matteo Cucchi demonstrates an approach for real-time classification of healthy and diseased bio-signals based on a biocompatible AI chip. They used polymer-based fiber networks that structurally resemble the human brain and enable the neuromorphic AI principle of reservoir computing. The random arrangement of polymer fibers forms a so-called “recurrent network,” which allows it to process data, analogous to the human brain. The nonlinearity of these networks enables to amplify even the smallest signal changes, which – in the case of the heartbeat, for example – are often difficult for doctors to evaluate. However, the nonlinear transformation using the polymer network makes this possible without any problems.
In trials, the AI was able to differentiate between healthy heartbeats from three common arrhythmias with an 88% accuracy rate. In the process, the polymer network consumed less energy than a pacemaker. The potential applications for implantable AI systems are manifold: For example, they could be used to monitor cardiac arrhythmias or complications after surgery and report them to both doctors and patients via smartphone, allowing for swift medical assistance.
“The vision of combining modern electronics with biology has come a long way in recent years with the development of so-called organic mixed conductors,” explains Matteo Cucchi, PhD student and first author of the paper. “So far, however, successes have been limited to simple electronic components such as individual synapses or sensors. Solving complex tasks has not been possible so far. In our research, we have now taken a crucial step toward realizing this vision. By harnessing the power of neuromorphic computing, such as reservoir computing used here, we have succeeded in not only solving complex classification tasks in real time but we will also potentially be able to do this within the human body. This approach will make it possible to develop further intelligent systems in the future that can help save human lives.”
Original Article: Using artificial intelligence for early detection and treatment of illnesses: TU Dresden researchers develop an implantable AI system
More from: TU Dresden
The Latest Updates from Bing News & Google News
Go deeper with Bing News on:
Implantable AI platform
- The top stories out of Heart Rhythm Society 2024
Once again, the top players in cardiac care gathered en masse as Heart Rhythm Society (HRS) 2024 took place in Boston last week.
- The Rise Of AI-Infused Application Generation Platforms
Between these two extremes, the rapid advancements in TuringBots (AI tools aiding the varied tasks of the software development life cycle) and low-code platforms point to a more realistic future for ...
- Implicity receives US FDA 510(k) clearance for new heart failure prediction algorithm, SignalHF
Implicity receives US FDA 510(k) clearance for new heart failure prediction algorithm, SignalHF: Cambridge, Massachusetts Monday, May 20, 2024, 16:00 Hrs [IST] Implicity, a leader ...
- ClearML Announces AI Infrastructure Orchestration and Compute Management
Open source AI platform company ClearML today announced the release of an AI orchestration and compute management capabilities, making it the first AI platform to support Kubernetes, Slurm, PBS and ...
- Philips presents study results at Heart Rhythm Annual Meeting demonstrating benefits of its AI-powered cardiac monitoring solutions
Three studies demonstrate how Philips MCOT wearable ambulatory monitoring ECG and proprietary AI models applied to ECG digital ...
Go deeper with Google Headlines on:
Implantable AI platform
[google_news title=”” keyword=”implantable AI platform” num_posts=”5″ blurb_length=”0″ show_thumb=”left”]
Go deeper with Bing News on:
Reservoir computing
- Predicting Chaos With AI: The New Frontier in Autonomous Control
Recent research highlights the development of advanced machine learning algorithms capable of controlling complex systems efficiently. These new algorithms, tested on digital twins of chaotic ...
- Optimizing Machine Learning Controllers with Digital Twins
How can machine learning be improved to provide better efficiency in the future? This is what a recent study published in Nature Communications hopes to ad | Technology ...
- Revolutionary AI Device Mimics Human Brain With Few-Molecule Computing
Progress in developing compact AI devices using molecular vibrations and confirming their functionality A collaborative research team from NIMS and Tokyo University of Science has successfully develop ...
- Best printer deals: 10+ cheap printers on sale as low as $79
Reservoir or Tank printers like the Epson EcoTank ET-2800 have a reservoir of ink in various colors, which you can refill if and when you need to. This one, in particular, comes with four ...
- New machine learning algorithm promises advances in computing
Systems controlled by next-generation computing algorithms could give rise to better and more efficient machine learning products, a new study suggests.
Go deeper with Google Headlines on:
Reservoir computing
[google_news title=”” keyword=”nano factory” num_posts=”5″ blurb_length=”0″ show_thumb=”left”]