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Tag: what is ai

  • New AI Listens to Toilet Sounds to Detect Diarrhea

    New AI Listens to Toilet Sounds to Detect Diarrhea

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    Dec. 27, 2022 — Artificial intelligence has achieved another milestone: Discerning the sound of an unhealthy bowel movement. 

    A design for a “Diarrhea Detector” that could alert health officials to disease outbreaks like cholera was recently presented by engineers from the Georgia Tech Research Institute. Someday, the AI could even be used with home smart devices to monitor one’s bowel health. 

    A prototype accurately identified diarrhea 98% of the time in tests, the engineers told a conference of the Acoustical Society of America in Nashville. Even with background noise, it was correct 96% of the time.

    Cholera infects millions of people each year, killing up to 143,000 who become dehydrated from severe diarrhea, according to the World Health Organization. Many deaths could be avoided with an oral rehydration solution if the outbreak is spotted fast enough. Cholera can be lethal within 24 hours after symptoms start. 

    The device could be installed in public toilets where inadequate plumbing raises the risk for a cholera outbreak.

    “Cholera typically has a more watery sound to it — it can sound a lot like urination and it doesn’t have a lot of the flatulence notes in general,” says project co-lead Maia Gatlin, an aerospace engineer and PhD candidate at the Georgia Tech Research Institute. “That someone is having severe diarrhea, and that they are having a lot of it — that can be captured.” 

    The idea grew out of conversations about how COVID-19 can be monitored by analyzing sewage, says project co-lead Alexis Noel, PhD, a biomechanics engineering researcher at the institute. 

    Other researchers have considered video analysis to look for diarrhea. 

    “I was curious if we could detect diarrhea using sound,” Noel says, “as some folks are a little wary about having a camera pointed at their bum in the toilet.”

    First, the researchers gathered 350 publicly available audio samples of bathroom sounds from YouTube and Soundsnap. Some clips had up to 10 hours of diarrhea noises.

    The researchers listened to the samples to establish authenticity. 

    “We didn’t know these people, we didn’t know how they recorded, so we had to listen to a good bit,” Gatlin says. “There were definitely lots of fart sounds where we were like, ‘That’s not a fart, that’s someone blowing into their elbow.’”

    The sounds of defecation, urination, flatulence, and diarrhea were converted into spectrogram images. A computer analyzed those images for about 10 hours using a “convolutional neural network.” The software, using trial and error, teaches itself how to identify the subtle similarities between diarrhea spectrograms and how they differ from other toilet sounds.

    For example, urination has a consistent tone and defecation may have a singular tone. Diarrhea’s sound is more random.

    Once the AI learning process was complete, the researchers loaded the diarrhea-decoding algorithm onto a Raspberry Pi, a computer roughly the size of a credit card that costs less than $50. Georgia Tech student Cade Tyler 3D-printed a case for the motherboard with a microphone connection, a series of lights (green for acquiring a signal, red for diarrhea, and orange for “other”), and the words “Diarrhea Detector” inscribed on the surface. 

    The computer takes a 10-second audio recording, which is converted to a spectrogram and fed to the algorithm. The whole process takes only seconds.

    The next iteration of the device would send a report via Wi-Fi or other wireless communication signal to a database, so public health officials can monitor for disease outbreaks. 

    “We’re not collecting anything identifiable about people,” Gatlin says.

    The researchers have not yet determined how many of these devices would be needed to cover a community, or where the ideal placement would be. 

    The algorithm still needs to be refined using better audio data collected in controlled conditions, from people who have provided informed consent, Gatlin says. Gatlin also hopes to train the AI to work in outdoor latrines, which are common in areas without functioning sewage systems. 

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  • Can AI Drive More Diversity in Drug Development?

    Can AI Drive More Diversity in Drug Development?

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    Nov. 29, 2022 – Artificial intelligence could help improve diversity, equity, and inclusion in clinical trials and drug development by overcoming some traditional human bias in these areas, but we’re not there yet, experts say. The technology could also assist doctors with data insights to make diagnosis and treatment more precise. 

    It starts with quality. Artificial intelligence (AI) relies on large amounts of data to create algorithms – or computer instructions – to develop best practices and predictions. But the instructions are only as good as the data used to create them. And people are the ones creating the data.

    “Underpinning the development of AI technologies are people, and those people have their own biases,” says Naheed Kurji, the chair of the board for the Alliance for Artificial Intelligence in Healthcare. “As a result, the algorithms will have their own biases.”

