When doctors couldn’t stop her daughter’s seizures, this mom earned a PhD and found a treatment herself.
Savannah Salazar (left) and her mother, Tracy Dixon-Salazaar, who earned a PhD in neurobiology in the quest for a treatment of her daughter's seizure disorder.
Twenty-eight years ago, Tracy Dixon-Salazaar woke to the sound of her daughter, two-year-old Savannah, in the midst of a medical emergency.
“I entered [Savannah’s room] to see her tiny little body jerking about violently in her bed,” Tracy said in an interview. “I thought she was choking.” When she and her husband frantically called 911, the paramedic told them it was likely that Savannah had had a seizure—a term neither Tracy nor her husband had ever heard before.
Over the next several years, Savannah’s seizures continued and worsened. By age five Savannah was having seizures dozens of times each day, and her parents noticed significant developmental delays. Savannah was unable to use the restroom and functioned more like a toddler than a five-year-old.
Doctors were mystified: Tracy and her husband had no family history of seizures, and there was no event—such as an injury or infection—that could have caused them. Doctors were also confused as to why Savannah’s seizures were happening so frequently despite trying different seizure medications.
Doctors eventually diagnosed Savannah with Lennox-Gaustaut Syndrome, or LGS, an epilepsy disorder with no cure and a poor prognosis. People with LGS are often resistant to several kinds of anti-seizure medications, and often suffer from developmental delays and behavioral problems. People with LGS also have a higher chance of injury as well as a higher chance of sudden unexpected death (SUDEP) due to the frequent seizures. In about 70 percent of cases, LGS has an identifiable cause such as a brain injury or genetic syndrome. In about 30 percent of cases, however, the cause is unknown.
Watching her daughter struggle through repeated seizures was devastating to Tracy and the rest of the family.
“This disease, it comes into your life. It’s uninvited. It’s unannounced and it takes over every aspect of your daily life,” said Tracy in an interview with Today.com. “Plus it’s attacking the thing that is most precious to you—your kid.”
Desperate to find some answers, Tracy began combing the medical literature for information about epilepsy and LGS. She enrolled in college courses to better understand the papers she was reading.
“Ironically, I thought I needed to go to college to take English classes to understand these papers—but soon learned it wasn’t English classes I needed, It was science,” Tracy said. When she took her first college science course, Tracy says, she “fell in love with the subject.”
Tracy was now a caregiver to Savannah, who continued to have hundreds of seizures a month, as well as a full-time student, studying late into the night and while her kids were at school, using classwork as “an outlet for the pain.”
“I couldn’t help my daughter,” Tracy said. “Studying was something I could do.”
Twelve years later, Tracy had earned a PhD in neurobiology.
After her post-doctoral training, Tracy started working at a lab that explored the genetics of epilepsy. Savannah’s doctors hadn’t found a genetic cause for her seizures, so Tracy decided to sequence her genome again to check for other abnormalities—and what she found was life-changing.
Tracy discovered that Savannah had a calcium channel mutation, meaning that too much calcium was passing through Savannah’s neural pathways, leading to seizures. The information made sense to Tracy: Anti-seizure medications often leech calcium from a person’s bones. When doctors had prescribed Savannah calcium supplements in the past to counteract these effects, her seizures had gotten worse every time she took the medication. Tracy took her discovery to Savannah’s doctor, who agreed to prescribe her a calcium blocker.
The change in Savannah was almost immediate.
Within two weeks, Savannah’s seizures had decreased by 95 percent. Once on a daily seven-drug regimen, she was soon weaned to just four, and then three. Amazingly, Tracy started to notice changes in Savannah’s personality and development, too.
“She just exploded in her personality and her talking and her walking and her potty training and oh my gosh she is just so sassy,” Tracy said in an interview.
Since starting the calcium blocker eleven years ago, Savannah has continued to make enormous strides. Though still unable to read or write, Savannah enjoys puzzles and social media. She’s “obsessed” with boys, says Tracy. And while Tracy suspects she’ll never be able to live independently, she and her daughter can now share more “normal” moments—something she never anticipated at the start of Savannah’s journey with LGS. While preparing for an event, Savannah helped Tracy get ready.
“We picked out a dress and it was the first time in our lives that we did something normal as a mother and a daughter,” she said. “It was pretty cool.”
Artificial intelligence in medicine, still in an early phase, stands to transform how doctors and nurses spend their time.
There's a quiet revolution going on in medicine. It's driven by artificial intelligence, but paradoxically, new technology may put a more human face on healthcare.
