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HANNOVER MEDICAL SCHOOL FIGHTS CHRONIC LUNG DISEASE WITH DATA

AI models trained on large datasets allow for faster and better detection and treatment of chronic diseases. Learn why leading doctors are building data-driven diagnostics on NetApp to help improve patients’ lives.


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Overview:

Data Visionaries at Hannover Medical School (MHH) use data to:

  • Support human experience with AI insights
  • Minimize diagnostic errors for better results
  • Streamline diagnosis to be much faster and less costly
  • Improve patients’ quality of life


What may be a simple cough today can become a chronic lung disease over time. In fact, at least 250 million people suffer already from Chronic Obstructive Pulmonary Disease or COPD. It affects every breath and causes 400,000 deaths per year in the United States alone. And the number will only continue to grow around the globe.

Currently, the destructive effects of COPD are irreversible. Early detection and constant monitoring are vital for treatment and have been proven to slow the progression of the disease. Dr. Hinrich Winther, resident, Institute for Diagnostic and Interventional Radiology, MHH, is at the forefront of COPD research, working to simplify and accelerate COPD diagnostic processes using the power of AI.

“We have novel imaging techniques for improved detection and monitoring in COPD patients,” said Prof. Dr. med. Jens Vogel-Claussen, senior radiologist, Institute for Diagnostic and Interventional Radiology, MHH. “However, the time-consuming manual segmentation of the lungs is a real bottleneck and cost factor in the diagnostic process.” Segmentation is used to mark and quantify a lung’s perfusion and indicates the severity of COPD. Historically, it took a day to evaluate a single case from beginning to end. It also required highly skilled medical doctors to manually turn image data into clinically relevant information.

Building a data-driven pipeline for better prognosis and faster diagnostics

A data infrastructure that can handle massive data sets helped Dr. Winther apply an AI model to automate the segmentation and cut hours to minutes.

“NetApp was immensely valuable in providing performance and reliability to gain sustained flows of data. It perfectly matches our compute power and, thus, allows us to improve the image quality, accelerate the data pipeline, and run five times more data in the same time,” said Dr. Winther.

Matching human experience with machine findings

The algorithm also proved to work well with organs that are less rigid in shape—like lungs, which change their form with every breath. The results have been highly promising. The medical findings from the automated process are nearly identical to the manual evaluation.

“Because of NetApp technology, we can now objectively quantify in roughly five minutes which part of a lung has diminished blood circulation and is damaged,” said Dr. Winther. “Using AI can help more patients with faster diagnostics and can improve their quality of life.”

The method has the potential to reduce time-consuming manual tasks, minimize human error, and bring down costs. Furthermore, it has the ability to scale case handling.

Data does not only drive detection, diagnosis, and treatment. Data can definitely help to improve the quality and efficacy of global healthcare while providing access to more people than ever.

Learn how NetApp helps Hannover Medical School.

Hannover Medical School focuses on medical education, research, and patient care. Its hospital is classified for maximum care. The German university teaches medicine, dentistry, biochemistry, biomedicine, midwifery, and health sciences. Its main research addresses transplantation and stem cell/regenerative medicine, infection and immunology, biomedical engineering, and implants.

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