The Algorithm Will See You Now
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."
From infections with no symptoms to why men are more likely to be hospitalized in the ICU and die of COVID-19, new research shows that your genes play a significant role
Early in the pandemic, genetic research focused on the virus because it was readily available. Plus, the virus contains only 30,000 bases in a dozen functional genes, so it's relatively easy and affordable to sequence. Additionally, the rapid mutation of the virus and its ability to escape antibody control fueled waves of different variants and provided a reason to follow viral genetics.
In comparison, there are many more genes of the human immune system and cellular functions that affect viral replication, with about 3.2 billion base pairs. Human studies require samples from large numbers of people, the analysis of each sample is vastly more complex, and sophisticated computer analysis often is required to make sense of the raw data. All of this takes time and large amounts of money, but important findings are beginning to emerge.
Asymptomatics
About half the people exposed to SARS-CoV-2, the virus that causes the COVID-19 disease, never develop symptoms of this disease, or their symptoms are so mild they often go unnoticed. One piece of understanding the phenomena came when researchers showed that exposure to OC43, a common coronavirus that results in symptoms of a cold, generates immune system T cells that also help protect against SARS-CoV-2.
Jill Hollenbach, an immunologist at the University of California at San Francisco, sought to identify the gene behind that immune protection. Most COVID-19 genetic studies are done with the most seriously ill patients because they are hospitalized and thus available. “But 99 percent of people who get it will never see the inside of a hospital for COVID-19,” she says. “They are home, they are not interacting with the health care system.”
Early in the pandemic, when most labs were shut down, she tapped into the National Bone Marrow Donor Program database. It contains detailed information on donor human leukocyte antigens (HLAs), key genes in the immune system that must match up between donor and recipient for successful transplants of marrow or organs. Each HLA can contain alleles, slight molecular differences in the DNA of the HLA, which can affect its function. Potential HLA combinations can number in the tens of thousands across the world, says Hollenbach, but each person has a smaller number of those possible variants.
She teamed up with the COVID-19 Citizen Science Study a smartphone-based study to track COVID-19 symptoms and outcomes, to ask persons in the bone marrow donor registry about COVID-19. The study enlisted more than 30,000 volunteers. Those volunteers already had their HLAs annotated by the registry, and 1,428 tested positive for the virus.
Analyzing five key HLAs, she found an allele in the gene HLA-B*15:01 that was significantly overrepresented in people who didn’t have any symptoms. The effect was even stronger if a person had inherited the allele from both parents; these persons were “more than eight times more likely to remain asymptomatic than persons who did not carry the genetic variant,” she says. Altogether this HLA was present in about 10 percent of the general European population but double that percentage in the asymptomatic group. Hollenbach and her colleagues were able confirm this in other different groups of patients.
What made the allele so potent against SARS-CoV-2? Part of the answer came from x-ray crystallography. A key element was the molecular shape of parts of the cold virus OC43 and SARS-CoV-2. They were virtually identical, and the allele could bind very tightly to them, present their molecular antigens to T cells, and generate an extremely potent T cell response to the viruses. And “for whatever reasons that generated a lot of memory T cells that are going to stick around for a long time,” says Hollenbach. “This T cell response is very early in infection and ramps up very quickly, even before the antibody response.”
Understanding the genetics of the immune response to SARS-CoV-2 is important because it provides clues into the conditions of T cells and antigens that support a response without any symptoms, she says. “It gives us an opportunity to think about whether this might be a vaccine design strategy.”
Dead men
A researcher at the Leibniz Institute of Virology in Hamburg Germany, Guelsah Gabriel, was drawn to a question at the other end of the COVID-19 spectrum: why men more likely to be hospitalized and die from the infection. It wasn't that men were any more likely to be exposed to the virus but more likely, how their immune system reacted to it
Several studies had noted that testosterone levels were significantly lower in men hospitalized with COVID-19. And, in general, the lower the testosterone, the worse the prognosis. A year after recovery, about 30 percent of men still had lower than normal levels of testosterone, a condition known as hypogonadism. Most of the men also had elevated levels of estradiol, a female hormone (https://pubmed.ncbi.nlm.nih.gov/34402750/).
