Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Thomas A. Burge"'
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-9 (2023)
Abstract The aim of this study was to develop an automated pipeline capable of designing custom total knee replacement implants from CT scans. The developed pipeline firstly utilised a series of machine learning methods including classification, obje
Externí odkaz:
https://doaj.org/article/e0a8b6d0d0a24127a54663c07bd47f14
Autor:
Thomas A. Burge, Gareth G. Jones, Christopher M. Jordan, Jonathan R.T. Jeffers, Connor W. Myant
Publikováno v:
Frontiers in Bioengineering and Biotechnology, Vol 10 (2022)
Purpose: The aim of this study was to outline a fully automatic tool capable of reliably predicting the most suitable total knee replacement implant sizes for patients, using bi-planar X-ray images. By eliminating the need for manual templating or gu
Externí odkaz:
https://doaj.org/article/c0cb2a5f8d25435390494af0c0050fad
Autor:
Thomas A. Burge, Maxwell J. Munford, Stylianos Kechagias, Jonathan R. T. Jeffers, Connor W. Myant
Publikováno v:
The International Journal of Advanced Manufacturing Technology. 126:3725-3737
Publikováno v:
Journal of Medical Devices. 16
The objective of this study was to outline a fully automated, X-ray-based, mass-customization pipeline for knee replacement surgery, thoroughly evaluate its robustness across a range of demographics, and quantify necessary input requirements. The pip
Publikováno v:
Journal of Mechanical Design. 144
For standard “off-the-shelf” knee replacement procedures, surgeons use X-ray images to aid implant selection from a limited number of models and sizes. This can lead to complications and the need for implant revision due to poor implant fit. Cust
Publikováno v:
International Journal of Electronic Healthcare. 10:96
With the move towards more 'outcome' and 'value'-based treatment regimens – increasingly tailored for the individual patient – there is growing pressure on healthcare systems and the pharmaceutical sector to collaborate and co-develop innovative