̽��ֱ�� of Cambridge - Raj Jena

Cambridge's Raj Jena becomes UK's first Professor of AI in radiotherapy

sb726 — Thu, 17 Oct 2024 08:00:31 +0000

̽��ֱ��creation of the new Clinical Professorship signals the importance of AI in the fight against cancer and builds on the ̽��ֱ��’s work to apply the latest technology to the world’s major challenges.

AI versus cancer - the Cambridge researchers using machine intelligence to beat disease

sb726 — Mon, 15 Jul 2024 07:00:31 +0000

‘Game changing’ technology which can predict how patients will respond to cancer treatment is part of a wave of Cambridge research harnessing AI to fight the disease.

Cancer is the public's biggest health concern, survey finds

cjb250 — Mon, 15 Jul 2024 07:00:29 +0000

Two-thirds of the public say they are very or somewhat worried about being told they have cancer – higher than for any other medical condition – according to polling released today.

A very healthy relationship: the ̽��ֱ�� and the NHS

cjb250 — Mon, 03 Jul 2023 15:40:21 +0000

As the NHS celebrates its 75th anniversary, we look at how the close relationship between the ̽��ֱ�� and the hospitals on its doorstep is driving major improvements in how we care for patients.

AI cuts waiting times for cancer patients in NHS first

Anonymous — Tue, 27 Jun 2023 08:00:18 +0000

'OSAIRIS' is saving many hours of doctors’ time in preparing scans and helping to cut the time patients have to wait between referral for radiotherapy and starting treatment.

Working alongside this AI technology, specialists can plan radiotherapy treatments approximately two and half times faster than if they were working alone, ensuring more patients can get treatment sooner and improving the likelihood of cure.

̽��ֱ��technology is currently being used at Addenbrooke’s for prostate and head and neck cancers, but has the potential to work for many other types of cancer, benefitting patients across the NHS.

Dr Raj Jena, oncologist at Cambridge ̽��ֱ�� Hospitals NHS Foundation Trust, led the research for the NHS and ̽��ֱ�� of Cambridge. His research includes long- term collaborations with Microsoft Research on an AI research project known as Project InnerEye to develop machine learning techniques to support the global medical imaging community. To broaden access to research in this field, Microsoft Research made available Project InnerEye toolkits as open-source software.

With a £500,000 grant from the NHS AI Lab, Dr Jena’s team created a new AI tool, OSAIRIS, using open-source software from Project InnerEye and data from patients who had previously been treated in the hospital and agreed to contribute to the research.

OSAIRIS works by significantly cutting the amount of time a doctor needs to spend drawing around healthy organs on scans before radiotherapy. Outlining the organs, known as ‘segmentation’, is critical in order to protect the healthy tissue around the cancer from radiation. It can take a doctor between 20 minutes and three hours to perform this task, per patient. This complex but routine task is ideally suited to AI with the oncologist in control, checking every scan after OSAIRIS has done the segmentation.

Rigorous tests and risk assessments have been carried out to ensure OSAIRIS is safe and can be used in the day-to-day care of radiotherapy patients across the NHS. In masked tests, known as ‘Turing tests’, doctors were unable to tell the difference between the work of OSAIRIS and the work of a doctor colleague.

Dr Raj Jena said: “OSAIRIS does much of the work in the background so that when the oncologist sits down to start planning treatment, most of the heavy lifting is done. It is the first cloud-based AI technology to be developed and deployed within the NHS. 18 months of rigorous testing will enable us to share this technology safely across the NHS for patient benefit.”

“We’ve already started to work on a model that works in the chest, so that will work for lung cancer and breast cancer particularly,” he explains. “And also, from my perspective as a neurooncologist, I’m interested that we’re building the brain model as well so that we’ve got something that works for brain tumours as well.”

Aditya Nori, General Manager of Healthcare for Microsoft Research, said: “By combining the power of AI with the world-class clinical expertise of the NHS, we have an amazing opportunity for revolutionising healthcare together, while preserving the human element that is the essence of high-quality and safe care.”

“Healthcare offers the possibility not only to have technical impact but also societal impact, so I am really thrilled about this. ̽��ֱ��fact that we have AI finally in the NHS will also open the doors for other kinds of AI technologies to really reduce the burden that’s placed on clinicians, and more importantly, improve patient safety, outcomes, and experiences.”

Health and Social Care Secretary Steve Barclay said: “Cutting edge technology can help us reduce waiting times for cancer patients, free up time for staff so they can focus on patient care, and ultimately save lives – and artificial intelligence is playing an increasingly important role.

“Backed by £500,000 in government funding, the team at Addenbrooke’s Hospital are utilising the innovative OSAIRIS tool to speed up radiotherapy scans at more than twice the normal rate - reducing the time it takes to start potentially life-saving treatment.

“It will also help ease the pressure on the NHS and cut waiting lists, one of the government’s five priorities.

“In the year the NHS turns 75 we are investing in its future and last week announced a new £21 million fund for Trusts to deploy AI tools in a safe and controlled way to speed up the diagnosis and treatment for a range of conditions.”

Artificial intelligence developed by and for the NHS at Addenbrooke’s Hospital in Cambridge is reducing the amount of time cancer patients wait for radiotherapy treatment.

CUH

Dr Raj Jena

̽��ֱ��text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright © ̽��ֱ�� of Cambridge and licensors/contributors as identified. All rights reserved. We make our image and video content available in a number of ways – as here, on our main website under its Terms and conditions, and on a range of channels including social media that permit your use and sharing of our content under their respective Terms.

