How Machine Learning Could Play a Key Role in the Diagnosis of Rare Genetic Diseases

How Machine Learning Could Play a Key Role in the Diagnosis of Rare Genetic Diseases

Machine learning as a subset of artificial intelligence (AI) has increasingly become the subject of interest by many industries, including in the field of healthcare. For instance, AI and machine learning can play a key role in the diagnosis of rare medical conditions. AI and machine learning in the context of medicine and disease diagnosis use large sets of data to train algorithms and patterns in computers which can then be applied to new input in order to make a prediction such as a disease diagnosis. Machine learning in the context of diagnosis of rare genetic diseases would enable healthcare professionals to sift through large volumes of research and medical literature in order to draw conclusions that would have taken them years to reach had they gone through the research manually.

Different countries have alternative ways of defining a disease as ‘rare’. In Canada, a rare disease is defined as a “condition affecting fewer than 1 person within 2000 in their lifetime.” There are a total of 7000 known rare diseases and with the discovery of new genetic disorders every year, it is projected that 1 in every 12 Canadian will be affected by a rare disease in their lifetime. Although there are policy incentives for pharmaceutical companies to invest in research and development of treatments for rare diseases that affect small populations (see Canada’s regulatory approach to drugs for rare diseases), such research and development may not be profitable, and many conditions go undiagnosed and many individuals end up having to live with the symptoms of their chronic diseases for years. Additionally, rare genetic conditions are extremely difficult to diagnose since even the most experienced physicians may never come across a single patient with one during their years of practice.

Patients with rare genetic diseases can greatly benefit from the implementation of machine learning in healthcare. AI’s ability to “memorize” large amounts of data and extrapolate from them in a meaningful way in order to categorize patients or reach new diagnoses should give sufferers of rare diseases hope that with the help of rapidly improving technology, their conditions may be better understood and treated in the near future. There are several initiatives worldwide that are aimed at gathering information about rare diseases and making them accessible to healthcare professionals for use in diagnosis- Orphanet, CORD-MI in Germany, Undiagnosed Diseases Network (UDN) in the United States to name a few.

Although AI in the context of healthcare certainly offers significant benefits, it is by no means a ‘cure-all’ for the immense challenges of disease diagnosis. After all, the quality of the output from a computer algorithm- coded by a programmer or ‘learned’ by the machine itself- is only as good as the input. In a review published by the Journal of Rare Diseases in 2020, it was found that not all rare diseases are studied to an equal extent. Rare neurologic, rheumatologic, cardiac and gastroenterological diseases were more broadly studied and hence appeared in the literature more frequently. On the other hand, rare skin diseases were highly understudied and it was difficult for a computer to form meaningful algorithms from the limited data that was available in order to better understand the conditions and apply the existing expertise to new cases. That is to say, unless more funding, effort and incentives are invested in the study of rare genetic diseases, no amount of help from AI can help save patients and improve their quality of life. Per President and CEO of Rady Children’s Institute for Genomic Medicine, Dr. Stephen Kingsmore’s statement regarding artificial intelligence and medicine, “Patient care will always begin and end with the doctor.” Technology will only help professionals in connecting the ‘dots’ where there is existing data and research.

Written by Bonnie Hassanzadeh, IPilogue editor and Clinic Fellow at Osgoode Innovation Clinic.