MedTech

Deep Learning based detection of hypoxic-ischemic encephalopathy

In cooperation with a few friends from different departments at the University Hospital Charité in Berlin, I am working on a pioneering software focused on the development of an advanced deep-learning model for the detection of hypoxic-ischemic encephalopathy (HIE) after cardiac arrest on CT scans. Our aim is to revolutionize the diagnosis and characterization of HIE through the application of artificial intelligence.

In a first innovative study, we conducted a comprehensive retrospective analysis of head CT scans obtained from patients who experienced cardiac arrest. The scans were meticulously classified into two categories: severe HIE and no signs of HIE. To train and validate our deep learning models, we employed a division of the images into a training set and a test set. Multiple deep learning models, based on Densely Connected Convolutional Networks, were evaluated using different image input formats, including both 2D and 3D representations.

The results of our research are highly promising, with all optimized networks exhibiting the ability to detect signs of HIE. Notably, our models based on 2D image stacks demonstrated particularly encouraging performance metrics. We also incorporated visual explainability data using Gradient-weighted Class Activation Mapping to enhance transparency and provide insights into the decision-making process of our AI model.

This proof-of-concept deep learning model represents a significant advancement in the field of HIE detection. By accurately identifying signs of HIE on CT images, our research has the potential to significantly impact clinical practice. The exceptional performance of our 2D image stack models suggests exciting prospects for the implementation of this technology in routine clinical settings, empowering healthcare professionals to enhance their characterization of imaging data and make more informed predictions regarding patient outcomes.

As we continue to validate our findings and refine our deep learning model, we foresee a future where CT-based HIE detection becomes an integral part of clinical routine. Our research has the potential to revolutionize the diagnostic landscape, providing healthcare professionals with advanced tools to improve patient care and prognosis.

Whole-Body Hyperthermia

Whole-Body Hyperthermia (WBH) is a therapeutic approach that involves elevating the body's temperature to promote healing and improve overall health. It has a fascinating history that can be traced back to ancient civilizations, where heat was used as a remedy for various ailments. The practice has evolved, with advancements in technology allowing for more precise temperature control and better patient safety. 

State-of-the-art medical devices for WBH treatments utilize only water-filtered infrared-A radiation, which plays a pivotal role in achieving therapeutic outcomes. This specialized form of infrared-A radiation provides a controlled and targeted heat source that penetrates deep into the body, triggering a cascade of physiological responses. These responses include increased blood flow, improved tissue oxygenation, enhanced endocrinological and metabolic functions, as well as boosting of the immune system and the speeding up of nerve stimulus transmission.

As part of my work at the Von Ardenne Institute of Applied Medical Research, I was leading the clinical research and technical development of a medical device that uses water-filtered infrared-A radiation for WBH treatments.