Title: OCTAHEDRON : Optical Coherence Tomography Automated Heuristics for Early Diagnosis via Retina in Ophthalmology and Neurology
Funder: Department of Health and Social Care

Abstract: Neurodegenerative disorders are often associated with changes in retinal structure. Optical coherence tomography (OCT) is a new imaging technique which reveals the retina in microscopic 3D detail. The complex 3D structure of OCT scans is challenging for humans to interpret, but lends itself to machine learning. The OCTAHEDRON project aims to use the latest deep learning techniques to enable early detection of neurodegenerative disease via OCT scans. Following previous approaches, we embed domain knowledge by first analysing OCT scans to extract distinct layers of tissue (segmentation) and intensity at each layer boundary (pseudo-fundus images). This information is passed to a classification network which learns to associate particular features with particular neurological and ophthalmological diagnoses. This requires large amounts of data in the form of OCT scans along with patient clinical information, which we will mine from NHS data stores. We obtain human-labelled segmentation via an innovative web application which also helps train junior ophthalmologists. We will use unsupervised learning techniques (auto-encoders and semi-supervised networks) to exploit still larger amounts of unannotated OCT scans. Throughout, we will engage patients and other stakeholders to learn how best to use data and exploit this technology. Benefiting from our pilot work, we aim to have an algorithm capable of reporting the probability of at least Parkinson s disease, and possibly other conditions, from a 3D OCT scan at the end of this one-year project. We will then roll this out to NHS clinics for research use and evaluation. Ultimately, our aim is to enable early diagnosis and neuroprotective therapy, which is essential for slowing disease progression. The system will be trialled in high-risk groups such as patients with genetic markers for Parkinson s. Since OCT imaging is quick, inexpensive and increasingly available in high-street opticians, our long-term goal is screening for neurodegenerative disease via eye tests.

Lay abstract: Degenerative brain diseases such as Parkinson s and Alzheimer s are costly and difficult both for people living with these conditions and for society. Increasingly, treatments are available, but these are most effective when begun early in the disease process, when relatively little damage has occurred. Early diagnosis of these disorders is therefore essential. Yet the first changes occur deep inside the brain, and often do not cause noticeable symptoms until damage is already widespread. Scientists have recently learnt that these diseases also cause changes in the retina, the light-sensitive tissue at the back of the eye. This is not as surprising as it sounds, because the retina develops from the same type of cells as the brain. In fact, the retina is effectively an outgrowth of the brain, and the only part which can be viewed from the outside. Over the last decade or so, the treatment of eye disease has been revolutionised by a new imaging technique, called optical coherence tomography or OCT. In just a few seconds, OCT scans a patient s eye and shows doctors the 3D structure of the patient s retina. OCT imaging is now standard in NHS eye clinics, and increasingly widespread in high-street opticians. OCT scans reveal almost microscopic levels of detail, but their very richness makes them hard for humans to interpret. We hope to overcome this challenge by using another revolutionary innovation &ndash; new techniques for programming computers which enable them to learn different tasks by studying vast amounts of data. This &ldquo;machine learning&rdquo; is what makes it possible for Alexa to understand your instructions, or Facebook to tag your friends in photos. The OCTAHEDRON project aims to use machine learning to detect early signs of neurodegenerative disease - such as Parkinson s - in OCT scans of the retina. To achieve this aim, NHS doctors will teach our computer system how to recognise the different layers of the retina in OCT images. As it learns, the computer will in turn help teach junior doctors, providing valuable training. Then, we will exploit the power of the NHS s vast data-sets. By studying thousands or even millions of NHS OCT scans, the computer becomes familiar with their structure. By combining this with a smaller number of scans from patients who went on to be diagnosed with neurological conditions, the computer can then learn how these patients retinas are different. Ultimately, the software will help the NHS to diagnose more efficiently and without overstretching the limited number of human experts trained to interpret OCT scans. Neurological disorders will be detected sooner, so treatment can begin earlier. The reduced disability and improved outcomes will benefit patients and reduce costs to wider society.

Research activity codes: 7.3 MANAGEMENT AND DECISION MAKING;4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: NEUROLOGICAL