    Technology that uses speech to diagnose disease is an example. 

    “There are many cases, examples where companies have failed to recognize the differences in speech across different cultures,” says Kurji. When technology is based on speech patterns of a limited demographic, “then when that model is applied in the real world to a different demographic with a different accent, that model fails.”

    “As a result, it’s not representative.”

    Another example is genetic and genomic data. 

    “Give or take, 90-plus percent of genetic and genomic data has originated from people of European descent. It’s not from people from the continent of Africa, Southeast Asia, Asia, or South America,” says Kurji, who is also president and CEO of Cyclica Inc., a data-driven drug discovery company based in Toronto. 

    Therefore, “a lot of research that has been done on that level of data is inherently biased,” he says. 

    To Be Fair 

    Creating data that takes diversity, equity, and inclusion of people and cultures around the world into account is not a hopeless challenge. But it will take time, experts say. Once that is achieved, AI should be closer to being free of human and systemic biases.

    Greater awareness is essential. 

    “The solution to the problem comes from people inherently understanding that the bias exists,” Kurji says, and then only including fair and balanced data that passes a diversity test.

    Choosing More Wisely?

    Another promising avenue for AI is streamlining the drug development process, narrowing down potential drug candidates, and making clinical trials more cost-effective. 

    “If the source data has challenges and limitations, then that the AI is going to just keep propagating those limitations,” agrees Sastry Chilukuri, co-CEO of the data-driven clinical trial company Medidata and founder and president of Acorn AI. “The source data has to get more representative and has to get more equitable for the AI to reflect what’s happening.”

    When it comes to human or systemic bias in drug development, “it would be too much of a simplification to say AI or machine learning can fix it,” says Angeli Moeller, PhD, head of data and integrations generating insights at Roche in Berlin. “But responsible use of AI and machine learning can help us identify biases and find ways to mitigate any negative effects it might cause.”

    Silent Partners?

    At the same time AI aims to streamline drug development, the technology also can help make all doctors better at their jobs, experts say. AI would, for instance, help by spreading knowledge and expertise far and wide, sharing best practices from doctors with a lot of experience in more complex patients. This would help guide those who treat only a few such patients each year. 

    The surgical volumes in New York City or in Delhi could be as high as hundreds of patients a year, Chilukuri says. “But if you go to interiors of the U.S. like Nebraska, the surgeon just doesn’t see that much volume.” 

    AI could help doctors “by providing the kind of tools that allow them to be able to deliver the same top-notch care to all of their populations at lot faster,” he says.

    Boosting Efficiency 

    AI could help target therapy by using data to identify patients at highest risk. The technology also could improve some bottleneck areas in medicine, such as the time it takes to interpret radiology images, Kurji says. 

    There is an AI company “whose entire business model is not to replace your radiologist but to make radiologists better,” he notes. One of company’s aims is “to prevent death or severe ailment from radiology scans that get missed or that get stacked on the pile and just don’t get acted on fast enough for that patient.” 

    Radiologists are so busy, they may have only 30 seconds or less to interpret each scan, says Chilukuri. AI can flag a lesion of potential concern, but it can also compare an image to past scans on the same patient. This view afforded by AI does not just apply to radiology but across data-driven areas of medicine. 

    Advancing Personalized Medicine

    AI could also guide a personal approach to surgery, “because it’s not like humans come in small, medium and large,” Chilukuri says. The technology could help surgeons determine exactly where to operate on an individual patient.

    Moeller agrees that AI holds potential for boosting personalized medicine. 

    “AI can help with diagnosis and risk prediction, which can mean earlier interventions,” says Moeller, who’s also vice chair of the Alliance for Artificial Intelligence in Healthcare board.  “If you look, for instance, at a diabetic patient, what is the likelihood that he or she might develop eye problems from diabetic macular edema?”

    The technology could also help with getting a look at the big picture. 

    “Machine learning can look for patterns in a population that might not be in your medical textbook,” Moeller says. 

    Beyond diagnosis and treatment, AI also could help with recovery by customizing rehabilitation for each patient, Chilukuri predicts. 

    “It’s not like every person is going to rehab the exact same way. So, you have highly individualized AI plans that allow you to actually stay on track and predict where you’re going.”