AI's usefulness in healthcare ranges far and wide.
Artificial intelligence is software that can process massive amounts of information and learn over time, arriving at decisions with striking accuracy and efficiency. It offers greater accuracy in diagnosis, exponentially faster genome sequencing, the mining of medical literature and patient records at breathtaking speed, a dramatic reduction in administrative bureaucracy, personalized medicine, and even the democratization of healthcare.
The algorithms that bring these advantages won't replace doctors; rather, by offloading some of the most time-consuming tasks in healthcare, providers will be able to focus on personal interactions with patients—listening, empathizing, educating and generally putting the care back in healthcare. The relationship can focus on the alleviation of suffering, both the physical and emotional kind.
Challenges of Getting AI Up and Running
The AI revolution, still in its early phase in medicine, is already spurring some amazing advances, despite the fact that some experts say it has been overhyped. IBM's Watson Health program is a case in point. IBM capitalized on Watson's ability to process natural language by designing algorithms that devour data like medical articles and analyze images like MRIs and medical slides. The algorithms help diagnose diseases and recommend treatment strategies.
But Technology Review reported that a heavily hyped partnership with the MD Anderson Cancer Center in Houston fell apart in 2017 because of a lack of data in the proper format. The data existed, just not in a way that the voraciously data-hungry AI could use to train itself.
The hiccup certainly hasn't dampened the enthusiasm for medical AI among other tech giants, including Google and Apple, both of which have invested billions in their own healthcare projects. At this point, the main challenge is the need for algorithms to interpret a huge diversity of data mined from medical records. This can include everything from CT scans, MRIs, electrocardiograms, x-rays, and medical slides, to millions of pages of medical literature, physician's notes, and patient histories. It can even include data from implantables and wearables such as the Apple Watch and blood sugar monitors.
None of this information is in anything resembling a standard format across and even within hospitals, clinics, and diagnostic centers. Once the algorithms are trained, however, they can crunch massive amounts of data at blinding speed, with an accuracy that matches and sometimes even exceeds that of highly experienced doctors.
Genome sequencing, for example, took years to accomplish as recently as the early 2000s. The Human Genome Project, the first sequencing of the human genome, was an international effort that took 13 years to complete. In April of this year, Rady Children's Institute for Genomic Medicine in San Diego used an AI-powered genome sequencing algorithm to diagnose rare genetic diseases in infants in about 20 hours, according to ScienceDaily.
"Patient care will always begin and end with the doctor."
Dr. Stephen Kingsmore, the lead author of an article published in Science Translational Medicine, emphasized that even though the algorithm helped guide the treatment strategies of neonatal intensive care physicians, the doctor was still an indispensable link in the chain. "Some people call this artificial intelligence, we call it augmented intelligence," he says. "Patient care will always begin and end with the doctor."
One existing trend is helping to supply a great amount of valuable data to algorithms—the electronic health record. Initially blamed for exacerbating the already crushing workload of many physicians, the EHR is emerging as a boon for algorithms because it consolidates all of a patient's data in one record.
Examples of AI in Action Around the Globe
If you're a parent who has ever taken a child to the doctor with flulike symptoms, you know the anxiety of wondering if the symptoms signal something serious. Kang Zhang, M.D., Ph.D., the founding director of the Institute for Genomic Medicine at the University of California at San Diego, and colleagues developed an AI natural language processing model that used deep learning to analyze the EHRs of 1.3 million pediatric visits to a clinic in Guanzhou, China.
The AI identified common childhood diseases with about the same accuracy as human doctors, and it was even able to split the diagnoses into two categories—common conditions such as flu, and serious, life-threatening conditions like meningitis. Zhang has emphasized that the algorithm didn't replace the human doctor, but it did streamline the diagnostic process and could be used in a triage capacity when emergency room personnel need to prioritize the seriously ill over those suffering from common, less dangerous ailments.
AI's usefulness in healthcare ranges far and wide. In Uganda and several other African nations, AI is bringing modern diagnostics to remote villages that have no access to traditional technologies such as x-rays. The New York Times recently reported that there, doctors are using a pocket-sized, hand-held ultrasound machine that works in concert with a cell phone to image and diagnose everything from pneumonia (a common killer of children) to cancerous tumors.
The beauty of the highly portable, battery-powered device is that ultrasound images can be uploaded on computers so that physicians anywhere in the world can review them and weigh in with their advice. And the images are instantly incorporated into the patient's EHR.
