CHALLENGE
The early detection of lung cancer remains a critical challenge in medical diagnostics. With the current CT-based screening, only a few percent of those at high risk actually receive the potentially life saving screening. Plus, CT is expensive and exposes patients to harmful radiation. There's a need for a rapid, cost-effective, and less invasive diagnostic tool that can operate in real-time to improve patient outcomes.
SOLUTION
A partnering University in Texas has initiated a groundbreaking study utilizing an innovative point-of-care (PoC) device based on electrochemistry that is powered by our AI platform. This device can analyze patient breath samples to detect lung cancer, focusing on eight specific biomarkers (so called Volatile Organic Compounds). This approach aims to provide a quicker, more affordable, and efficient diagnostic method that can be used at the point of care by General Practitioners and Lung Doctors, allowing to make better informed decisions already in the first patient visit.
EXPECTED IMPACT
The development of a portable, accurate PoC device for lung cancer detection has the potential to revolutionize cancer diagnostics. By providing near-instantaneous results and reducing costs, this technology could significantly improve early detection rates, patient experiences, and ultimately, survival rates. The study's success could also pave the way for similar diagnostic tools for other diseases, including other cancer types, autoimmune, neurodegenerative, and infectious diseases.
FUTURE DIRECTIONS
Building on the initial findings, we will refine the device further, increasing its biomarker range and accuracy—at the core of all this is Alchemy AI, our powerful explainable multimodal AI platform. Future studies will aim to validate the technology across a broader patient population and explore its application in detecting other forms of cancer or diseases.
COLLABORATION
This case study underscores the importance of interdisciplinary collaboration, empowering our clinical partner to unlock their medical expertise for advanced, AI-driven innovation. It highlights how academic research and practical AI application can converge to create truly impactful healthcare solutions.
CHALLENGE
The increasing complexity and variability of municipal waste feedstocks present significant operational challenges for biogas reactor management. To optimize efficiency and productivity, a sophisticated, data-driven approach is required that can predict and manage the nuances of biogas reactor operations.
SOLUTION
We have embarked on a collaborative project with the Technical University of Berlin to develop an explainable machine learning (ML) model, AlchemyAI, aimed at enhancing biogas reactor management. This initiative involves a systematic process of data selection, model development, and user interaction analysis, with the goal of improving reactor productivity and operational efficiency. The project will utilize existing data to tailor the ML model to address specific operational challenges, employing a user-centric approach to ensure the model's outcomes are aligned with the needs and expectations of reactor operators.
EXPECTED IMPACT
FUTURE DIRECTIONS
The project sets the stage for ongoing advancements in biogas reactor management. Future research will extend the developed methodologies to address additional operational challenges, with the aim of continuously refining the model's accuracy and utility in real-world applications.
COLLABORATION
This case study exemplifies the synergy between academic research and industry innovation, showcasing how collaborative efforts can lead to significant advancements in sustainable energy management and operational efficiency in biogas reactors.
Theta Diagnostics USA
800 West Campbell Road, Richardson, Texas 75080, United States
Represented in Europe by Halitus GmbH
Schlesische Str. 29/30, Aufgang M, 3. Obergeschoss, 10997 Berlin
Copyright © 2022-2024 Theta Diagnostics, LLC - All Rights Reserved.