According to a story from the National Center for Advancing Translational Sciences (NCATS), research supported by the center is making it possible for kids born with serious, rare genetic disorders to get diagnosed more quickly, which should lead to improved outcomes for patients. Whole genome sequencing is the procedure that can allow for an accurate diagnosis, but interpreting the results requires extensive expertise and training. However, an automated, machine learning approach could make whole genome sequencing much more impactful by allowing the results to be interpreted more easily.
Delayed Diagnosis: a Massive Problem
Diagnostic delays are a major issue for rare disease patients across the globe. Patients with rare disease are routinely misdiagnosed or experience major delays. Many rare disease patients never get diagnosed at all. The results are predictable; delayed diagnosis means delayed treatment, leading to diminished treatment effectiveness. It can turn a patient whose life and function could be preserved into a hopeless case. Every day or hour can make a difference.
Innovating to Improve Rare Patient Lives
A team associated with the Rady Children’s Institute for Genomic Medicine, led by CEO Stephen Kingsmore, is responsible for the development of the machine learning interpreter for rapid whole genome sequencing. The automated approach allow for a patient’s results to be compared with a vast trove of clinical data in an instant. The challenge of interpreting the results of rapid whole genome sequencing has been a major barrier to its implementation at medical centers and hospitals, but the automated approach will make the entire process much more practical.
The end result will be more rapid diagnosis for patients with rare genetic disorders. This means that they can be treated more quickly and effectively, resulting in improved overall outcomes and quality of life. The development of the automated interpreter was supported in part by NCAT’s Clinical and Translational Science Award Program Collaborative Innovation Award. You can learn more about this program here.
The results of this research were first published in the journal Science Translational Medicine. Check it out here.
