The University Medical Center Hamburg-Eppendorf (UKE) and Royal Philips Electronics have developed a computer aided diagnosis (CAD) system to support clinicians in early diagnosis of neurodegenerative disease.
The new diagnostic technique has already proven its accuracy using historical image data and known patient outcomes, and is about to undergo clinical evaluation at UKE.
The CAD system is a software package that automatically interprets Positron Emission Tomography (PET) brain scans of patients suspected of having a neurodegenerative disease that leads to dementia, and combines them with Magnetic Resonance Imaging (MRI) scans for accurate differential diagnosis.
The development of such a system could mean a better quality of life for patients by enabling earlier prescription of drugs that delay progression of the disease.
It will also provide pharmaceutical companies and clinicians with a valuable tool for the development and testing of new, potentially curative drugs for neurodegenerative diseases such as Alzheimer’s.
As the demographics of world populations increasingly shift towards older age groups, dementia is widely expected to reach epidemic proportions unless effective treatments are found for it.
‘Building on our expertise in multi-modal diagnostic imaging, we’ve combined functional and structural brain-scan information into a fully integrated and easy to use system for diagnosing the principal neurodegenerative diseases that cause dementia,’ said Dr Lothar Spies, Head of the Digital Imaging Department at Philips Research. ‘Ultimately, it will enable early treatment and highly personalised therapies.’
The software tool developed by Philips Research and UKE accurately overlays anatomical images of the brain obtained from MRI scans with PET scans that display brain activity, specifically the uptake of glucose that fuels brain activity. By using advanced image processing and computer learning techniques in combination with a database of reference brain-scans, the system then analyses the images automatically and displays anomalous brain patterns in a concise way. Based on these patterns, it then suggests a diagnosis. As a result, the system will help less experienced doctors to achieve the same diagnostic accuracy as highly trained specialists.
The clinical evaluation that is about to start will run the computer aided diagnostic system alongside UKE’s existing dementia diagnosis procedures with the aim of fine-tuning the system’s ability to detect and differentiate the three most common types of neurodegenerative disease, Alzheimer’s disease, Lewy-body dementia and frontotemporal dementia.