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Technology Takes Aim at Rare Diseases

29 Feb 2016 by Teresa Meek

Suffering from a rare disease is frightening, frustrating and lonely.

And it’s not as uncommon as you may think. About 7,000 rare diseases have been identified so far. Most are genetic, and they affect 30 million people in the US — about 10% of the population.

Tragically, 50% are children, and 30% of them usually don’t live to see their fifth birthday.

To make matters worse, only 5% of rare diseases have an FDA-approved drug treatment.

But technology is creating new ways of sharing information and crunching data that could have a dramatic effect on recognizing — and eventually treating — rare diseases. In the meantime, affected people or family members can feel a little less alone by banding together on Rare Disease Day. It raises awareness of the issue, appropriately, on a rare day: February 29.


Diagnosing a rare disease is notoriously difficult. Genomic sequencing reveals details to put the puzzle together, but often it’s not covered by insurance.

An "Amplify Hope" program run by the Rare Genomics Institute (RGI) helps families use crowdfunding sites to create their own campaigns to cover the cost of sequencing, usually about $7,500. Families are assigned to a crowdfunding site — Indiegogo Life, YouCaring or Crowdrise — and are given assistance and tips for running a successful campaign. Since the program started in 2013, every family has reached its goal, and 64% have exceeded it, RGI says.


Patients can also try crowdsourcing their diagnosis. CrowdMed, a healthcare crowdsourcing platform, lets patients share their cases with “medical detectives,” including doctors, nurses, medical students, Eastern medicine practitioners and other patients.

It isn’t free — patients pay from $150 to more than $700 to have their cases reviewed. But the average user has already been sick for eight years, has seen at least eight doctors, has spent approximately $60,000 — and still doesn't have a diagnosis. According to the platform’s website, more than 50% of patients who have used it said their diagnosis was later confirmed by a physician.


What if you didn’t need to sequence someone’s entire genome to diagnose a rare disease? What if you could somehow read it on their face?

It sounds outlandish, but facial recognition software was voted the top potential solution in a rare disease hackathon at MIT sponsored by drug marketing company Siren Interactive. The event last year brought together entrepreneurs, software engineers, healthcare professionals and designers to spend a hectic weekend developing tools to speed diagnosis of rare diseases.

Facial recognition software could help doctors — who often don’t recognize rare diseases simply because they seldom see them in their practice — to narrow the possibilities. The program maps the face, measuring distances from the tip of the nose to the pupils, and using many other reference points. Measurements outside a normal range indicate a risk for an associated genetic condition. While the tool needs refining, its possibilities are exciting — as is the novel way it was developed.

New network

Though individual physicians don’t often see rare diseases, their knowledge collectively could be a gold mine. To help them pool information, the National Institutes of Health recently set up an Undiagnosed Diseases Network, which they can use to test their patients’ genetic codes and share information about them.

Getting a diagnosis is a relief to many patients even if their disease has no known cure. The Shire Rare Disease Impact Report found that it takes an average of more than seven years for rare disease patients to be diagnosed. Many are misdiagnosed, often several times, before learning what’s wrong with them.

Ask Watson

Apparently, IBM’s Watson isn’t kidding in that cute ad where he tells a little girl he helps doctors.

Boston Children's Hospital is using the powerful analytics platform to diagnose and treat a rare genetic kidney disease. Watson will bone up on nephrology, then doctors will enter genomic sequencing data from their past patients. The goal is to find hidden insights that will help doctors diagnose future patients. If the project is successful, it will be expanded to include other diseases.