Every cell has a sex, expressing receptors for male and female hormones on their surface. Hormones docking with these receptors affect the cells' internal function and the signals they send to other cells. The number and role of these receptors varies from tissue to tissue.
Gabriel began her search by examining whole exome sequences, the protein-coding part of the genome, for key enzymes involved in the metabolism of sex hormones. The research team quickly zeroed in on CYP19A1, an enzyme that converts testosterone to estradiol. The gene that produces this enzyme has a number of different alleles, the molecular variants that affect the enzyme's rate of metabolizing the sex hormones. One genetic variant, CYP19A1 (Thr201Met), is typically found in 6.2 percent of all people, both men and women, but remarkably, they found it in 68.7 percent of men who were hospitalized with COVID-19.
Lung surprise
Lungs are the tissue most affected in COVID-19 disease. Gabriel wondered if the virus might be affecting expression of their target gene in the lung so that it produces more of the enzyme that converts testosterone to estradiol. Studying cells in a petri dish, they saw no change in gene expression when they infected cells of lung tissue with influenza and the original SARS-CoV viruses that caused the SARS outbreak in 2002. But exposure to SARS-CoV-2, the virus responsible for COVID-19, increased gene expression up to 40-fold, Gabriel says.
Did the same thing happen in humans? Autopsy examination of patients in three different cites found that “CYP19A1 was abundantly expressed in the lungs of COVID-19 males but not those who died of other respiratory infections,” says Gabriel. This increased enzyme production led likely to higher levels of estradiol in the lungs of men, which “is highly inflammatory, damages the tissue, and can result in fibrosis or scarring that inhibits lung function and repair long after the virus itself has disappeared.” Somehow the virus had acquired the capacity to upregulate expression of CYP19A1.
Only two COVID-19 positive females showed increased expression of this gene. The menopause status of these women, or whether they were on hormone replacement therapy was not known. That could be important because female hormones have a protective effect for cardiovascular disease, which women often lose after going through menopause, especially if they don’t start hormone replacement therapy. That sex-specific protection might also extend to COVID-19 and merits further study.
The team was able to confirm their findings in golden hamsters, the animal model of choice for studying COVID-19. Testosterone levels in male animals dropped 5-fold three days after infection and began to recover as viral levels declined. CYP19A1 transcription increased up to 15-fold in the lungs of the male but not the females. The study authors wrote, “Virus replication in the male lungs was negatively associated with testosterone levels.”
The medical community studying COVID-19 has slowly come to recognize the importance of adipose tissue, or fat cells. They are known to express abundant levels of CYP19A1 and play a significant role as metabolic tissue in COVID-19. Gabriel adds, “One of the key findings of our study is that upon SARS-CoV-2 infection, the lung suddenly turns into a metabolic organ by highly expressing” CYP19A1.
She also found evidence that SARS-CoV-2 can infect the gonads of hamsters, thereby likely depressing circulating levels of sex hormones. The researchers did not have autopsy samples to confirm this in humans, but others have shown that the virus can replicate in those tissues.
A possible treatment
Back in the lab, substituting low and high doses of testosterone in SARS-COV-2 infected male hamsters had opposite effects depending on testosterone dosage used. Gabriel says that hormone levels can vary so much, depending on health status and age and even may change throughout the day, that “it probably is much better to inhibit the enzyme” produced by CYP19A1 than try to balance the hormones.
Results were better with letrozole, a drug approved to treat hypogonadism in males, which reduces estradiol levels. The drug also showed benefit in male hamsters in terms of less severe disease and faster recovery. She says more details need to be worked out in using letrozole to treat COVID-19, but they are talking with hospitals about clinical trials of the drug.
