Endoscopy event recognition using machine learning
Lead Research Organisation:
University College London
Department Name: Medical Physics and Biomedical Eng
Abstract
1) Brief description of the context of the research including potential impact
An endoscopic procedure such as colonoscopy is defined by a series of events these include reaching the
caecum, withdrawal, detecting/diagnosing disease, resection and removal. The quality of a procedure is
measured by metrics related to these events e.g. % of procedures where caecum is reached, number of
polyps detected etc. There are multiple studies that show the quality of colonoscopy varies significantly
amongst doctors and that the recording and identification of these events is inconsistent.
The scope of this project is to explore machine learning to improve the detection and documentation of events in colonoscopy with the goal of improving the quality of procedures.
2) Aims and Objectives
The specific objectives are to:
Develop video-based methods that include temporal information for event detection.
3) Novelty of Research Methodology
This is a highly challenging topic that will address open research questions related to
temporal machine learning with small amounts of data
domain adaption for data generalization and
multi-task learning that can use data from other endoscopic events to improve performance.
4) Alignment to EPSRC's strategies and research areas
This project lies within the healthcare technology theme helping to accelerate translation within the scope of the following grand challenges:
Developing future therapies
Frontiers of physical intervention
Optimising treatment
Transforming community health and care
5) Any companies or collaborators involved
Odin Medical Limited
An endoscopic procedure such as colonoscopy is defined by a series of events these include reaching the
caecum, withdrawal, detecting/diagnosing disease, resection and removal. The quality of a procedure is
measured by metrics related to these events e.g. % of procedures where caecum is reached, number of
polyps detected etc. There are multiple studies that show the quality of colonoscopy varies significantly
amongst doctors and that the recording and identification of these events is inconsistent.
The scope of this project is to explore machine learning to improve the detection and documentation of events in colonoscopy with the goal of improving the quality of procedures.
2) Aims and Objectives
The specific objectives are to:
Develop video-based methods that include temporal information for event detection.
3) Novelty of Research Methodology
This is a highly challenging topic that will address open research questions related to
temporal machine learning with small amounts of data
domain adaption for data generalization and
multi-task learning that can use data from other endoscopic events to improve performance.
4) Alignment to EPSRC's strategies and research areas
This project lies within the healthcare technology theme helping to accelerate translation within the scope of the following grand challenges:
Developing future therapies
Frontiers of physical intervention
Optimising treatment
Transforming community health and care
5) Any companies or collaborators involved
Odin Medical Limited
Planned Impact
The critical mass of scientists and engineers that i4health will produce will ensure the UK's continued standing as a world-leader in medical imaging and healthcare technology research. In addition to continued academic excellence, they will further support a future culture of industry and entrepreneurship in healthcare technologies driven by highly trained engineers with deep understanding of the key factors involved in delivering effective translatable and marketable technology. They will achieve this through high quality engineering and imaging science, a broad view of other relevant technological areas, the ability to pinpoint clinical gaps and needs, consideration of clinical user requirements, and patient considerations. Our graduates will provide the drive, determination and enthusiasm to build future UK industry in this vital area via start-ups and spin-outs adding to the burgeoning community of healthcare-related SMEs in London and the rest of the UK. The training in entrepreneurship, coupled with the vibrant environment we are developing for this topic via unique linkage of Engineering and Medicine at UCL, is specifically designed to foster such outcomes. These same innovative leaders will bolster the UK's presence in medical multinationals - pharmaceutical companies, scanner manufacturers, etc. - and ensure the UK's competitiveness as a location for future R&D and medical engineering. They will also provide an invaluable source of expertise for the future NHS and other healthcare-delivery services enabling rapid translation and uptake of the latest imaging and healthcare technologies at the clinical front line. The ultimate impact will be on people and patients, both in the UK and internationally, who will benefit from the increased knowledge of health and disease, as well as better treatment and healthcare management provided by the future technologies our trainees will produce.
In addition to impact in healthcare research, development, and capability, the CDT will have major impact on the students we will attract and train. We will provide our talented cohorts of students with the skills required to lead academic research in this area, to lead industrial development and to make a significant impact as advocates of the science and engineering of their discipline. The i4health CDT's combination of the highest academic standards of research with excellent in-depth training in core skills will mean that our cohorts of students will be in great demand placing them in a powerful position to sculpt their own careers, have major impact within our discipline, while influencing the international mindset and direction. Strong evidence demonstrates this in our existing cohorts of students through high levels of conference podium talks in the most prestigious venues in our field, conference prizes, high impact publications in both engineering, clinical, and general science journals, as well as post-PhD fellowships and career progression. The content and training innovations we propose in i4health will ensure this continues and expands over the next decade.
In addition to impact in healthcare research, development, and capability, the CDT will have major impact on the students we will attract and train. We will provide our talented cohorts of students with the skills required to lead academic research in this area, to lead industrial development and to make a significant impact as advocates of the science and engineering of their discipline. The i4health CDT's combination of the highest academic standards of research with excellent in-depth training in core skills will mean that our cohorts of students will be in great demand placing them in a powerful position to sculpt their own careers, have major impact within our discipline, while influencing the international mindset and direction. Strong evidence demonstrates this in our existing cohorts of students through high levels of conference podium talks in the most prestigious venues in our field, conference prizes, high impact publications in both engineering, clinical, and general science journals, as well as post-PhD fellowships and career progression. The content and training innovations we propose in i4health will ensure this continues and expands over the next decade.
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
EP/S021930/1 | 30/09/2019 | 30/03/2028 | |||
2405083 | Studentship | EP/S021930/1 | 13/01/2020 | 12/01/2024 | Juana Gonzalez Bueno Puyal |