Technology Helping to Make Registries, Databases More Efficient
In amyotrophic lateral sclerosis (ALS), as in many diseases, patient registries, biorepositories, and natural history studies are useful in planning clinical trials and as tools for scientists to learn more about how a person’s lifestyle, genetics, and environment may lead to ALS. These databases can also help find new ways to treat the disease.
ALS is a rare condition for which not much data is available. But ALS-related institutions make it easier and more efficient to collect data hosted on platforms such as registries, thanks to technological advances in artificial intelligence (AI), computers and smartphones.
Erin Dittoe, 55, was diagnosed with ALS two years ago and shares her health data in a number of different ways.
She registered with the National ALS Registry of the Centers for Disease Control and Prevention (CDC), participated in genetic testing, and wears a watch that tracks her movements for observation clinical trial (NCT05276349). That hearing is searching to determine whether home measurements can replace repeated in-person clinic visits.
Diagnosed with sporadic ALS in November 2020 – the most common type, where there is no family history – Dittoe’s disease was slow. She can walk with help, talk (albeit slowly) and work from home.
The Ohioan hopes that the information she contributes will help scientists better understand what causes ALS and how to treat it.
“I kind of feel like [it’s] for the people who for one reason or another cannot answer questions, this is for them,” said Dittoe. “It does my part.”
The ALS Therapy Development Institute (ALS TDI), a nonprofit organization focused on disease research, is enlisting Google’s help to streamline its data analysis and use of patient data. Google’s application programming interface forms the analytical backbone of its Precision Medicine Program (PMP), which currently has data covering 813 fully enrolled patients—those who contributed three or more months of information. The PMP project, started in 2014, stores information about a patient’s movement capabilities, medical history, genetics, biomarkers and clinical measurement scores, as well as voice recordings.
According to Fernando Vieira, managing director, CEO and chief scientific officer of the ALS TDI, Google’s Looker platform, as the interface is called, is a big data analytics platform used primarily for business intelligence. The powerful tool allows researchers to look up people’s information — as detailed as their genome, for some — and compare it to their disability progression, as measured by the ALS Functional Rating Score-Revised (ALSFRS-R).
Identifiable information—including participants’ names, social security numbers, or email addresses—is not available to researchers. Data that can identify any individuals is protected under the Health Insurance Portability and Accountability Act (HIPAA) of 1996 in the US. Each participant is referred to as a number only.
To date, ALS TDI has captured 9,873 voice recordings, 19,404 ALSFRS-R scores, and 7,422 weeks of data from patients wearing accelerometers, which calculate how much each wearer moves.
ALS TDI believes it is important to share the collected data with patients, Vieira said, so they can track their own health.
“A key principle of our program has been to treat our participants as partners from the beginning,” said Vieira. “That, I think, has been the key to maintaining compliance and commitment to the efforts, and I would encourage that.”
Recognize patient ‘voices’
In 2018, ALS TDI too with Google on Project Euphonia, which aims to use artificial intelligence to help people with impaired speech (dysarthria) – a common ALS symptom – interface with voice recognition on its platforms, such as the Google Pixel smartphone.
Google uses available PMP voice recording data to train its AI in interpreting speech with dysarthria. The first publicly available tool to come from Project Euphonia, Project Relate, is an app now in beta testing.
Speech recognition software, such as that used with Siri or Google Home, recognize wavelengths that correspond to spoken words. It brings those words togetherr and, through a series of steps, come to an understanding of someone’s speech and commands – for example, “Turn off the lights.” But it can only interpret phrases based on the quality of the voices used to train it. People with speech impairments, whose voices are typically not part of a software’s training parameters, often struggle to use this technology.
Loss of vocal clarity and strength is common with ALS. In a YouTube Original documentary about AI, the father of former NFL linebacker Tim Shaw recounts his son’s frustrations with his increasing lack of clarity in speech. As his ALS progressed, the former soccer player had to change his phone’s contact information from “Dad” to “Jojo” because it couldn’t understand Shaw’s instruction to “call Dad.”
ALS TDI’s work with Google also has a algorithm, derived from machine learning, that can better detect changes in disease severity based on a patient’s speech and movement data than ALSFRS-R tests taken at a given time. The code behind the tool has been made available for scientists to use and continue to improve.
While such technology continues to be refined, its use continues to increase. As part of her trial, Dittoe is recording her voice in an app that aims to track disease progression through changes in speech.
What blood samples and earlier HIV work can teach
With its PMP project, ALS TDI also collects data from patients’ blood samples, looking for biological measures of disease progression. This work is supported by a $281,000 grant awarded to the institute in March by the Congressional Directed Medical Research Program, part of the US Department of Defense.
ALS TDI plans to send blood sampleses, collected quarterly over one year, to SomaLogic, a Colorado company, “to observe the concentrations of thousands of cytokines, growth factors, … kinases, structural proteins, hormones and other proteins in each sample” that can serve as biomarkers.
Vieira compares the race to cure ALS to the charge to find treatments for HIV.
In the mid-1990s, scientists conducting a natural history study of HIV found that a certain genetic variation confers resistance to the virus. Specifically, they discovered that people with genetic mutations that lead to a “non-functional,” or inactive, CCR5 receptor protein were “highly resistant” to the infectious disease. This pointed to CCR5 as a promising target for preventing HIV infection.
Through a study “designed to teach you,” Vieira said, scientists could more quickly develop “drugs that target that receptor” and better treat HIV.
“They were able to reveal that you can go into a natural history study knowing nothing and let it show you something,“ he said, adding: “And that’s how, I think, we got into this [ALS data] … if we accept that we know very little, collect as much data as we can, and then allow it to show us what is important over time.”
Like that HIV natural history study, whose scientists “didn’t know how many people they’d need, or how long they’d have to watch…we don’t know how many people we’d need or how long we’d have to watch,” Vieira said. “But it will be the source of many of our answers.”
This article is one of three published as part of the Rare Disease Fellowship by the National Press Foundation.