| EPSRC Reference: |
EP/Y01958X/1 |
| Title: |
AID-PitSurg: AI-enabled Decision support in Pituitary Surgery to reduce complications |
| Principal Investigator: |
Bano, Dr S |
| Other Investigators: |
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| Department: |
Computer Science |
| Organisation: |
UCL |
| Scheme: |
Standard Research - NR1 |
| Starts: |
02 October 2023 |
Ends: |
01 April 2025 |
Value (£): |
557,392
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| EPSRC Research Topic Classifications: |
| Artificial Intelligence |
Digital Signal Processing |
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Summary on Grant Application Form |
The pituitary is a small gland at the base of the brain that produces hormones that control several important bodily functions. Pituitary tumours are one of the most common types of brain tumours, where a symptomatic tumour can cause hormonal imbalances and other health problems. Transsphenoidal surgery is the gold standard treatment for most symptomatic pituitary tumours. This is a minimally invasive surgery as it is performed through the nostrils and nasal sinuses leaving no visible scars from the procedure.
Transsphenoidal surgery is challenging and high risk due to the narrow approach and proximity of critical neurovascular structures such as the optic nerves and carotid arteries, resulting in a relatively high rate of complications. The most common of these complications requiring medical or surgical treatment are dysnatraemia (related to pituitary dysfunction), and post-operative cerebrospinal fluid (CSF) rhinorrhoea (related to insufficient repair of the skull base). Thus, leading to increased hospitalization and recovery time with high risk of life-threatening conditions.
To reduce the risk of these complications, this research project aims to develop a real-time Artificial Intelligence (AI) assisted decision support framework that can understand the surgical procedure, predict surgical errors and identify intraoperative causes of complications. The AI model will recognise surgical steps, detect surgical instruments, and identify specific instrument-tissue interactions during the sellar phase (for dysnatraemia) and closure phase (for CSF rhinorrhoea) of the surgery. The framework will use multimodal data, including pre- and post-operative clinical data and surgical scene perception, to predict and alert the surgeon of any surgical errors and potential post-operative complications in real-time.
By developing this framework, the project aims to improve surgical outcomes by reducing the frequency of post-operative complications, shortening the length of hospital stays, and improving patients' recovery.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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