Gabriel has proposed a four hit explanation of how COVID-19 can be so deadly for men: the metabolic quartet. First is the genetic risk factor of CYP19A1 (Thr201Met), then comes SARS-CoV-2 infection that induces even greater expression of this gene and the deleterious increase of estradiol in the lung. Age-related hypogonadism and the heightened inflammation of obesity, known to affect CYP19A1 activity, are contributing factors in this deadly perfect storm of events.
Studying host genetics, says Gabriel, can reveal new mechanisms that yield promising avenues for further study. It’s also uniting different fields of science into a new, collaborative approach they’re calling “infection endocrinology,” she says.
New device finds breast cancer like earthquake detection
Mammograms are necessary breast cancer checks for women as they reach the recommended screening age between 40 and 50 years. Yet, many find the procedure uncomfortable. “I have large breasts, and to be able to image the full breast, the radiographer had to manipulate my breast within the machine, which took time and was quite uncomfortable,” recalls Angela, who preferred not to disclose her last name.
Breast cancer is the most widespread cancer in the world, affecting 2.3 million women in 2020. Screening exams such as mammograms can help find breast cancer early, leading to timely diagnosis and treatment. If this type of cancer is detected before the disease has spread, the 5-year survival rate is 99 percent. But some women forgo mammograms due to concerns about radiation or painful compression of breasts. Other issues, such as low income and a lack of access to healthcare, can also serve as barriers, especially for underserved populations.
Researchers at the University of Canterbury and startup Tiro Medical in Christchurch, New Zealand are hoping their new device—which doesn’t involve any radiation or compression of the breasts—could increase the accuracy of breast cancer screening, broaden access and encourage more women to get checked. They’re digging into clues from the way buildings move in an earthquake to help detect more cases of this disease.
Earthquake engineering inspires new breast cancer screening tech
What’s underneath a surface affects how it vibrates. Earthquake engineers look at the vibrations of swaying buildings to identify the underlying soil and tissue properties. “As the vibration wave travels, it reflects the stiffness of the material between that wave and the surface,” says Geoff Chase, professor of engineering at the University of Canterbury in Christchurch, New Zealand.
Chase is applying this same concept to breasts. Analyzing the surface motion of the breast as it vibrates could reveal the stiffness of the tissues underneath. Regions of high stiffness could point to cancer, given that cancerous breast tissue can be up to 20 times stiffer than normal tissue. “If in essence every woman’s breast is soft soil, then if you have some granite rocks in there, we’re going to see that on the surface,” explains Chase.
The earthquake-inspired device exceeds the 87 percent sensitivity of a 3D mammogram.
That notion underpins a new breast screening device, the brainchild of Chase. Women lie face down, with their breast being screened inside a circular hole and the nipple resting on a small disc called an actuator. The actuator moves up and down, between one and two millimeters, so there’s a small vibration, “almost like having your phone vibrate on your nipple,” says Jessica Fitzjohn, a postdoctoral fellow at the University of Canterbury who collaborated on the device design with Chase.
Cameras surrounding the device take photos of the breast surface motion as it vibrates. The photos are fed into image processing algorithms that convert them into data points. Then, diagnostic algorithms analyze those data points to find any differences in the breast tissue. “We’re looking for that stiffness contrast which could indicate a tumor,” Fitzjohn says.
A nascent yet promising technology
The device has been tested in a clinical trial of 14 women: one with healthy breasts and 13 with a tumor in one breast. The cohort was small but diverse, varying in age, breast volume and tumor size.
Results from the trial yielded a sensitivity rate, or the likelihood of correctly detecting breast cancer, of 85 percent. Meanwhile, the device’s specificity rate, or the probability of diagnosing healthy breasts, was 77 percent. By combining and optimizing certain diagnostic algorithms, the device reached between 92 and 100 percent sensitivity and between 80 and 86 percent specificity, which is comparable to the latest 3D mammogram technology. Called tomosynthesis, these 3D mammograms take a number of sharper, clearer and more detailed 3D images compared to the single 2D image of a conventional mammogram, and have a specificity score of 92 percent. Although the earthquake-inspired device’s specificity is lower, it exceeds the 87 percent sensitivity of a 3D mammogram.