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AI could help cut waiting times for cancer by automating mark-up of patient scans prior to radiotherapy

cjb250 — Wed, 16 Dec 2020 14:08:25 +0000

̽��ֱ��AI technology, known as InnerEye, is the result of an eight-year collaboration between researchers at Cambridge-based Microsoft Research, Addenbrooke’s Hospital and the ̽��ֱ�� of Cambridge.

InnerEye aims to save clinicians many hours of time laboriously marking up patient scans prior to radiotherapy. ̽��ֱ��team has demonstrated how machine learning (ML) models built using the InnerEye open-source technology can cut this preparation time by up to 90% - meaning that waiting times for starting potentially life-saving radiotherapy treatment can be dramatically reduced.

Health and Social Care Secretary Matt Hancock said: “New innovations like this can make all the difference to patients and I am proud to see we are once again leading the way in new cancer treatments.

“Helping people receive treatment faster is incredibly important and will not only improve recovery rates but will save clinicians precious time so they can focus on caring for patients.

“Embracing new technologies will help save lives and is vital for the sustainability of the NHS, and our NHS Long Term Plan will continue to deliver the best possible care for patients so that we can offer faster, more personalised and effective cancer treatment for all.”

Dr Raj Jena from the Department of Oncology at the ̽��ֱ�� of Cambridge and an oncologist at Addenbrooke’s, who co-leads InnerEye, said: “These results are a game-changer. To be diagnosed with a tumour of any kind is an incredibly traumatic experience for patients. So as clinicians we want to start radiotherapy promptly to improve survival rates and reduce anxiety. Using machine learning tools can save time for busy clinicians and help get our patients onto treatment as quickly as possible.”

Dr Yvonne Rimmer, also from at Addenbrooke's, said: “There is no doubt that InnerEye is saving me time. It’s very good at understanding where tumours and healthy organs are. It’s speeding up the process so I can concentrate on looking at a patient’s diagnostic images and tailoring treatment to them.

“But it’s important for patients to know that the AI is helping me do my job; it’s not replacing me in the process. I double check everything the AI does and can change it if I need to. ̽��ֱ��key thing is that most of the time, I don’t need to change anything.”

Up to half of the population in the UK will be diagnosed with cancer at some point in their lives. Of those, half will be treated with radiotherapy, often in combination with other treatments such as surgery, chemotherapy, and increasingly immunotherapy.

Radiotherapy involves focusing high-intensity radiation beams to damage the DNA of hard cancerous tumours while avoiding surrounding healthy organs. This is a critical tool, with around 40% of successfully treated patients undergoing some form of radiotherapy.

Planning radiotherapy treatment can be a lengthy process. It starts with a 3D CT (Computed Tomography) imaging scan of the part of the body to be targeted. These CT images come in the form of stacks of 2D images, dozens of images deep, each of which must be examined and marked up by a radiation oncologist or specialist technician. This process is called contouring. In each image, an expert must manually draw a contour line around the tumours and key healthy organs at risk in the target area using dedicated computer software. For complex cases, this can take several hours in the planning of a single patient’s treatment.

This image segmentation task is a rate-limiting factor in the cancer treatment pathway for radiotherapy, which increases the burden of time on clinicians and the financial cost to hospitals. As this task is subjective, there can be significant variability across experts and institutions where acquisition protocols and patient demographics vary. This is a limitation to the use of imaging in clinical trials and can introduce variability in patient care.

Research published by the team in JAMA Network Open confirms that the InnerEye ML models can accurately and rapidly carry out the otherwise lengthy ‘image segmentation’ requiring hours of expert clinicians’ time.

Head of Health Intelligence at Microsoft Research, Aditya Nori, said: “This is the first time, we believe, that an NHS Trust has implemented its own deep learning solution trained on their own data, so it can be used on their patients. It paves the way for more NHS Trusts to take advantage of open-source AI tools to help reduce cancer treatment times.”

̽��ֱ��InnerEye Deep Learning Toolkit has been made freely available as open-source software by Microsoft.

While ML models developed using the tool need to be tested and validated in each individual healthcare setting, doctors at Cambridge ̽��ֱ�� Hospitals (CUH) have demonstrated how the technology can be applied in clinical settings.

Reference
Ozan Oktay, et al. Evaluation of Deep Learning to Augment Image-Guided Radiotherapy for Head and Neck and Prostate Cancers. JAMA Network Open; 30 Nov 2020; DOI: 10.1001/jamanetworkopen.2020.27426

Adapted from press releases from Microsoft and Cambridge ̽��ֱ�� Hospitals NHS Foundation Trust.

Doctors at Addenbrooke’s Hospital in Cambridge aim to drastically cut cancer waiting times by using artificial intelligence (AI) to automate lengthy radiotherapy preparations.

As clinicians we want to start radiotherapy promptly to improve survival rates and reduce anxiety. Using machine learning tools can save time for busy clinicians and help get our patients onto treatment as quickly as possible

Raj Jena

People Who Change ̽��ֱ��World | Dr Rajesh Jena

Dr Raj Jena and Yvonne Rimmer look at scans

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. Images, including our videos, are Copyright © ̽��ֱ�� of Cambridge and licensors/contributors as identified. All rights reserved. We make our image and video content available in a number of ways – as here, on our main website under its Terms and conditions, and on a range of channels including social media that permit your use and sharing of our content under their respective Terms.

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