This App Helps Diagnose Rare Genetic Disorders from a Picture
Medical geneticist Omar Abdul-Rahman had a hunch. He thought that the three-year-old boy with deep-set eyes, a rounded nose, and uplifted earlobes might have Mowat-Wilson syndrome, but he'd never seen a patient with the rare disorder before.
"If it weren't for the app I'm not sure I would have had the confidence to say 'yes you should spend $1000 on this test."
Rahman had already ordered genetic tests for three different conditions without any luck, and he didn't want to cost the family any more money—or hope—if he wasn't sure of the diagnosis. So he took a picture of the boy and uploaded the photo to Face2Gene, a diagnostic aid for rare genetic disorders. Sure enough, Mowat-Wilson came up as a potential match. The family agreed to one final genetic test, which was positive for the syndrome.
"If it weren't for the app I'm not sure I would have had the confidence to say 'yes you should spend $1000 on this test,'" says Rahman, who is now the director of Genetic Medicine at the University of Nebraska Medical Center, but saw the boy when he was in the Department of Pediatrics at the University of Mississippi Medical Center in 2012.
"Families who are dealing with undiagnosed diseases never know what's going to come around the corner, what other organ system might be a problem next week," Rahman says. With a diagnosis, "You don't have to wait for the other shoe to drop because now you know the extent of the condition."
A diagnosis is the first and most important step for patients to attain medical care. Disease prognosis, treatment plans, and emotional coping all stem from this critical phase. But diagnosis can also be the trickiest part of the process, particularly for rare disorders. According to one European survey, 40 percent of rare diseases are initially misdiagnosed.
Healthcare professionals and medical technology companies hope that facial recognition software will help prevent families from facing difficult disruptions due to misdiagnoses.
"Patients with rare diseases or genetic disorders go through a long period of diagnostic odyssey, and just putting a name to a syndrome or finding a diagnosis can be very helpful and relieve a lot of tension for the family," says Dekel Gelbman, CEO of FDNA.
Consequently, a misdiagnosis can be devastating for families. Money and time may have been wasted on fruitless treatments, while opportunities for potentially helpful therapies or clinical trials were missed. Parents led down the wrong path must change their expectations of their child's long-term prognosis and care. In addition, they may be misinformed regarding future decisions about family planning.
Healthcare professionals and medical technology companies hope that facial recognition software will help prevent families from facing these difficult disruptions by improving the accuracy and ease of diagnosing genetic disorders. Traditionally, doctors diagnose these types of conditions by identifying unique patterns of facial features, a practice called dysmorphology. Trained physicians can read a child's face like a map and detect any abnormal ridges or plateaus—wide-set eyes, broad forehead, flat nose, rotated ears—that, combined with other symptoms such as intellectual disability or abnormal height and weight, signify a specific genetic disorder.
These morphological changes can be subtle, though, and often only specialized medical geneticists are able to detect and interpret these facial clues. What's more, some genetic disorders are so rare that even a specialist may not have encountered it before, much less a general practitioner. Diagnosing rare conditions has improved thanks to genomic testing that can confirm (or refute) a doctor's suspicion. Yet with thousands of variants in each person's genome, identifying the culprit mutation or deletion can be extremely difficult if you don't know what you're looking for.
Facial recognition technology is trying to take some of the guesswork out of this process. Software such as the Face2Gene app use machine learning to compare a picture of a patient against images of thousands of disorders and come back with suggestions of possible diagnoses.
"This is a classic field for artificial intelligence because no human being can really have enough knowledge and enough experience to be able to do this for thousands of different disorders."
"When we met a geneticist for the first time we were pretty blown away with the fact that they actually use their own human pattern recognition" to diagnose patients, says Gelbman. "This is a classic field for AI [artificial intelligence], for machine learning because no human being can really have enough knowledge and enough experience to be able to do this for thousands of different disorders."
When a physician uploads a photo to the app, they are given a list of different diagnostic suggestions, each with a heat map to indicate how similar the facial features are to a classic representation of the syndrome. The physician can hone the suggestions by adding in other symptoms or family history. Gelbman emphasized that the app is a "search and reference tool" and should not "be used to diagnose or treat medical conditions." It is not approved by the FDA as a diagnostic.
