A woman checking her breast for the presence of concerning lumps.
Nancy Cappello was proactive. When she turned 36, she had a baseline mammogram, a standard medical recommendation in the late 1980s and early 1990s as a comparison tool for future screenings. At 40, Cappello started getting them annually.
Her breast surgeon estimated the cancer had been festering for four to five years under the radar of her annual mammograms.
Six weeks after her 11th-consecutive normal mammogram, she was diagnosed with Stage IIIc breast cancer.
A doctor felt a lump while doing a breast exam during her annual physical and a subsequent ultrasound detected cancer that had spread to 13 lymph nodes. That's when Cappello, then 51, learned she had dense breast tissue, making mammography less likely to detect tumors in her breasts.
She also discovered through her own research that she was among the 40 to 50 percent of women with dense breast tissue — almost half the female population — but medical protocol did not require physicians to inform women of their dense tissue status. If she had known, she said, she would have gotten an ultrasound every year in addition to a mammogram that could have detected the cancer much earlier. Cappello said her breast surgeon estimated the cancer had been festering for four to five years under the radar of her annual mammograms.
Although ultrasound as a cancer screening tool has been available for decades, technological advances are helping doctors find more invasive cancers in women with dense breasts, in turn giving women who know their tissue status the opportunity for earlier detection and treatment.
"We know that the gold standard for breast cancer screening is mammography, but in women with dense breast tissue, up to one third of breast cancers can be missed with this modality alone."
Dr. Georgia Giakoumis Spear, chief of the department of breast imaging at NorthShore University HealthSystem in suburban Chicago and assistant professor of radiology at the University of Chicago, has been a leader in developing standards for the use of new ultrasound technology. She is leading a study to develop more specific national guidelines around the use of Automated Whole Breast Tissue Ultrasound (ABUS), a non-invasive procedure in which sound waves are used to scan breast tissue while a patient lies on her back with her arm over her head.
Approved by the Food and Drug Administration in 2012, ABUS provides higher quality 3D images and faster delivery to provide more accurate results than past ultrasound technology. The scan does not involve radiation, and a practitioner can complete the process in 15 to 20 minutes, from patient preparation to image creation. NorthShore has been using ABUS since 2015, Dr. Spear said, and the technology can improve breast cancer detection in women with dense breasts by up to 55 percent.
"We know that the gold standard for breast cancer screening is mammography, but in women with dense breast tissue, up to one third of breast cancers can be missed with this modality alone," Spear says. "And when we supplement screening with ultrasound in this population of women, we have found a large number of cancers by ultrasound that are not visible on the mammogram."
Mammography should still be used as the first step for breast cancer detection, but if an initial mammogram shows that a patient has dense breast tissue, studies encourage discussion of additional screening with ultrasound.
On a mammogram, dense tissue appears white. So do cancerous masses, making them easy to miss.
A radiologist determines tissue density, according to the American College of Radiology's Breast Imaging Reporting and Data System (BI-RADS). "A" and "B" breast density categories designate ratios of mostly fatty, or non-dense tissues, while the "C" and "D" categories designate heterogeneously dense and extremely dense tissue, respectively. Such patients would be classified as having dense tissue. Younger women, women with lower levels of body fat and women undergoing hormone therapy are more likely to have C and D breast density.
On a mammogram, dense tissue appears white. So do cancerous masses, making them easy to miss. Fatty tissue, in comparison, appears black, making tumors easier to spot.
The FDA stated among its policy goals for 2018 that it's placing an improved focus on recognizing technological advances to help "ensure women get the most relevant, up-to-date information about their breast density, which is now recognized as a risk factor for breast cancer." An article in the March 2018 Journal of the American College of Radiology recommended supplemental screening for women with higher-than-average breast cancer risk, placing women with dense breast tissue in that category.
To be sure, some in the medical community are reluctant to push for ultrasounds, saying that a mammogram might be enough even if the woman has dense breast tissue. A patient is advised to discuss the option of ultrasound with her physician and they can decide from there.
Access to such information became political for Cappello after her diagnosis in 2004. She said that as she underwent six surgeries, a mastectomy, chemotherapy, radiation and hormone therapy, she asked doctors why they weren't required to inform women of their dense breast tissue status. Her dissatisfaction with their responses led to the formation of Are You Dense, Inc., an advocacy group aimed to inform women of their medical options while working to pass legislation mandating that women know their tissue status. Other legislation has focused on mandating insurance coverage for breast ultrasounds.
Nancy Cappello.
(Courtesy)
Cappello's work led Connecticut to become the first state to pass an information law in 2009, and 35 states now have similar requirements. Depending on the state, the law could mandate that certain language or information about breast density be included in the patient's mammogram results, or require physicians to tell women about dense tissue if their breast density falls in the BI-RADS categories C and D. Other states might require that patients be given general information about breast density and advice to discuss their options with a physician. (Note: There is a chart on Cappello's website that shows what laws exist – or don't – in each state.)
Through her site and social media, she's connected with other women who've lobbied for laws in their states, including Dr. Spear, who recently testified before legislative committees in Illinois as they considered companion bills. The Illinois legislation is expected to be signed into law this summer.
