NIHR i4i NIHR i4i

Summary

 

This was a collaborative project between the University of Manchester, the Central Manchester NHS Foundation Trust, and Optasia Medical Ltd. It was supported by an award of £806K from the Health Innovation Challenge Fund (grant no. HICF-R7-414/WT100936), a parallel funding partnership between the Department of Health and the Wellcome Trust. The project ran from 2013 to 2016, and the work is now continuing under the STOpFrac project.

NIHR i4i NIHR i4i NIHR i4i

Project Description

 

Unmet Need: Osteoporosis is a condition in which there is too little bone, and leads to affected patients suffering fractures, most commonly in the spine, wrist and hip. These lead to pain and defomity and often death. Osteoporosis affects 1 in 2 women and 1 in 5 men over age 50 years and the treatment of fractures will cost £2 billion in UK by 2020. Vertebral fractures are the most common fractures in osteoporosis, and if present indicate that the patient is at significantly increased risk of future fractures and should be treated. However, over 50% of vertebral fractures are not associated with symptoms and so their presence may not be suspected; and are often not reported if present on various imaging techniques.

Technology: We will develop a fully automated computer tool for identifying vertebral fractures on X-ray images, suitable for adoption within the NHS, building on our existing state-of-the-art image analysis methods. We will demonstrate the tool’s accuracy when applied in NHS radiology departments.

Impact: By identifying subjects with vertebral fractures who would benefit from referral for further assessment for osteoporosis the system should ultimately reduce the number of fractures, including the numbers of potentially fatal hip fractures.

Publications

 

Bromiley, P.A., Kariki, E.P., Adams, J.A. and Cootes, T.F.
Fully Automatic Localisation of Vertebrae in CT images using Random Forest Regression Voting.
Proc. Computational Methods and Clinical Applications for Spine Imaging: Fourth International Workshop and Challenge (CSI 2016).
Held in Conjunction with MICCAI 2016, Athens, Greece, 17th-21st October, 2016.
Runner-up: Best Paper Award

Kariki, E.P., Bromiley, P.A., Cootes, T.F., and Adams, J.A.
Opportunistic Identification of Vertebral Fractures on Computed Radiography: Need for Improvement.
National Osteoporosis Society Osteoporosis Conference 2016, Birmingham, U.K., 7th-9th November, 2016.
In: Osteoporosis International 27 (Supplement 2) p. 621, 2016.

Bromiley, P.A., Adams, J., and Cootes, T.F.
Automatic Localisation of Vertebrae in DXA Images using Random Forest Regression Voting.
Proc. Computational Methods and Clinical Applications for Spine Imaging: Third International Workshop and Challenge (CSI 2015). Held in Conjunction with MICCAI 2015, Munich, Germany, October 5, 2015. In LNCS: Image Processing, Computer Vision, Pattern Recognition, and Graphics, vol. 9402. Eds. Tomaž Vrtovec, Jianhua Yao, Ben Glocker, Tobias Klinder, Alejandro Frangi, Guoyan Zheng, and Shuo Li. Springer International Publishing, pp. 38-51, 2016.

Bromiley, P.A., Adams, J., and Cootes, T.F.
Localization of vertebrae on DXA VFA images using constrained local models with random forest regression voting.
Proc. 20th International Bone Densitometry Workshop (IBDW), Hong Kong, 13-17 October 2014. [Poster]
Winner: Best Poster Award

Bromiley, P.A., Adams, J., and Cootes, T.F.
Localisation of Vertebrae on DXA Images using Constrained Local Models with Random Forest Regression Voting.
Proc. MICCAI Workshop on Computational Methods and Clinical Applications for Spine Imaging (CSI 2014). Boston USA, 14 September 2014. In "Recent Advances in Computational Methods and Clinical Applications for Spine Imaging", Eds Jianhua Yao, Ben Glocker, Tobias Klinder, Shuo Li. Springer International Publishing Switzerland, Lecture Notes in Computational Vision and Biomechanics vol 20, 2015, p. 159-171.
Honorable Mention: Best Paper Award

Press

 

The project featured in articles in MedicalXpress, The Business Desk, News Medical, Science Daily, Health Canal, Medical News Today, MDLinx, The Engineer, The Daily Mail, and the web page of the International Society for Clinical Densitometry (ISCD). It also appeared in the MAHSC newsletter and as a case study on the MAHSC website.

Optasia Medical, our industrial collaborators, won the BioNow Healthcare Product of the year award for their work on the project.

ASPIRE™

 

The machine learning algorithms developed through this project are used in the AVERT software that our industrial collaborators Optasia Medical Ltd use in their ASPIRE™ service. The software is currently in use at the University of Manchester and the University of Sheffield. Potential users of the AVERT software or the ASPIRE™ service should contact Optasia Medical to discuss their requirements.

AVERT Software