Artificial intelligence for osteoporosis diagnostics
Researchers at Kiel University have developed software that automatically detects vertebral fractures on CT images and evaluates them prognostically.
The bone density of many people decreases as they get older. This process, which is known as osteoporosis, often goes unnoticed, even if there are fractures to the vertebral bodies. These fractures could be detected by means of X-rays or computed tomography (CT), but this does not always occur. For example, because the CT is done for a different reason and a vertebral fracture is overlooked in the stress of everyday life in the clinic. Researchers led by Professor Claus-Christian Glüer from the Section Biomedical Imaging at the Department of Diagnostic Radiology at the University Hospital Schleswig-Holstein (UKSH), Campus Kiel, and the Molecular Imaging North Competence Center (MOIN CC), have developed software to improve osteoporosis diagnostics. The program uses artificial intelligence (AI) methods to automatically detect indications of osteoporosis and prognostically unfavourable vertebral fractures on computer tomographies taken for a wide variety of reasons. The latest findings were recently presented by Eren Yilmaz, a doctoral candidate in the working group, at the "SPIE Medical Imaging" conference in San Diego, California, and published in the conference transcript Proceedings of SPIE (Society of Photo-Optical Instrumentation Engineers, SPIE). The work in the Kiel Life Science (KLS) priority research area at Kiel University (CAU) was funded by the ARTEMIS project from the Federal Ministry of Education and Research and KI-RAD from the Federal Ministry for Economic Affairs and Climate Action.
AI detects 9 out of 10 vertebral fractures in CT images
CT images of the chest are often taken to have a look at the lungs, for example. The spine can be seen on the picture but it is not checked, perhaps because the attention is on another problem. "Our program can run in the background during examinations like these. It automatically inspects the spine and gives an indication of any fractures of the vertebrae that might otherwise not have been detected," explains lead author Yilmaz. This is important because the presence of vertebral fractures significantly increases the risk of further breaks. The software works using what are known as neural networks. These are algorithms modelled on the way the human brain works, and are often used to recognise patterns. The AI was tested on 159 CT images of the spine, which came from seven hospitals in Germany. Experienced radiologists examined the images beforehand and discovered 170 fractures. "90 percent of the cases with fractures were correctly classified by the neural network, as well as 87 percent of vertebrae without fractures," Yilmaz reports.
In addition, the program is not only able to detect fractures, but also to distinguish between mild fractures (grade 1) and more severe ones (grade 2 or higher). "This diagnosis is crucial for estimating future fracture risks," says Yilmaz. It is particularly applicable to hip fractures, which are associated with a high reduction in quality of life and increased mortality, especially in old age. "We are thus developing an early warning system to prevent serious consequences of osteoporosis.” The technology is not yet available for general use in hospitals. However, it should be possible to use it for research purposes at least in the foreseeable future.
Original publication:
Eren B. Yilmaz, Tobias Fricke, Julian Laue, Constanze Polzer, Sam Sedaghat, Jan-Bernd Hövener, Claus-Christian Glüer, Carsten Meyer, "Towards fracture risk assessment by deep-learning-based classification of prevalent vertebral fractures," Proc. SPIE 12465, Medical Imaging 2023: Computer-Aided Diagnosis, 124651D (7 April 2023); https://doi.org/10.1117/12.2653526
More information:
Molecular Imaging North Competence Center (MOIN CC), Faculty of Medicine, CAU
www.moincc.de
Priority research area “Kiel Life Science”, CAU
Text: Kerstin Nees
Scientific contact:
Eren Yilmaz
Section Biomedical Imaging
Molecular Imaging North Competence Center (MOIN CC), Faculty of Medicine, CAU,
Department of Diagnostic Radiology, UKSH Campus Kiel,
+49 431- 500-15123
eren.yilmaz@rad.uni-kiel.de
About Kiel Life Science (KLS)
The interdisciplinary centre for applied life sciences – Kiel Life Science (KLS) – links research at the CAU from the fields of agricultural and nutritional sciences, the natural sciences and medicine. It forms one of four research focus areas at Kiel University, and is aimed at achieving a better understanding of the cellular and molecular processes with which organisms respond to environmental influences. The research is focussed on issues such as how agricultural crop plants adapt to specific growth conditions, or how illnesses can arise through the interaction of genes, individual lifestyle and environmental factors. Health is always viewed holistically in the context of evolution. Under the research focus’ name, there are currently around 80 scientists from 40 institutes and six faculties from Kiel University working collaboratively as full members.
2D sectional image of a CT scan showing two fractures. They were correctly classified by the AI as moderate (grade 2). The other vertebrae were correctly identified as "normal" (grade 0).
Computer scientist Eren Yilmaz is developing artificial intelligence methods for image recognition in his doctoral studies.
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