"There should be no excuses," Cappello says. "Women should have this information. There should be no concealing or hiding of her status."
Artificial Intelligence is already helping to grow some of the food we eat.
The chef-farmers choose from among 45 types of herb and leafy-greens seeds, plop them into grow trays, and a few weeks later they pick and serve. While success is predicated on at least a small amount of these humans’ care, the systems are autonomously surveilled round-the-clock from Babylon’s base of operations. And artificial intelligence is helping to run the show.
Babylon piloted the use of specialized cameras that take pictures in different spectrums to gather some less-obvious visual data about plants’ wellbeing and alert people if something seems off.
Imagine consistently perfect greens and tomatoes and strawberries, grown hyper-locally, using less water, without chemicals or environmental contaminants. This is the hefty promise of controlled environment agriculture (CEA) — basically, indoor farms that can be hydroponic, aeroponic (plant roots are suspended and fed through misting), or aquaponic (where fish play a role in fertilizing vegetables). But whether they grow 4,160 leafy-green servings per year, like one Babylon farm, or millions of servings, like some of the large, centralized facilities starting to supply supermarkets across the U.S., they seek to minimize failure as much as possible.
Babylon’s soilless hydroponic growing system
Courtesy Babylon Micro-Farms
Here, AI is starting to play a pivotal role. CEA growers use it to help “make sense of what’s happening” to the plants in their care, says Scott Lowman, vice president of applied research at the Institute for Advanced Learning and Research (IALR) in Virginia, a state that’s investing heavily in CEA companies. And although these companies say they’re not aiming for a future with zero human employees, AI is certainly poised to take a lot of human farming intervention out of the equation — for better and worse.
Most of these companies are compiling their own data sets to identify anything that might block the success of their systems. Babylon had already integrated sensor data into its farms to measure heat and humidity, the nutrient content of water, and the amount of light plants receive. Last year, they got a National Science Foundation grant that allowed them to pilot the use of specialized cameras that take pictures in different spectrums to gather some less-obvious visual data about plants’ wellbeing and alert people if something seems off. “Will this plant be healthy tomorrow? Are there things…that the human eye can't see that the plant starts expressing?” says Amandeep Ratte, the company’s head of data science. “If our system can say, Hey, this plant is unhealthy, we can reach out to [users] preemptively about what they’re doing wrong, or is there a disease at the farm?” Ratte says. The earlier the better, to avoid crop failures.
Natural light accounts for 70 percent of Greenswell Growers’ energy use on a sunny day.
Courtesy Greenswell Growers
IALR’s Lowman says that other CEA companies are developing their AI systems to account for the different crops they grow — lettuces come in all shapes and sizes, after all, and each has different growing needs than, for example, tomatoes. The ways they run their operations differs also. Babylon is unusual in its decentralized structure. But centralized growing systems with one main location have variabilities, too. AeroFarms, which recently declared bankruptcy but will continue to run its 140,000-square foot vertical operation in Danville, Virginia, is entirely enclosed and reliant on the intense violet glow of grow lights to produce microgreens.
Different companies have different data needs. What data is essential to AeroFarms isn’t quite the same as for Greenswell Growers located in Goochland County, Virginia. Raising four kinds of lettuce in a 77,000-square-foot automated hydroponic greenhouse, the vagaries of naturally available light, which accounts for 70 percent of Greenswell’s energy use on a sunny day, affect operations. Their tech needs to account for “outside weather impacts,” says president Carl Gupton. “What adjustments do we have to make inside of the greenhouse to offset what's going on outside environmentally, to give that plant optimal conditions? When it's 85 percent humidity outside, the system needs to do X, Y and Z to get the conditions that we want inside.”
AI will help identify diseases, as well as when a plant is thirsty or overly hydrated, when it needs more or less calcium, phosphorous, nitrogen.
Nevertheless, every CEA system has the same core needs — consistent yield of high quality crops to keep up year-round supply to customers. Additionally, “Everybody’s got the same set of problems,” Gupton says. Pests may come into a facility with seeds. A disease called pythium, one of the most common in CEA, can damage plant roots. “Then you have root disease pressures that can also come internally — a change in [growing] substrate can change the way the plant performs,” Gupton says.
AI will help identify diseases, as well as when a plant is thirsty or overly hydrated, when it needs more or less calcium, phosphorous, nitrogen. So, while companies amass their own hyper-specific data sets, Lowman foresees a time within the next decade “when there will be some type of [open-source] database that has the most common types of plant stress identified” that growers will be able to tap into. Such databases will “create a community and move the science forward,” says Lowman.
In fact, IALR is working on assembling images for just such a database now. On so-called “smart tables” inside an Institute lab, a team is growing greens and subjects them to various stressors. Then, they’re administering treatments while taking images of every plant every 15 minutes, says Lowman. Some experiments generate 80,000 images; the challenge lies in analyzing and annotating the vast trove of them, marking each one to reflect outcome—for example increasing the phosphate delivery and the plant’s response to it. Eventually, they’ll be fed into AI systems to help them learn.
