Crohn Disease Clinical Trial
Official title:
Pilot Study to Develop a Deep Learning Algorithm for Identification & Scoring of Terminal Ileal Crohn's Disease in Magnetic Resonance Enterography Images.
Verified date | March 2024 |
Source | London North West Healthcare NHS Trust |
Contact | n/a |
Is FDA regulated | No |
Health authority | |
Study type | Interventional |
Crohn's disease affects 200,000 people in the UK (~1 in 500), most are young (diagnosed < 35 years) with costs of direct medical care exceeding £500 million. Crohn's disease is caused by an auto-immune response and affects any part of the digestive tract, most commonly the last segment of the small bowel (the terminal ileum). Magnetic resonance imaging (MRI) plays a role in 3 areas: Crohn's disease diagnosis , monitoring treatment response & assessing development of complications. To evaluate the small bowel using MRI, Radiologists visually examine the scan slice-by-slice. The interpretation is time consuming and error-prone because of disease presentation variability and differentiation of diseased segments from collapsed segments. Deep learning for image analysis is based on a computer algorithm "learning" from human (Radiologist) generated training data. This method has been successfully applied to medical imaging, for example computer detection of lung cancer on chest X-rays. This pilot study investigates if a deep learning algorithm can identify and score segments of inflamed terminal ileum affected by Crohn's disease. To our knowledge this is the first project attempting to develop such an algorithm.The study will retrospectively review MR images obtained as part of standard care from patients being investigated for, Crohn's or being followed up with Crohn's disease. 226 patients' images will be used for the study. On fully anonymised images two Radiologists working at Northwick Park Hospital will score and outline normal and abnormal loops of terminal ileum. Imperial College computer science department will then develop a deep learning algorithm from imaging features of normal and abnormal loops. The study end-point is algorithm performance vs. images labelled by Radiologists. The eventual aim is to develop an algorithm that assists Radiologists in the accurate diagnosis and follow-up of patients with Crohn's disease.
Status | Active, not recruiting |
Enrollment | 226 |
Est. completion date | December 2025 |
Est. primary completion date | April 2025 |
Accepts healthy volunteers | No |
Gender | All |
Age group | 16 Years and older |
Eligibility | Inclusion Criteria for all cases: - Patient's age >16 years of age, (this age cut off has been used in the recent METRIC trial investigating imaging in Crohn's disease) - MRI sequences obtained include axial T2 weighted images; coronal T2 weighted images and axial post contrast MRI images. Inclusion criteria for normal MR Enterography cases: • Normal MR Enterography studies reviewed in consensus by two Radiologists (UP & PL). Normal is defined as no sites of small or large bowel Crohn's disease. Inclusion criteria for terminal ileal Crohn's cases: - MR Enterography studies reviewed in consensus by two Radiologists shows terminal ileal Crohn's disease. Patients with more than one segment of small bowel Crohn's disease including terminal ileum are eligible. Patients with terminal ileal Crohn's disease continuous with large bowel are eligible. - Diagnosis of Crohn's disease of terminal ileum based on endoscopic, histological and radiological findings. (This criteria has been used in the recent METRIC trial investigating imaging in Crohn's disease). Exclusion Criteria for all cases: - Poor quality MRI images as judged by consensus Radiologist opinion. - No more than 3 MRI scans will come from the same patient. Exclusion criteria for terminal ileal Crohn's cases: - MR Enterography shows any bowel abnormality not due to Crohn's. - Patient has undergone previous small or large bowel resection (this will distort anatomy and is beyond the scope of the present project). Patients' with other previous surgeries are eligible. - Patients with large bowel Crohn's disease not continuous with the terminal ileum. |
Country | Name | City | State |
---|---|---|---|
United Kingdom | St Mark's Hospital | London | Harrow |
Lead Sponsor | Collaborator |
---|---|
London North West Healthcare NHS Trust | Imperial College London |
United Kingdom,
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | Machine learning algorithm's ability to accurately localize the terminal ileum. | Study will compare manually segmented regions of interest by Radiologists with predictions by machine learning localisation algorithm. | 24 months | |
Secondary | Data processing time until a diagnosis reported by algorithm. | Study will assess time taken for algorithm to give a diagnostic outcome. (Previous studies have shown this time can be variable). | 24 months | |
Secondary | Machine learning algorithm's ability to accurately distinguish abnormal and normal terminal ileum. | Agreement between Radiologists and predictions by machine learning classification algorithm will be analysed. | 24 months |
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