Clinical Academic Training

Older people and complex health needs

Deep learning techniques to improve clinical phenotyping of musculo-skeletal deformity

  • Rob Ashford
  • Anna Peek
  • Harvinder Singh
  • Jit Mangwani

Musculoskeletal conditions, congenital, developmental or degenerative, form a significant burden of disease worldwide, causing deformity and functional loss(1). The analysis of deformity and functional loss involves complex data which is often difficult to accurately quantify. There is increasing interest in utilising Deep Learning Computer analytics, where the multiple factors in the data (2D shape, 3D shape, speed of movement) can be assessed concurrently to improve the understanding of the disease or deformity.

The Orthopaedic Department at University Hospitals of Leicester has established links with Leicester University Computer Science department who have a strong track record in the field of bioinformatics using Deep Learning techniques to analyse complex data (2-4).

The ACF will join an Orthopaedics department very active in research recruiting for a number of NIHR portfolio studies. Prof Singh is currently co-applicant on the NIHR funded HUSH study (5) and has supervised previous MSc students. Prof Ashford was CI for the multi-centre study “Genetic profile and telomere characteristics and of high-grade soft tissue sarcomas” and has supervised previous ACF appointees within the department as well as PhD and MD candidates. Mr Mangwani is a co-applicant on NIHR commissioned multi-centre study FAME (6). He is supervisor for MSc students at The RCSurg of Edinburgh and Keele University. The Training Programme Directors of  core and higher surgical training are supportive.

The aim of the projects will be to analyse deformity and functional loss using advances in analytics deep learning techniques. This will promote more accurate elucidation of deformity to allow further research into outcomes and treatments.


  • Global, regional, and national burden of other musculoskeletal disorders 1990-2017: results from the Global Burden of Disease Study 2017. Safiri S, Kolahi AA, Cross M, Carson-Chahhoud K, Almasi-Hashiani A, Kaufman J, Mansournia MA, Sepidarkish M, Ashrafi-Asgarabad A, Hoy D, Collins G, Woolf AD, March L, Smith E.Rheumatology (Oxford). 2021 Feb 1;60(2):855-865. doi: 10.1093/rheumatology/keaa315
  • V. Rajinikanth, H. Lin, J. Panneerselvam and N.S.M. Raja, Examination of Retinal Anatomical Structures – A Study with Spider Monkey Optimization Algorithm, Applied Nature Inspired Computing: Algorithms and Case Studies, pp. 177-197, Springer, 2020.
  • V. Rajinikanth, N. Dey, R. Kumar, J. Panneerselvam and N. S. M. Raja, Fetal Head Periphery Extraction from Ultrasound Image using Jaya Algorithm and Chan-Vese Segmentation, Procedia Computer Science, pp. 66-73, vol. 152, 2019.
  • A. Miller, J. Panneerselvam, L. Liu, A Review of Regression and Classification Techniques for Analysis of Common and Rare Variants and Gene-Environmental Factors, IEEE Transactions on Computational Biology and Bioinformatics. (under review)
  • HUSH
  • FAME

Medical Education

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