For all the enthusiasm surrounding this technology, it’s not without downsides. Training just one AI system can emit over 250,000 pounds of carbon dioxide, according to MIT Technology Review. AI could also be used “to enhance environmental benefit for CEA and optimize [its] energy consumption,” says Rozita Dara, a computer science professor at the University of Guelph in Canada, specializing in AI and data governance, “but we first need to collect data to measure [it].”
The chef-farmers can choose from 45 types of herb and leafy-greens seeds.
Courtesy Babylon Micro-Farms
Any system connected to the Internet of Things is also vulnerable to hacking; if CEA grows to the point where “there are many of these similar farms, and you're depending on feeding a population based on those, it would be quite scary,” Dara says. And there are privacy concerns, too, in systems where imaging is happening constantly. It’s partly for this reason, says Babylon’s Ratte, that the company’s in-farm cameras all “face down into the trays, so the only thing [visible] is pictures of plants.”
Tweaks to improve AI for CEA are happening all the time. Greenswell made its first harvest in 2022 and now has annual data points they can use to start making more intelligent choices about how to feed, water, and supply light to plants, says Gupton. Ratte says he’s confident Babylon’s system can already “get our customers reliable harvests. But in terms of how far we have to go, it's a different problem,” he says. For example, if AI could detect whether the farm is mostly empty—meaning the farm’s user hasn’t planted a new crop of greens—it can alert Babylon to check “what's going on with engagement with this user?” Ratte says. “Do they need more training? Did the main person responsible for the farm quit?”
Lowman says more automation is coming, offering greater ability for systems to identify problems and mitigate them on the spot. “We still have to develop datasets that are specific, so you can have a very clear control plan, [because] artificial intelligence is only as smart as what we tell it, and in plant science, there's so much variation,” he says. He believes AI’s next level will be “looking at those first early days of plant growth: when the seed germinates, how fast it germinates, what it looks like when it germinates.” Imaging all that and pairing it with AI, “can be a really powerful tool, for sure.”
Researchers from the University of Cambridge have laid the foundations for growing synthetic embryos that could develop a beating heart, gut and brain.
The organoid revolution
In 1981, scientists discovered how to keep stem cells alive. This was a significant breakthrough, as stem cells have notoriously rigorous demands. Nevertheless, stem cells remained a relatively niche research area, mainly because scientists didn’t know how to convince the cells to turn into other cells.
Then, in 1987, scientists embedded isolated stem cells in a gelatinous protein mixture called Matrigel, which simulated the three-dimensional environment of animal tissue. The cells thrived, but they also did something remarkable: they created breast tissue capable of producing milk proteins. This was the first organoid — a clump of cells that behave and function like a real organ. The organoid revolution had begun, and it all started with a boob in Jello.
For the next 20 years, it was rare to find a scientist who identified as an “organoid researcher,” but there were many “stem cell researchers” who wanted to figure out how to turn stem cells into other cells. Eventually, they discovered the signals (called growth factors) that stem cells require to differentiate into other types of cells.
For a human embryo (and its organs) to develop successfully, there needs to be a “dialogue” between these three types of stem cells.
By the end of the 2000s, researchers began combining stem cells, Matrigel, and the newly characterized growth factors to create dozens of organoids, from liver organoids capable of producing the bile salts necessary for digesting fat to brain organoids with components that resemble eyes, the spinal cord, and arguably, the beginnings of sentience.
Synthetic embryos
Organoids possess an intrinsic flaw: they are organ-like. They share some characteristics with real organs, making them powerful tools for research. However, no one has found a way to create an organoid with all the characteristics and functions of a real organ. But Magdalena Żernicka-Goetz, a developmental biologist, might have set the foundation for that discovery.
Żernicka-Goetz hypothesized that organoids fail to develop into fully functional organs because organs develop as a collective. Organoid research often uses embryonic stem cells, which are the cells from which the developing organism is created. However, there are two other types of stem cells in an early embryo: stem cells that become the placenta and those that become the yolk sac (where the embryo grows and gets its nutrients in early development). For a human embryo (and its organs) to develop successfully, there needs to be a “dialogue” between these three types of stem cells. In other words, Żernicka-Goetz suspected the best way to grow a functional organoid was to produce a synthetic embryoid.
As described in the aforementioned Nature paper, Żernicka-Goetz and her team mimicked the embryonic environment by mixing these three types of stem cells from mice. Amazingly, the stem cells self-organized into structures and progressed through the successive developmental stages until they had beating hearts and the foundations of the brain.
“Our mouse embryo model not only develops a brain, but also a beating heart [and] all the components that go on to make up the body,” said Żernicka-Goetz. “It’s just unbelievable that we’ve got this far. This has been the dream of our community for years and major focus of our work for a decade and finally we’ve done it.”
If the methods developed by Żernicka-Goetz’s team are successful with human stem cells, scientists someday could use them to guide the development of synthetic organs for patients awaiting transplants. It also opens the door to studying how embryos develop during pregnancy.
This article originally appeared on Big Think, home of the brightest minds and biggest ideas of all time.
