Developing diagnostic methods for clinical genetics - phenotyping from faces in photos.

I am leading research that will help clinicians to diagnose rare diseases using automated computer analysis of photos.

Rare diseases are numerous - so much so that as a group they are very common. One person in 17 has a rare genetic disorder, but most fail to receive a genetic diagnosis. Diagnosing the genetic cause of a disorder, even when there are only a handful of cases in the world, represents the first of many steps in finding effective treatments. Even though new genetic tests promise to assist in diagnosing some of these patients, the tests are expensive and currently only available to a few people in the wealthiest countries.

For the past 65 years expert clinical doctors have been matching a diagnosis to patients based on facial features and follow up clinical tests. We are developing algorithms through which a computer will learn and apply these skills objectively.

Identifying patients with the same genetic disorders allows comparisons to be made between them. In turn, this can improve estimates of how the disease might progress and allow direct therapeutic benefits, for instance by showing which symptoms are caused by the genetic disorder and which symptoms might be caused by other clinical issues that can be treated.

Using the latest research in computer vision and machine learning the algorithm automatically analyses patient photographs and finds their place in "Clinical Face Phenotype Space" (CFPS). Patients that share a specific dysmorphic disease or syndrome, will cluster together in CFPS. The CFPS model is created and shaped using ordinary, family album photos and accounts for variations between images that are not disease relevant (such as lighting, image quality, background, pose, age, gender, ethnicity, and facial expression).

In the present application I seek funding to develop methods for clinical geneticists to query CFPS for clinically relevant information. The work will develop means by which a patient's similarity to other patient groups can be visualised, explored and tested through robust statistical modelling.

Furthermore it will make it possible to overlay a patient's DNA with CFPS to identify disease causing mutations. This will improve our understanding of how rare diseases disrupt the normal functioning of the body, and in turn influence decisions in treatment strategies.

A clinician should, in future, be able to take a smartphone picture of a patient and query CFPS to quickly find out which genetic disease the person might have. For diseases unknown to medical science, CFPS will find if there are any other patients around the world that might have the same disease.

CFPS will learn from our faces to help diagnose rare diseases.

Aim 1. Enable complete phenotype descriptions from photos for clinicians. I will adapt existing computer vision and machine learning methods for the specific purpose of obtaining phenotype descriptions. CFPS already performs exceptionally well given the limited information that it exploits thus far. Developing the accuracy of craniofacial point annotations and feature descriptors for capturing phenotype characteristics, inferring 3D orientation from 2D images, and applying machine learning approaches will ensure that CFPS reaches its full potential.

Aim 2. Robustly predict causative mutations from DNA variants and CFPS. I will lead research that will aid genetic diagnoses with CFPS. The collective body of work to bring CFPS and DNA variant data together is essential for CFPS to become ready for clinical genetic data. To do this I will create family representations to differentiate between de novo and inherited phenotype. With a vector representation of a face during development CFPS will be able to capture and predict disease progression. I will also put in place a robust statistical framework to infer causative DNA variants in rare de novo mutation diseases to empower the search for ultra-rare syndromes.

Aim 3. Develop the big data framework for implementing CFPS in global healthcare. Current ethical, law and data security concerns are a major hurdle in the path of bringing CFPS to patients. Within the scope of this Fellowship I will address each of these challenges. Patient images through collaborations and patient contributions will involve applying for REC approval to ask for consent and for people to contribute photos from their family albums. This will be both an exercise in public engagement but also en masse data collection. I will be working with ethics, law and eHealth data management questions to enable CFPS to develop into a form which will be not only powerful, but also usable in clinical settings.

This project is highly collaborative both within the UK and internationally. This is also translational and interdisciplinary science, criss-crossing the fields of computer vision, computational biology, and clinical genetics. Consequently it provides several direct pathways to impact. The most important of these is that my work will aid in the clinical diagnosis worldwide of the genetics of rare disorders. I have established and will be expanding an international network of collaborations to apply and develop our approach using patient data. I expect this project in 4 years' time to have progressed to being in the early stages of implementation in clinical genetics counselling, initially in the UK and Europe and then elsewhere.

In the space of three years the following directions will lead to measurable impacts:

The methods to interrogate CFPS together with genotype will add statistical power to the inference of genetic diagnoses. In collaboration with clinical sequencing efforts our approach will be applied to patient image data to add value to these existing datasets and on-going projects. This will result in research participants receiving diagnoses that otherwise they would not.

I will set up partnerships with patient support groups for syndromes with identified genetic origins. This will be a means to present the science and the project to directly engage the public, and offer the opportunity to contribute photos (under a REC approved informed consent model) for the further refinement of the CFPS. This is public engagement of the best and most interactive kind, which can be measured by the number of images of individuals (>10,000) and syndromes (>200) that I aim to collect over the coming 4 years.

Collaborations will be expanded to additional clinical geneticists across the world. For clinicians the practical utility would be to evaluate the approach as a means of narrowing down hypotheses for the clinical diagnosis of known syndromes. As the work to interrogate DNA variant data together with CFPS develops we will be able to narrow the search for causative variants in patient data even where no established diagnosis exists. Results will also directly feed back to the project, as they will expand the number of images of clinically defined syndromes that will iteratively improve the CFPS - the more people that use it the better it gets.

Extending contacts within the MRC Units in The Gambia and Uganda would be a beneficial means of geographically widening our research to efficiently test and apply CFPS in the future. At the end of the 4 year Fellowship CFPS will be ready for testing and accreditation for use in the NHS and be ready to begin testing in clinics in more countries around the world. With care during the methodological development of the CFPS this should be achievable on a large scale rapidly.