Vertebral fractures (VFs) in adults over 50 years lead to reduced quality life, increased mortality and are strongly associated with osteoporosis and the risk of further fractures. While older adults with fractures at other sites such as the hip, shoulder, and wrist present to acute services for care and can be readily identified to be checked and managed for osteoporosis, most patients with vertebral fractures remain undiagnosed and go on to have further fractures. We know now around 10% of all CT scans that include the thoracic or lumbar spine will demonstrate vertebral fractures but less than 50% are reported and less than 10% lead to improvement in patient care. A typical large hospital will have 50,000 to 150,000 CT scans per year in this age group and it is not possible to re-read these scans manually to find those with a VF. Artificial intelligence (AI) algorithms offer the opportunity to efficiently review 100,000 CTs scan rapidly to identify those with probable VF and flag these patients for more detailed clinical care. However, there is no evidence for the scale of benefits to patients and healthcare systems if a hospital uses AI to do this. This information is needed to justify the costs from using the AI and associated increase in patient assessments and treatments. The ADOPT study has been set up with an NIHR grant to better understand the implementation challenges, clinical and cost effectiveness for using AI in the fracture setting. ADOPT has successfully implemented AI into 3 NHS hospitals to detect VFs and refer the patients for osteoporosis care and will describe the expected number of fractures avoided using the number of patients identified and recommended treatment using health economic models. This DPhil will extend follow-up of these patient to compare the expected with the observed clinical and cost effectiveness using a matched historical control cohort within existing ethical approval. The specific objectives are: Systematic review of the clinical and cost benefits of Artificial Intelligence of Vertebral fracturesImpact of AI on observed vs expected clinical outcomes using historical controls in Fracture Liaison Service (FLS) setting: 1 to 3 years follow-up Impact of AI on observed vs expected cost effective outcomes using historical controls in FLS setting: 1 to 3 years follow-up Description of barriers / challenges/ threats for sustained adoption of AI-enabled FLS in the NHS setting from perspectives of patients, clinicians and commissioners. This DPhil project offers a unique opportunity to link AI methodologies with the integration of patient reported outcomes, hospital data sources including health economic data and implementation barriers to address a major challenge facing the NHS and healthcare systems across the globe. Internal supervisorsMK Javaid Kassim Javaid — Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (ox.ac.uk)R Pinedo-Villanueva Rafael Pinedo Villanueva — Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (ox.ac.uk)TrainingThe Botnar Research Centre plays host to the University of Oxford's Institute of Musculoskeletal Sciences, which enables and encourages research and education into the causes of musculoskeletal disease and their treatment. Training will be provided in techniques including: Biostatistics and epidemiology Health economics Vertebral fracture identification Ethical, information governance and IT considerations for the use of Artificial intelligence in the routine health care Qualitative methodsImplementation scienceA core curriculum of lectures will be taken in the first term to provide a solid foundation in a broad range of subjects including musculoskeletal biology, inflammation, epigenetics, translational immunology, data analysis and the microbiome.  Students will also be required to attend regular seminars within the Department and those relevant in the wider University. Students will be expected to present data regularly in Departmental seminars and to attend external conferences to present their research globally, with limited financial support from the Department.Students will also have the opportunity to work closely with the Pharmaco- and Device Epidemiology Group. Pharmaco- and Device epidemiology — Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (ox.ac.uk)Students will have access to various courses run by the Medical Sciences Division Skills Training Team and other Departments. All students are required to attend a 2-day Statistical and Experimental Design course at NDORMS and run by the IT department (information will be provided once accepted to the programme). How to applyPlease contact the relevant supervisor(s), to register your interest in the project, and the departmental Education Team ([email protected]), who will be able to advise you of the essential requirements for the programme and provide further information on how to make an official application.Interested applicants should have, or expect to obtain, a first or upper second-class BSc degree or equivalent in a relevant subject and will also need to provide evidence of English language competence (where applicable). The application guide and form is found online and the DPhil or MSc by research will commence in October 2025. Applications should be made to one of the following programmes using the specified course code:D.Phil in Musculoskeletal Sciences (course code: RD_ML2)MSc by research in Musculoskeletal Sciences (course code: RM_ML2)For further information, please visit http://www.ox.ac.uk/admissions/graduate/applying-to-oxford. The Botnar Institute is a proud supporter of the Academic Futures scholarship programme, designed to address under-representation and help improve equality, diversity and inclusion in our graduate student body. The Botnar and the wider University rely on bringing the very best minds from across the world together, whatever their race, gender, religion or background to create new ideas, insights and innovations to change the world for the better. Up to 50 full awards are available across the three programme streams, and you can find further information on each stream on their individual tabs (Academic futures | Graduate access | University of Oxford).