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Hannover Medical School​

Fights Chronic Lung Disease with Data
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Hannover Medical School​

Leading doctors are building data-driven diagnostics on NetApp to help improve patients’ lives

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.

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.

"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.

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.

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