Deep learning techniques for clinical decision support

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Clinicians are increasingly pressed to use vast volumes of data generated by scanners, ranging from low grade xrays to high resolution multi-dimensional CT scans. Analysing this data can be a subjective process, and is also time consuming for a clinician. A system based around robust artificial intelligence methods can greatly improve the accuracy of estimates and decisions made in the clinic.Whilst this project will focus on advancing computer science methods within the general area of artificial intelligence in general, and medical imaging in particular, the clinical applicability will remain in sight, and clinical input will be sought from collaborators at the Nuffield Orthopaedic Hospital.We will leverage the availability of both annotated and raw (unannotated) data, likely from MRI scans of adult hips in order to design methods which can support clinical decisions. These methods will predict the presence or absence of anatomical features within the scan, and will localise specific key points which can automate certain measurements (such as angles) taken in the clinic. Semantic segmentation techniques can also help identify contours around specific areas of interest, as well as help clinicians analyse the scanner signal within such areas.The generic aims are related to the segmentation and landmark identification in general. More particularly, special attention will be given to the robustness of the prediction. Such agents can rarely quantify their confidence in their prediction. As such, we will aim to calculate and output a measure of the uncertainty of the system in its own results, and will allow the clinician have final say in whether to accept or adjust any output from this adjunct system.These methods will support safe screening and monitoring patients with emerging long-term conditions such as the early stages of osteoarthritis. The system could ultimately help the clinician decide whether to employ conservative management (e.g. physiotherapy) or to make a more invasive intervention (such as hip replacement).Delaying hip replacement will greatly reduce the need for what is known as a "revision", which is a second hip replacement operation, carried out when the first prosthetic hip has worn out. Revisions are extremely expensive and complicated, so aiding the decision making and delaying some intervention in safe ways will potentially save the NHS significant costs.This project falls within the EPSRC Healthcare Technologies research area, particularly implementing "Complex models and decision-support systems that accommodate uncertainty". Once validated, the proposed methods will be an adjunct to clinicians in choosing and justifying the chosen patient-specific treatment.Our clinical collaborators are Professors Sion Glyn-Jones and Simon Newman from the Nuffield Orthopaedic Centre in Oxford.