"As a tool, we've all been waiting for this, something that can help everyone," says Julian Martinez-Agosto, an associate professor in human genetics and pediatrics at UCLA. He sees the greatest benefit of facial recognition technology in its ability to empower non-specialists to make a diagnosis. Many areas, including rural communities or resource-poor countries, do not have access to either medical geneticists trained in these types of diagnostics or genomic screens. Apps like Face2Gene can help guide a general practitioner or flag diseases they might not be familiar with.
One concern is that most textbook images of genetic disorders come from the West, so the "classic" face of a condition is often a child of European descent.
Maximilian Muenke, a senior investigator at the National Human Genome Research Institute (NHGRI), agrees that in many countries, facial recognition programs could be the only way for a doctor to make a diagnosis.
"There are only geneticists in countries like the U.S., Canada, Europe, Japan. In most countries, geneticists don't exist at all," Muenke says. "In Nigeria, the most populous country in all of Africa with 160 million people, there's not a single clinical geneticist. So in a country like that, facial recognition programs will be sought after and will be extremely useful to help make a diagnosis to the non-geneticists."
One concern about providing this type of technology to a global population is that most textbook images of genetic disorders come from the West, so the "classic" face of a condition is often a child of European descent. However, the defining facial features of some of these disorders manifest differently across ethnicities, leaving clinicians from other geographic regions at a disadvantage.
"Every syndrome is either more easy or more difficult to detect in people from different geographic backgrounds," explains Muenke. For example, "in some countries of Southeast Asia, the eyes are slanted upward, and that happens to be one of the findings that occurs mostly with children with Down Syndrome. So then it might be more difficult for some individuals to recognize Down Syndrome in children from Southeast Asia."
There is a risk that providing this type of diagnostic information online will lead to parents trying to classify their own children.
To combat this issue, Muenke helped develop the Atlas of Human Malformation Syndromes, a database that incorporates descriptions and pictures of patients from every continent. By providing examples of rare genetic disorders in children from outside of the United States and Europe, Muenke hopes to provide clinicians with a better understanding of what to look for in each condition, regardless of where they practice.
There is a risk that providing this type of diagnostic information online will lead to parents trying to classify their own children. Face2Gene is free to download in the app store, although users must be authenticated by the company as a healthcare professional before they can access the database. The NHGRI Atlas can be accessed by anyone through their website. However, Martinez and Muenke say parents already use Google and WebMD to look up their child's symptoms; facial recognition programs and databases are just an extension of that trend. In fact, Martinez says, "Empowering families is another way to facilitate access to care. Some families live in rural areas and have no access to geneticists. If they can use software to get a diagnosis and then contact someone at a large hospital, it can help facilitate the process."
Martinez also says the app could go further by providing greater transparency about how the program makes its assessments. Giving clinicians feedback about why a diagnosis fits certain facial features would offer a valuable teaching opportunity in addition to a diagnostic aid.
Both Martinez and Muenke think the technology is an innovation that could vastly benefit patients. "In the beginning, I was quite skeptical and I could not believe that a machine could replace a human," says Muenke. "However, I am a convert that it actually can help tremendously in making a diagnosis. I think there is a place for facial recognition programs, and I am a firm believer that this will spread over the next five years."
DNA- and RNA-based electronic implants may revolutionize healthcare
Implantable electronic devices can significantly improve patients’ quality of life. A pacemaker can encourage the heart to beat more regularly. A neural implant, usually placed at the back of the skull, can help brain function and encourage higher neural activity. Current research on neural implants finds them helpful to patients with Parkinson’s disease, vision loss, hearing loss, and other nerve damage problems. Several of these implants, such as Elon Musk’s Neuralink, have already been approved by the FDA for human use.