Jonathan Rothberg, the founder of Butterfly Network, the Connecticut company that makes the device, told The New York Times that "Two thirds of the world's population gets no imaging at all. When you put something on a chip, the price goes down and you democratize it." The Butterfly ultrasound machine, which sells for $2,000, promises to be a game-changer in remote areas of Africa, South America, and Asia, as well as at the bedsides of patients in developed countries.
AI algorithms are rapidly emerging in healthcare across the U.S. and the world. China has become a major international player, set to surpass the U.S. this year in AI capital investment, the translation of AI research into marketable products, and even the number of often-cited research papers on AI. So far the U.S. is still the leader, but some experts describe the relationship between the U.S. and China as an AI cold war.
"The future of machine learning isn't sentient killer robots. It's longer human lives."
The U.S. Food and Drug Administration expanded its approval of medical algorithms from two in all of 2017 to about two per month throughout 2018. One of the first fields to be impacted is ophthalmology.
One algorithm, developed by the British AI company DeepMind (owned by Alphabet, the parent company of Google), instantly scans patients' retinas and is able to diagnose diabetic retinopathy without needing an ophthalmologist to interpret the scans. This means diabetics can get the test every year from their family physician without having to see a specialist. The Financial Times reported in March that the technology is now being used in clinics throughout Europe.
In Copenhagen, emergency service dispatchers are using a new voice-processing AI called Corti to analyze the conversations in emergency phone calls. The algorithm analyzes the verbal cues of callers, searches its huge database of medical information, and provides dispatchers with onscreen diagnostic information. Freddy Lippert, the CEO of EMS Copenhagen, notes that the algorithm has already saved lives by expediting accurate diagnoses in high-pressure situations where time is of the essence.
Researchers at the University of Nottingham in the UK have even developed a deep learning algorithm that predicts death more accurately than human clinicians. The algorithm incorporates data from a huge range of factors in a chronically ill population, including how many fruits and vegetables a patient eats on a daily basis. Dr. Stephen Weng, lead author of the study, published in PLOS ONE, said in a press release, "We found machine learning algorithms were significantly more accurate in predicting death than the standard prediction models developed by a human expert."
New digital technologies are allowing patients to participate in their healthcare as never before. A feature of the new Apple Watch is an app that detects cardiac arrhythmias and even produces an electrocardiogram if an abnormality is detected. The technology, approved by the FDA, is helping cardiologists monitor heart patients and design interventions for those who may be at higher risk of a cardiac event like a stroke.
If having an algorithm predict your death sends a shiver down your spine, consider that algorithms may keep you alive longer. In 2018, technology reporter Tristan Greene wrote for Medium that "…despite the unending deluge of panic-ridden articles declaring AI the path to apocalypse, we're now living in a world where algorithms save lives every day. The future of machine learning isn't sentient killer robots. It's longer human lives."
The Risks of AI Compiling Your Data
To be sure, the advent of AI-infused medical technology is not without its risks. One risk is that the use of AI wearables constantly monitoring our vital signs could turn us into a nation of hypochondriacs, racing to our doctors every time there's a blip in some vital sign. Such a development could stress an already overburdened system that suffers from, among other things, a shortage of doctors and nurses. Another risk has to do with the privacy protections on the massive repository of intimately personal information that AI will have on us.
In an article recently published in the Journal of the American Medical Association, Australian researcher Kit Huckvale and colleagues examined the handling of data by 36 smartphone apps that assisted people with either depression or smoking cessation, two areas that could lend themselves to stigmatization if they fell into the wrong hands.
Out of the 36 apps, 33 shared their data with third parties, despite the fact that just 25 of those apps had a privacy policy at all and out of those, only 23 stated that data would be shared with third parties. The recipients of all that data? It went almost exclusively to Facebook and Google, to be used for advertising and marketing purposes. But there's nothing to stop it from ending up in the hands of insurers, background databases, or any other entity.
Even when data isn't voluntarily shared, any digital information can be hacked. EHRs and even wearable devices share the same vulnerability as any other digital record or device. Still, the promise of AI to radically improve efficiency and accuracy in healthcare is hard to ignore.
AI Can Help Restore Humanity to Medicine
Eric Topol, director of the Scripps Research Translational Institute and author of the new book Deep Medicine, says that AI gives doctors and nurses the most precious gift of all: time.
Topol welcomes his patients' use of the Apple Watch cardiac feature and is optimistic about the ways that AI is revolutionizing medicine. He says that the watch helps doctors monitor how well medications are working and has already helped to prevent strokes. But in addition to that, AI will help bring the humanity back to a profession that has become as cold and hard as a stainless steel dissection table.
