Experts have created a new artificial intelligence (AI) tool that dramatically speeds up and improves the accuracy of brain tumour imaging.
Developed by a research team from University College London (UCL) and University College London Hospitals (UCLH), this innovation will transform treatment planning.
It offers a more personalised approach, helping surgeons make better decisions during and after surgery.
The AI tool, which has undergone rigorous clinical validation, promises substantial benefits for neurosurgeons by delivering rapid and precise imaging analysis.
The tool supports intraoperative and postoperative decision-making, streamlining surgical workflows and improving patient outcomes.
One of the most striking features of the AI tool is its efficiency. It can analyse brain tumour scans in just three seconds, a remarkable improvement over the five minutes it typically takes experienced neuroradiologists.
This drastic reduction in analysis time could transform surgical workflows, particularly in high-stakes, time-sensitive cases, allowing surgeons to make faster, data-driven decisions.
The AI system also offers improved predictive capabilities, delivering more accurate treatment outcome predictions than traditional methods.
This enhanced accuracy enables surgeons to better anticipate patient responses to surgery and hone tailored interventions, leading to more personalised and effective care strategies.
Implementing the AI tool could also bring substantial financial benefits. Research suggests that its national deployment could save the UK’s NHS over £1.5 million within three years.
Increasing efficiency and accuracy in neuro-oncology practices offers a high-value, low-cost addition to current clinical workflows.
The tool addresses a common challenge in brain tumour treatment: patient imaging variability.
With more than 100,000 people in the UK currently living with brain tumours, this variability often complicates treatment decisions. The AI tool enhances neuroradiologists’ ability to identify tumour characteristics precisely, providing surgeons with detailed information on tumour location, size, and proximity to critical structures.
The tool has been clinically validated through tests on 1,172 patients, demonstrating its accuracy across different age groups and genders.
This broad applicability gives neurosurgeons confidence in the AI’s reliability throughout diagnostic and therapeutic treatment phases.
Lead researcher Dr James Ruffle emphasised the potential of the AI tool to personalise patient care at no extra cost, aligning with the growing trend toward precision surgery. He said: ‘This tool supports data-driven decision-making, enabling surgeons to make more informed choices throughout the patient’s treatment journey.’
Dr Harpreet Hyare, senior neuroradiologist at UCLH, added that the AI’s ability to quantify tumour characteristics objectively enhances treatment planning.
She said: ‘For surgeons, this means better preoperative planning and postoperative monitoring, providing more targeted interventions at critical points.’
The Medical Research Council, the Wellcome Trust, and the NIHR UCLH Biomedical Research Centre funded the development of the AI tool.
The National Brain Appeal further supported translational efforts.
