Small Bowel Deep Learning Algorithm Project

Introduction.

The principal aim of the study is evaluating the accuracy of deep learning algorithm in differentiating between normal and abnormal terminal ileum against experienced Radiologists on MR Enterography images.

The study builds on existing research, which has shown statistical methods can identify sites of small bowel Crohn's disease. However the process was time consuming >1hr and not fully automatic. Our project investigates if cutting edge "deep learning" algorithm (based on neural networks) coupled with increased computing power can provide accurate and timely information.

The project has been designed jointly by Specialist Radiologists in Gastrointestinal imaging (who are aware of the challenges in imaging Crohn's disease accurately) and Imperial College Computer Science Department (who are experienced in developing neural networks for medical imaging). Input and review from London North-West Research and Development department is also acknowledged.

Study design.

Retrospective design & Recruitment.

The study will retrospectively identify eligible patients and use a consecutive case sampling technique, (all eligible images will be included working backwards from most recent).

This retrospective approach compromises between generalisability of findings being reduced vs. the study being carried out relatively quickly and at low cost (study has no grant funding).

The investigators are confident of the generalisability of the results as a recruitment target of 113 normal cases and 113 cases with terminal ileal disease should cover the spectrum of normal and abnormal appearances (previous studies have used <50 image sets).

Cases with normal terminal ileum on MRI are included as an approach to algorithm development involves comparison of imaging features of normal and abnormal terminal ileum on MRI studies.

Non-experimental approach.

The method uses MRI scans undertaken as part of standard clinical care. No additional imaging is undertaken for this study. The study results will not change the current treatment/s eligible patients are taking..

Consent & confidentiality.

As the images used for algorithm development are fully anonymized so explicit consent will not be obtained. This follows guidance from the General Medical Council Guidelines in 2011 and The Royal College of Radiologists(UK) in 2017 which state anonymized recordings can be shared for use in research without consent.

MRI images used for this study were acquired as part of routine standard clinical care, and would routinely be viewed by the Radiologists taking part in this study as part of their normal working practice.

As soon as suitable patient is identified the patient's images will be copied in a fully anonymized form with no direct or indirect identifiers. A robust anonymization function is included in the Radiology image viewing program. Study subject Identifiers will be randomly allocated preventing pseudo-anonymization if scans from the same patient at different time points are included.

No sensitive/patient identifiable data will be transferred for algorithm development during the study. The algorithm development is based on matching MRI pixel intensities to disease scores/ annotations across multiple scans. Anonymization does not affect the pixels within the image. Only aggregate data will be presented in publications- i.e. single case examples will not be published.

Conflict of interest. The researchers on this study declare no conflict of interest.

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.