"When I graduated from medical school in the 1970s," he says, "you had a really intimate relationship with your doctor." Over the decades, he has seen that relationship steadily erode as medical organizations demanded that doctors see more and more patients within ever-shrinking time windows.
"Doctors have no time to think, to communicate. We need to restore the mission in medicine."
In addition to that, EHRs have meant that doctors and nurses are getting buried in paperwork and administrative tasks. This is no doubt one reason why a recent study by the World Health Organization showed that worldwide, about 50 percent of doctors suffer from burnout. People who are utterly exhausted make more mistakes, and medical clinicians are no different from the rest of us. Only medical mistakes have unacceptably high stakes. According to its website, Johns Hopkins University recently announced that in the U.S. alone, 250,000 people die from medical mistakes each year.
"Doctors have no time to think, to communicate," says Topol. "We need to restore the mission in medicine." AI is giving doctors more time to devote to the thing that attracted them to medicine in the first place—connecting deeply with patients.
There is a real danger at this juncture, though, that administrators aware of the time-saving aspects of AI will simply push doctors to see more patients, read more tests, and embrace an even more crushing workload.
"We can't leave it to the administrators to just make things worse," says Topol. "Now is the time for doctors to advocate for a restoration of the human touch. We need to stand up for patients and for the patient-doctor relationship."
AI could indeed be a game changer, he says, but rather than squander the huge benefits of more time, "We need a new equation going forward."
This Special Music Helped Preemie Babies’ Brains Develop
Listening to music helped preterm babies' brains develop, according to the results of a new Swiss study.
Move over, Baby Einstein: New research from Switzerland shows that listening to soothing music in the first weeks of life helps encourage brain development in preterm babies.
For the study, the scientists recruited a harpist and a new-age musician to compose three pieces of music.
The Lowdown
Children who are born prematurely, between 24 and 32 weeks of pregnancy, are far more likely to survive today than they used to be—but because their brains are less developed at birth, they're still at high risk for learning difficulties and emotional disorders later in life.
Researchers in Geneva thought that the unfamiliar and stressful noises in neonatal intensive care units might be partially responsible. After all, a hospital ward filled with alarms, other infants crying, and adults bustling in and out is far more disruptive than the quiet in-utero environment the babies are used to. They decided to test whether listening to pleasant music could have a positive, counterbalancing effect on the babies' brain development.
Led by Dr. Petra Hüppi at the University of Geneva, the scientists recruited Swiss harpist and new-age musician Andreas Vollenweider (who has collaborated with the likes of Carly Simon, Bryan Adams, and Bobby McFerrin). Vollenweider developed three pieces of music specifically for the NICU babies, which were played for them five times per week. Each track was used for specific purposes: To help the baby wake up; to stimulate a baby who was already awake; and to help the baby fall back asleep.
When they reached an age equivalent to a full-term baby, the infants underwent an MRI. The researchers focused on connections within the salience network, which determines how relevant information is, and then processes and acts on it—crucial components of healthy social behavior and emotional regulation. The neural networks of preemies who had listened to Vollenweider's pieces were stronger than preterm babies who had not received the intervention, and were instead much more similar to full-term babies.
Next Up
The first infants in the study are now 6 years old—the age when cognitive problems usually become diagnosable. Researchers plan to follow up with more cognitive and socio-emotional assessments, to determine whether the effects of the music intervention have lasted.
The first infants in the study are now 6 years old—the age when cognitive problems usually become diagnosable.
The scientists note in their paper that, while they saw strong results in the babies' primary auditory cortex and thalamus connections—suggesting that they had developed an ability to recognize and respond to familiar music—there was less reaction in the regions responsible for socioemotional processing. They hypothesize that more time spent listening to music during a NICU stay could improve those connections as well; but another study would be needed to know for sure.
Open Questions
Because this initial study had a fairly small sample size (only 20 preterm infants underwent the musical intervention, with another 19 studied as a control group), and they all listened to the same music for the same amount of time, it's still undetermined whether variations in the type and frequency of music would make a difference. Are Vollenweider's harps, bells, and punji the runaway favorite, or would other styles of music help, too? (Would "Baby Shark" help … or hurt?) There's also a chance that other types of repetitive sounds, like parents speaking or singing to their children, might have similar effects.
But the biggest question is still the one that the scientists plan to tackle next: Whether the intervention lasts as the children grow up. If it does, that's great news for any family with a preemie — and for the baby-sized headphone industry.