Deep learning techniques for clinical decision support

临床医生越来越迫切地需要使用扫描仪生成的大量数据，从低级X射线到高分辨率多维CT扫描。分析这些数据可能是一个主观的过程，并且对于临床医生来说也是耗时的。基于强大的人工智能方法的系统可以大大提高临床评估和决策的准确性。虽然该项目将专注于在人工智能的一般领域，特别是医学成像领域推进计算机科学方法，但临床应用仍将是可见的，和临床输入将寻求合作者在纳菲尔德骨科医院。我们将利用这两个注释和原始的可用性（未注释）数据，可能来自成人髋关节的MRI扫描，以便设计可以支持临床决策的方法。这些方法将预测扫描中是否存在解剖特征，并将定位特定的关键点，从而可以自动进行临床中的某些测量（例如角度）。语义分割技术还可以帮助识别特定感兴趣区域周围的轮廓，以及帮助临床医生分析这些区域内的扫描仪信号。一般目标与分割和地标识别有关。更具体地说，将特别关注预测的鲁棒性。这些代理人很少能够量化他们对自己预测的信心。因此，我们的目标是计算和输出系统自身结果的不确定性度量，并允许临床医生最终决定是否接受或调整该辅助系统的任何输出。这些方法将支持安全筛查和监测患有新出现的长期疾病（如早期骨关节炎）的患者。该系统最终可以帮助临床医生决定是否采用保守治疗（例如物理治疗）或进行更具侵入性的干预（例如髋关节置换）。延迟髋关节置换将大大减少所谓的“翻修”的需要，这是第二次髋关节置换手术，当第一个假体髋关节磨损时进行。修订是非常昂贵和复杂的，所以帮助决策和延迟一些干预安全的方式将有可能节省NHS显着cost.This项目福尔斯属于EPSRC医疗保健技术研究领域，特别是实施“复杂的模型和决策支持系统，适应不确定性”。一旦验证，所提出的方法将是一个辅助临床医生在选择和证明所选择的患者的具体treatment.Our临床合作者是教授锡永Glyn-Jones和西蒙纽曼从Nuffield骨科中心在牛津大学。

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.