Yet, pacemakers, neural implants, and other such electronic devices are not without problems. They require constant electricity, limited through batteries that need replacements. They also cause scarring. “The problem with doing this with electronics is that scar tissue forms,” explains Kate Adamala, an assistant professor of cell biology at the University of Minnesota Twin Cities. “Anytime you have something hard interacting with something soft [like muscle, skin, or tissue], the soft thing will scar. That's why there are no long-term neural implants right now.” To overcome these challenges, scientists are turning to biocomputing processes that use organic materials like DNA and RNA. Other promised benefits include “diagnostics and possibly therapeutic action, operating as nanorobots in living organisms,” writes Evgeny Katz, a professor of bioelectronics at Clarkson University, in his book DNA- And RNA-Based Computing Systems.
While a computer gives these inputs in binary code or "bits," such as a 0 or 1, biocomputing uses DNA strands as inputs, whether double or single-stranded, and often uses fluorescent RNA as an output.
Adamala’s research focuses on developing such biocomputing systems using DNA, RNA, proteins, and lipids. Using these molecules in the biocomputing systems allows the latter to be biocompatible with the human body, resulting in a natural healing process. In a recent Nature Communications study, Adamala and her team created a new biocomputing platform called TRUMPET (Transcriptional RNA Universal Multi-Purpose GatE PlaTform) which acts like a DNA-powered computer chip. “These biological systems can heal if you design them correctly,” adds Adamala. “So you can imagine a computer that will eventually heal itself.”
The basics of biocomputing
Biocomputing and regular computing have many similarities. Like regular computing, biocomputing works by running information through a series of gates, usually logic gates. A logic gate works as a fork in the road for an electronic circuit. The input will travel one way or another, giving two different outputs. An example logic gate is the AND gate, which has two inputs (A and B) and two different results. If both A and B are 1, the AND gate output will be 1. If only A is 1 and B is 0, the output will be 0 and vice versa. If both A and B are 0, the result will be 0. While a computer gives these inputs in binary code or "bits," such as a 0 or 1, biocomputing uses DNA strands as inputs, whether double or single-stranded, and often uses fluorescent RNA as an output. In this case, the DNA enters the logic gate as a single or double strand.
If the DNA is double-stranded, the system “digests” the DNA or destroys it, which results in non-fluorescence or “0” output. Conversely, if the DNA is single-stranded, it won’t be digested and instead will be copied by several enzymes in the biocomputing system, resulting in fluorescent RNA or a “1” output. And the output for this type of binary system can be expanded beyond fluorescence or not. For example, a “1” output might be the production of the enzyme insulin, while a “0” may be that no insulin is produced. “This kind of synergy between biology and computation is the essence of biocomputing,” says Stephanie Forrest, a professor and the director of the Biodesign Center for Biocomputing, Security and Society at Arizona State University.
Biocomputing circles are made of DNA, RNA, proteins and even bacteria.
Evgeny Katz
The TRUMPET’s promise
Depending on whether the biocomputing system is placed directly inside a cell within the human body, or run in a test-tube, different environmental factors play a role. When an output is produced inside a cell, the cell's natural processes can amplify this output (for example, a specific protein or DNA strand), creating a solid signal. However, these cells can also be very leaky. “You want the cells to do the thing you ask them to do before they finish whatever their businesses, which is to grow, replicate, metabolize,” Adamala explains. “However, often the gate may be triggered without the right inputs, creating a false positive signal. So that's why natural logic gates are often leaky." While biocomputing outside a cell in a test tube can allow for tighter control over the logic gates, the outputs or signals cannot be amplified by a cell and are less potent.
TRUMPET, which is smaller than a cell, taps into both cellular and non-cellular biocomputing benefits. “At its core, it is a nonliving logic gate system,” Adamala states, “It's a DNA-based logic gate system. But because we use enzymes, and the readout is enzymatic [where an enzyme replicates the fluorescent RNA], we end up with signal amplification." This readout means that the output from the TRUMPET system, a fluorescent RNA strand, can be replicated by nearby enzymes in the platform, making the light signal stronger. "So it combines the best of both worlds,” Adamala adds.
These organic-based systems could detect cancer cells or low insulin levels inside a patient’s body.
The TRUMPET biocomputing process is relatively straightforward. “If the DNA [input] shows up as single-stranded, it will not be digested [by the logic gate], and you get this nice fluorescent output as the RNA is made from the single-stranded DNA, and that's a 1,” Adamala explains. "And if the DNA input is double-stranded, it gets digested by the enzymes in the logic gate, and there is no RNA created from the DNA, so there is no fluorescence, and the output is 0." On the story's leading image above, if the tube is "lit" with a purple color, that is a binary 1 signal for computing. If it's "off" it is a 0.
