Amogh Patil
Research Subjects
Research Interests
Amogh Patil has a total of 16 co-authors. Here are the top co-authors by number of shared publications:
- Amit Patel
Clinical and Investigative Orthopedics Surgery Unit, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, USA
1 shared publication - Anthony Michael James Bull
Imperial College London
1 shared publication - Batya Stimler
Barts Bone & Joint Health, The Royal London Hospital, Whitechapel Rd, London, E1 1FR United Kingdom
1 shared publication - Charmilie Chandrakumar
Department of Orthopaedics, King’s College Hospital NHS Foundation Trust, SE5 9RS London, UK
1 shared publication Francesc Malagelada Romans
Barts Bone & Joint Health, The Royal London Hospital, Whitechapel Rd, London, E1 1FR United Kingdom
1 shared publicationGareth G. Jones
The MSk Lab Imperial College London London UK
1 shared publication
Latest Publications
- Open Access
- 5 February 2026
- Published by IMR Press in British Journal of Hospital Medicine
- Vol. 87 (2) , 50723
Less than Full Time Training—A Survey of Prospective Trauma&Orthopaedic Surgeons in the UK—Understanding Prospective Trauma&Orthopaedic Surgeons’ Attitudes Towards Less than Full Time Training
Aims/Background: This study explores the attitudes and barriers faced by UK orthopaedic trainees regarding less than full time (LTFT) training. LTFT training has become more accessible since 2022, yet surgical trainees have been slower to adopt it compared to other specialties. Methods: A cross-sectional, questionnaire-based study was carried out via a 16-item online questionnaire which was distributed to aspiring orthopaedic surgeons registering for an orthopaedic themed teaching programme. Results: 216 respondents met the inclusion criteria. The study found that 42% of respondents would consider LTFT training, with higher interest among female trainees. Key barriers identified included extended training duration, financial concerns, potential decline in surgical skills, and negative perceptions from colleagues. Conclusion: The research highlights the need for clearer guidance, better support, and a cultural shift within orthopaedic departments to address these barriers. Initiating education and awareness programmes for senior staff and providing comprehensive information to trainees are essential steps to fostering a more inclusive and supportive environment for LTFT training in orthopaedic surgery.
- 5 February 2026
- Published by IMR Press in British Journal of Hospital Medicine
- Vol. 87 (2) , 50723
Open AccessLess than Full Time Training—A Survey of Prospective Trauma&Orthopaedic Surgeons in the UK—Understanding Prospective Trauma&Orthopaedic Surgeons’ Attitudes Towards Less than Full Time Training
Aims/Background: This study explores the attitudes and barriers faced by UK orthopaedic trainees regarding less than full time (LTFT) training. LTFT training has become more accessible since 2022, yet surgical trainees have been slower to adopt it compared to other specialties. Methods: A cross-sectional, questionnaire-based study was carried out via a 16-item online questionnaire which was distributed to aspiring orthopaedic surgeons registering for an orthopaedic themed teaching programme. Results: 216 respondents met the inclusion criteria. The study found that 42% of respondents would consider LTFT training, with higher interest among female trainees. Key barriers identified included extended training duration, financial concerns, potential decline in surgical skills, and negative perceptions from colleagues. Conclusion: The research highlights the need for clearer guidance, better support, and a cultural shift within orthopaedic departments to address these barriers. Initiating education and awareness programmes for senior staff and providing comprehensive information to trainees are essential steps to fostering a more inclusive and supportive environment for LTFT training in orthopaedic surgery.
- Open Access
- 10 February 2023
- Published by Wiley in The International Journal of Medical Robotics and Computer Assisted Surgery
- Vol. 19 (3) , e2503
The accuracy of statistical shape models in predicting bone shape: A systematic review
Background: This systematic review aims to ascertain how accurately 3D models can be predicted from two‐dimensional (2D) imaging utilising statistical shape modelling.Methods: A systematic search of published literature was conducted in September 2022. All papers which assessed the accuracy of 3D models predicted from 2D imaging utilising statistical shape models and which validated the models against the ground truth were eligible.Results: 2127 papers were screened and a total of 34 studies were included for final data extraction. The best overall achievable accuracy was 0.45 mm (root mean square error) and 0.16 mm (average error).Conclusion: Statistical shape modelling can predict detailed 3D anatomical models from minimal 2D imaging. Future studies should report the intended application domain of the model, the level of accuracy required, the underlying demographics of subjects, and the method in which accuracy was calculated, with root mean square error recommended if appropriate.
- 10 February 2023
- Published by Wiley in The International Journal of Medical Robotics and Computer Assisted Surgery
- Vol. 19 (3) , e2503
Open AccessThe accuracy of statistical shape models in predicting bone shape: A systematic review
Background: This systematic review aims to ascertain how accurately 3D models can be predicted from two‐dimensional (2D) imaging utilising statistical shape modelling.Methods: A systematic search of published literature was conducted in September 2022. All papers which assessed the accuracy of 3D models predicted from 2D imaging utilising statistical shape models and which validated the models against the ground truth were eligible.Results: 2127 papers were screened and a total of 34 studies were included for final data extraction. The best overall achievable accuracy was 0.45 mm (root mean square error) and 0.16 mm (average error).Conclusion: Statistical shape modelling can predict detailed 3D anatomical models from minimal 2D imaging. Future studies should report the intended application domain of the model, the level of accuracy required, the underlying demographics of subjects, and the method in which accuracy was calculated, with root mean square error recommended if appropriate.