- Spine AI significantly reduces the time it takes for radiologists to interpret MRI scans, study shows.
- Siemens Healthineers partnered with National University Hospital (NUH) on the Spine AI project to integrate AI solutions into their clinical workflow.
Image source: National University Hospital
Lumbar spinal stenosis is the narrowing of the spinal canal in the lower back, leading to nerve compression that affects the lower limbs. Patients typically experience cramps and pain in the buttocks or legs while walking, which are relieved by sitting or bending forward.
About one in ten people in the general population1 are affected by lumbar spinal stenosis, which relies heavily on lumbar MRI scans for diagnosis. In a significant advancement for medical diagnostics, the National University Hospital (NUH) is now using an in-house artificial intelligence (AI) deep learning system that more than halves the time required for radiologists to interpret MRI scans.
Since June, Spine AI has been trialled in more than 50 patients. This deep learning tool automatically detects areas of spinal canal narrowing and categorizes the severity of stenosis, providing rapid results that enhance clinical decision-making and efficiency.
Developed in-house by NUH in collaboration with a team from the National University of Singapore’s School of Computing and the National University Spine Institute, Spine AI was trained using lumbar spine MRI studies from 446 patients, encompassing more than 18,000 images.
Traditionally, after a patient undergoes an MRI scan, a radiologist manually assess the severity of stenosis at each level of the lumbar spine. With five spinal segments, each having five potential sites for stenosis, the radiologist has to analyse 25 regions in a patient’s spine before writing a report, a process that can take 10 minutes or more.
Dr Andrew Makmur, Group Chief Technology Officer, National University Health System (NUHS), and Consultant, Department of Diagnostic Imaging, NUH, said: “The degree of stenosis, or narrowing, at each region plays a role in determining the appropriate treatment, but detailing such information in a report can be repetitive and time-consuming. In addition, there are multiple grading systems for lumbar spinal stenosis, with a lack of standardisation.”
Dr Makmur and Dr James Hallinan, Senior Consultant, Department of Diagnostic Imaging, NUH, spearheaded the development of Spine AI, which improves the consistency, accuracy and objectivity of MRI scan assessments.
Dr Hallinan explained: “This model will automatically detect the sites of stenosis and then grade the severity, before automatically generating a report. Colour-coded boxes will then be placed along every site in the lumbar spine to enable the radiologist to quickly inspect the gradings. This streamlined process can reduce the time for interpretation of a scan from 10 minutes to just 3 minutes.”
A study2 published in the medical journal, Radiology in 2022 showed that radiologists using Spine AI took as little as 47 seconds to interpret each spine MRI study, compared to up to 4.5 minutes when they did not use the model. This time-saving advantage was observed across all experience levels, with in-training radiologists having the largest mean time saving of 74 per cent when using Spine AI.
Dr Makmur estimated that with about 4,000 lumbar MRI scans performed at NUH each year, and approximately 7 minutes saved per scan – translates to about 466 hours saved annually.
He said: “AI is transforming spinal imaging and patient care through automated analysis and enhanced decision-making. With Singapore’s ageing population and an expected increase in imaging volume, there is great potential for Spine AI to augment radiologists’ efficiency and allow them to focus on more complex cases.”
Dr Jonathan Tan, Consultant, University Spine Centre, Department of Orthopaedic Surgery, NUH, said: “Lumbar spinal stenosis can lead to a significant loss of mobility and quality of life. It is the most common indication for spinal surgery in patients aged 65 and above. Spine AI can potentially contribute to the efficient diagnosis and classification of this common condition to enable prompt and appropriate interventions.”
In November 2023, NUH partnered with German medical technology company Siemens Healthineers to optimize the user interface / user experience (UI/UX) of Spine AI. This collaboration aims to bring the AI tool to a global market, potentially transforming spinal care worldwide.
“Siemens Healthineers is proud to be partnering with the NUHS team on the Spine AI project, integrating the AI solution into their clinical workflow. This has a significant impact on both clinicians and patients,” says Ms. Siow Ai Li, Managing Director of Siemens Healthineers Singapore and Malaysia.
“It is wonderful to see how this AI solution is now accessible throughout the NUHS radiology department for clinical evaluation. With the success of this collaboration, we are confident that radiologists will be able to identify abnormalities in a large volume of scans, improving efficiency and reducing the burden of repetitive tasks through automation. This project clearly demonstrates Singapore’s commitment and ability to build clinical AI solutions, and we are very proud to be able to leverage the integration capabilities of Siemens Healthineers’ existing digital solutions within the radiology infrastructure to deploy the Spine AI model smoothly. We hope that this approach will serve as a template for further adoption and integration of AI solutions in clinical environments, ultimately benefiting patients by enabling faster diagnosis and treatment," she added.
1Lurie J, Tomkins-Lane C. BMJ. Management of lumbar spinal stenosis. 2016 Jan 4;352:h6234
2 DSW Lim, et al. Radiology. Improved Productivity Using Deep Learning-assisted Reporting for Lumbar Spine MRI. 2022 Oct;305(1):160-166.
About Siemens Healthineers
Siemens Healthineers AG (listed in Frankfurt, Germany: SHL) pioneers breakthroughs in healthcare. For everyone. Everywhere. As a leading medical technology company headquartered in Erlangen, Germany, Siemens Healthineers and its regional companies are continuously developing their product and service portfolio, with AI-supported applications and digital offerings that play an increasingly important role in the next generation of medical technology. These new applications will enhance the company’s foundation in in-vitro diagnostics, image-guided therapy, in-vivo diagnostics, and innovative cancer care. Siemens Healthineers also provides a range of services and solutions to enhance healthcare providers’ ability to provide high-quality, efficient care. In fiscal 2022, which ended on September 30, 2022, Siemens Healthineers, which has approximately 69,500 employees worldwide, generated revenue of around €21.7 billion and adjusted EBIT of almost €3.7 billion. Further information is available at www.siemens-healthineers.com.
About National University Hospital
The National University Hospital (NUH) is Singapore’s leading university hospital. While the hospital at Kent Ridge first received its patients on 24 June 1985, our legacy started from 1905, the date of the founding of what is today the NUS Yong Loo Lin School of Medicine. NUH is the principal teaching hospital of the medical school. Our unique identity as a university hospital is a key attraction for healthcare professionals who aspire to do more than practise tertiary medical care. We offer an environment where research and teaching are an integral part of medicine, and continue to shape medicine and transform care for the community we care for. We are an academic medical centre with over 1,200 beds, serving more than one million patients a year with over 50 medical, surgical and dental specialties. NUH is the only public and not-for-profit hospital in Singapore to provide trusted care for adults, women and children under one roof, including the only paediatric kidney and liver transplant programme in the country. The NUH is a key member of the National University Health System (NUHS), one of three public healthcare clusters in Singapore.
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