While still in research, TRUMPET and other biocomputing systems promise significant benefits to personalized healthcare and medicine. These organic-based systems could detect cancer cells or low insulin levels inside a patient’s body. The study’s lead author and graduate student Judee Sharon is already beginning to research TRUMPET's ability for earlier cancer diagnoses. Because the inputs for TRUMPET are single or double-stranded DNA, any mutated or cancerous DNA could theoretically be detected from the platform through the biocomputing process. Theoretically, devices like TRUMPET could be used to detect cancer and other diseases earlier.
Adamala sees TRUMPET not only as a detection system but also as a potential cancer drug delivery system. “Ideally, you would like the drug only to turn on when it senses the presence of a cancer cell. And that's how we use the logic gates, which work in response to inputs like cancerous DNA. Then the output can be the production of a small molecule or the release of a small molecule that can then go and kill what needs killing, in this case, a cancer cell. So we would like to develop applications that use this technology to control the logic gate response of a drug’s delivery to a cell.”
Although platforms like TRUMPET are making progress, a lot more work must be done before they can be used commercially. “The process of translating mechanisms and architecture from biology to computing and vice versa is still an art rather than a science,” says Forrest. “It requires deep computer science and biology knowledge,” she adds. “Some people have compared interdisciplinary science to fusion restaurants—not all combinations are successful, but when they are, the results are remarkable.”
In today’s podcast episode, Leaps.org Deputy Editor Lina Zeldovich speaks about the health and ecological benefits of farming crickets for human consumption with Bicky Nguyen, who joins Lina from Vietnam. Bicky and her business partner Nam Dang operate an insect farm named CricketOne. Motivated by the idea of sustainable and healthy protein production, they started their unconventional endeavor a few years ago, despite numerous naysayers who didn’t believe that humans would ever consider munching on bugs.
Yet, making creepy crawlers part of our diet offers many health and planetary advantages. Food production needs to match the rise in global population, estimated to reach 10 billion by 2050. One challenge is that some of our current practices are inefficient, polluting and wasteful. According to nonprofit EarthSave.org, it takes 2,500 gallons of water, 12 pounds of grain, 35 pounds of topsoil and the energy equivalent of one gallon of gasoline to produce one pound of feedlot beef, although exact statistics vary between sources.
Meanwhile, insects are easy to grow, high on protein and low on fat. When roasted with salt, they make crunchy snacks. When chopped up, they transform into delicious pâtes, says Bicky, who invents her own cricket recipes and serves them at industry and public events. Maybe that’s why some research predicts that edible insects market may grow to almost $10 billion by 2030. Tune in for a delectable chat on this alternative and sustainable protein.
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Further reading:
More info on Bicky Nguyen
https://yseali.fulbright.edu.vn/en/faculty/bicky-n...
The environmental footprint of beef production
https://www.earthsave.org/environment.htm
https://www.watercalculator.org/news/articles/beef-king-big-water-footprints/
https://www.frontiersin.org/articles/10.3389/fsufs.2019.00005/full
https://ourworldindata.org/carbon-footprint-food-methane
Insect farming as a source of sustainable protein
https://www.insectgourmet.com/insect-farming-growing-bugs-for-protein/
https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/insect-farming
Cricket flour is taking the world by storm
https://www.cricketflours.com/
https://talk-commerce.com/blog/what-brands-use-cricket-flour-and-why/
Lina Zeldovich has written about science, medicine and technology for Popular Science, Smithsonian, National Geographic, Scientific American, Reader’s Digest, the New York Times and other major national and international publications. A Columbia J-School alumna, she has won several awards for her stories, including the ASJA Crisis Coverage Award for Covid reporting, and has been a contributing editor at Nautilus Magazine. In 2021, Zeldovich released her first book, The Other Dark Matter, published by the University of Chicago Press, about the science and business of turning waste into wealth and health. You can find her on http://linazeldovich.com/ and @linazeldovich.