Quick and Accurate Diagnosis Saves Lives
At six days old, Maverick Coltrin developed seizures. Doctors were baffled, and his parents feared for his life, and conventional diagnostics had failed to reach a conclusion. The medical team at the Rady Children’s hospital suggested Maverick take part in an innovative digital diagnostics study. The approach used data gathered from the latest genetic sequencing techniques, then applied Artificial Intelligence (AI) to match it to existing research and diagnostics information sources about rare genetic diseases.
The results came back, showing that Maverick had a rare form of epilepsy, but one that was easily treatable with vitamin B6 supplements. His life was transformed. The technique used to diagnose Maverick is part of a growing field of digital diagnostics made possible by the combination of sophisticated AI and the ever increasing availability of genetic and patient data.
Why the Time is Right for AI-assisted Diagnostics
These new approaches to diagnostics are possible because of the ever-increasing availability of data and sophisticated low-cost AI tools. Every year the cost and time to produce genetic sequencing data are more economical and faster than the previous year. Patient electronic medical records (EMR), digital scans, and digitized research outcomes are now more readily available than ever before. The most advanced AI tools ever developed are widely accessible in an open-source format and often pre-installed on powerful yet reasonably priced cloud platforms.
The Secret Sauce
It is essential to understand that developing AI diagnostics systems is not as straight forward as just combining cutting edge AI technology with vast quantities of data. Generating insight from clinical and healthcare data is challenging work; it needs medical science expertise, world-class data science technology, and talent, as well as the IT experience to integrate emerging and novel technologies into robust operational systems.
The vast history of medical literature and genetic data does not present itself in an easy to navigate spreadsheet – but appears in many different and complex formats, from research papers to handwritten notes, to images. Comparing and extracting this kind of data demands a highly specific approach to the design of AI and machine learning algorithms. This kind of work is far removed from the world of e-commerce or consumer AI, where simple correlations can deliver a “good enough” return. The need for the highest accuracy and a rigorous proof of causation from complex data sets requires a very different breed and blend of data science.
In this field, much of the work utilizes Deep Learning, a form of AI whereby the model learns to interpret relationships in large and complex sets of data. Presented with enough data, it gradually learns to identify what is essential and how information relates to each other. It typically involves training a multi-layer artificial neural network on a labelled dataset. With enough training data, a well-designed deep learning model will classify information as accurately as human experts, in a fraction of the time. Other advanced AI and machine learning techniques may combine several models together to create sophisticated learning capabilities that, when applied to a variety of data sources, produce highly accurate predictions.
Who is Doing it Well?
Many organizations are doing this well. For example, a system developed in a collaboration between the Manton Center for Orphan Disease Research, part of the Boston Children’s Hospital, and Alexion is able to produce a highly personalized sequence of clinical questions to aid diagnosis of rare-diseases. This system combines the diagnostic knowledge of healthcare professionals, a global disease database, and the specifics of a patient’s personal medical history to go through a series of questions that step by step narrows down the highest likelihood indication. The resulting AI platform, developed by Alexion with their affiliates, uses various AI algorithms including the “20 questions game” format. This platform has the potential to place the global knowledge of rare disease into the hands of every clinician. They then combine this with their personal experience and expertise in patient interaction to significantly increase the probability of diagnostic success. Rare diseases present a specific challenge to AI systems – AI systems need large quantities of data to ‘learn,’ but by definition, large data sets are not available for rare diseases.
Another Alexion affiliate, The Rady Children’s Institute, has gone even further, leading it to receive a Guinness World Record for a rapid under 24 hours diagnosis, using genome sequencing as an input to the AI diagnosis. An AI engine analysed the digitized results and identified the overlap between the specific observations of a given child’s illness with a reference set of expected observations covering all genetic diseases. The key to the automated diagnosis is combining genome sequencing, phenotypes and Natural Language Processing. The patient’s electronic medical records (EMR) are processed to identify and extract observed disease features; these are then compared with the results of genetic sequencing. In this race against time, the manual review of health records takes precious hours from an overloaded medical professional and is prone to personal subjectivity. AI diagnosis support, which combines the evidence in the gene sequence and health records, not only reduces the elapsed time, it can combat this bias.
Future AI Diagnostic
Companies like Alexion, working with their affiliates, are looking to bring AI-assisted diagnostics to an ever-wider range of diseases. They will look to make greater use of phenotypes and genetics data to reach this goal.
The challenge for the Alexion data sciences team and those like Tessella working with them is to master and integrate an extensive range of highly specialized algorithmic techniques, known under the generic term of AI. This challenge has been met through an active collaboration between internal and external data scientists working closely with physicians and healthcare experts. New specialists are able to join the core team rapidly and communicate clearly how complex technical choices will play out to the medical, business, data, and IT subject matter experts.
Thanks to AI diagnostics, new levels of speed and accuracy are possible, helping doctors make correct diagnoses and expanding the likelihood that they will spot more unusual conditions. The upshot will be faster interventions and increased accurate diagnoses. This will reduce costs of readmissions and increase the chance of initial treatment being successful; most importantly, it will save lives.