The team hopes that cameras with better resolution can help improve the numbers. And with a limited amount of data in the first trial, the researchers are looking into funding for another clinical trial to validate their results on a larger cohort size.
Additionally, during the trial, the device correctly identified one woman’s breast as healthy, while her prior mammogram gave a false positive. The device correctly identified it as being healthy tissue. It was also able to capture the tiniest tumor at 7 millimeters—around a third of an inch or half as long as an aspirin tablet.
Diagnostic findings from the device are immediate.
When using the earthquake-inspired device, women lie face down, with their breast being screened inside circular holes.
University of Canterbury.
But more testing is needed to “prove the device’s ability to pick up small breast cancers less than 10 to 15 millimeters in size, as we know that finding cancers when they are small is the best way of improving outcomes,” says Richard Annand, a radiologist at Pacific Radiology in New Zealand. He explains that mammography already detects most precancerous lesions, so if the device will only be able to find large masses or lumps it won’t be particularly useful. While not directly involved in administering the clinical trial for the device, Annand was a director at the time for Canterbury Breastcare, where the trial occurred.
Meanwhile, Monique Gary, a breast surgical oncologist and medical director of the Grand View Health Cancer program in Pennsylvania, U.S., is excited to see new technologies advancing breast cancer screening and early detection. But she notes that the device may be challenging for “patients who are unable to lay prone, such as pregnant women as well as those who are differently abled, and this machine might exclude them.” She adds that it would also be interesting to explore how breast implants would impact the device’s vibrational frequency.
Diagnostic findings from the device are immediate, with the results available “before you put your clothes back on,” Chase says. The absence of any radiation is another benefit, though Annand considers it a minor edge “as we know the radiation dose used in mammography is minimal, and the advantages of having a mammogram far outweigh the potential risk of radiation.”
The researchers also conducted a separate ergonomic trial with 40 women to assess the device’s comfort, safety and ease of use. Angela was part of that trial and described the experience as “easy, quick, painless and required no manual intervention from an operator.” And if a person is uncomfortable being topless or having their breasts touched by someone else, “this type of device would make them more comfortable and less exposed,” she says.
While mammograms remain “the ‘gold standard’ in breast imaging, particularly screening, physicians need an option that can be used in combination with mammography.
Fitzjohn acknowledges that “at the moment, it’s quite a crude prototype—it’s just a block that you lie on.” The team prioritized function over form initially, but they’re now planning a few design improvements, including more cushioning for the breasts and the surface where the women lie on.
While mammograms remains “the ‘gold standard’ in breast imaging, particularly screening, physicians need an option that is good at excluding breast cancer when used in combination with mammography, has good availability, is easy to use and is affordable. There is the possibility that the device could fill this role,” Annand says.
Indeed, the researchers envision their new breast screening device as complementary to mammograms—a prescreening tool that could make breast cancer checks widely available. As the device is portable and doesn’t require specialized knowledge to operate, it can be used in clinics, pop-up screening facilities and rural communities. “If it was easily accessible, particularly as part of a checkup with a [general practitioner] or done in a practice the patient is familiar with, it may encourage more women to access this service,” Angela says. For those who find regular mammograms uncomfortable or can’t afford them, the earthquake-inspired device may be an option—and an even better one.
Broadening access could prompt more women to go for screenings, particularly younger women at higher risk of getting breast cancer because of a family history of the disease or specific gene mutations. “If we can provide an option for them then we can catch those cancers earlier,” Fitzjohn syas. “By taking screening to people, we’re increasing patient-centric care.”
With the team aiming to lower the device’s cost to somewhere between five and eight times less than mammography equipment, it would also be valuable for low-to-middle-income nations that are challenged to afford the infrastructure for mammograms or may not have enough skilled radiologists.
For Fitzjohn, the ultimate goal is to “increase equity in breast screening and catch cancer early so we have better outcomes for women who are diagnosed with breast cancer.”