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  • Your Next Colonoscopy Could Get an Assist From AI

    Your Next Colonoscopy Could Get an Assist From AI

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    Nov. 11,  2022 – Artificial intelligence holds a lot of potential in medicine, helping doctors find skin cancer, flagging potential issues on a chest X-ray, and assisting in many other procedures. Screening for colorectal cancer during a colonoscopy is another prime example.  

    A colonoscopy — recommended for Americans at average cancer risk starting at age 45 — won’t be much different for patients with the addition of artificial intelligence, or AI. But behind the scenes, AI could be making detection of any precancerous polyps or cancerous lesions more likely. 

    “AI-enhanced colonoscopy effectively turbocharges the physician’s ability to find even the most subtle precancerous polyps,” says Tyler M. Berzin, MD, a gastroenterologist at the Center for Advanced Endoscopy at Beth Israel Deaconess Medical Center in Boston. 

    The technology is designed to flag anything the computer “sees” as suspicious, but it does not replace the training and expertise of a gastroenterologist. Even with AI, doctors remain at the patient’s side and perform the procedure. 

    The doctor remains in full control, says Prateek Sharma, MD, a gastroenterologist and professor of medicine at University of Kansas School of Medicine in Kansas City, KS. “AI is assisting and alerting them to colon polyps — the precancerous lesions in the colon — so that the doctor can remove them.”

    Controversy Continues

    Size, height, and numbers matter with polyps. Doctors generally remove or biopsy lesions 10 millimeters and larger. 

    But there remains less consensus about the best approach to smaller polyps.

    “The clinical relevance of detecting and removing small (5 to 9 mm) or diminutive (less than 5 mm) adenomas is a subject of ongoing debate,” Berzin and co-authors wrote in a leading gastroenterology journal in May 2020. 

    One of the potential disadvantages to using AI polyp tools, for example, is “the risk of removing a higher number of diminutive or hyperplastic polyps, which increases cost and risk, without any benefit to the patient,” Berzin says. 

    “Trained gastroenterologists are experts at identifying and removing precancerous colon polyps,” Berzin says. “But a gastroenterologist working with an AI polyp detection tool has a big advantage because AI computer vision tools can simultaneously analyze every pixel of the endoscopy monitor and can do so without being distracted or fatigued for even a millisecond.”

    The benefit for patients is “another pair of eyes looking for polyps and helping the doctor,” says Sharma, who is also chair of the Artificial Intelligence Task Force at the American Society for Gastrointestinal Endoscopy.

    How It Works

    AI is based on computer instructions called algorithms that learn the difference between worrisome and benign colonoscopy images and videos. AI gets better over item in a process called machine learning. When an AI system spots a potential area of concern, the technology calls attention to it by framing it within a box on the screen. Some systems also sound an audible alarm. 

    “We are seeing more interest in using these algorithms since they will standardize endoscopists’ polyp detection and, therefore, reduce the number of colon cancers missed,” says Sravanthi Parasa, MD, a gastroenterologist at the Swedish Health Services in Seattle. 

    “These products are slowly gaining traction. During colonoscopy scheduling, patients should ask if their endoscopist has access to augmented diagnostic tools,” she says. 

    The technology is not accurate 100% of the time – there can be false positives where the system flags a bubble in the colon, for example, as potentially dangerous. That’s just one reason that doctors still have the final say on whether a polyp is suspicious or not. 

    AI or no AI, “colonoscopy has long been our most effective tool for preventing colon cancer, detecting precancerous polyps earlier than any other screening method,” says Berzin, who is also an associate professor of medicine at Harvard Medical School.

    AI Can Be Costly

    AI and machine learning already play a role in “smart” technologies (smartphones, smartwatches, and smart speakers), self-driving cars, and speech recognition software. But the use of AI in medicine is comparatively new. And like a lot of novel technologies, it’s also expensive. “The AI equipment needs to be purchased and is expensive,” Sharma says.  

    “The cost of the algorithms currently can be prohibitive for some centers in the current health care landscape,” Parasa agrees. “The cost is likely to come down as more algorithms enter the GI market, as it is with other software solutions.”

    Colorectal Cancer Is Common

    Not counting some kinds of skin cancer, colorectal cancer is the fourth most common cancer in Americans. It is also the fourth leading cause of cancer-related deaths in the United States, the CDC reports. More than 150,000 Americans will be diagnosed with colorectal cancer and more than 50,000 will die in 2022, according to National Cancer Institute figures. 

    Future Insights

    More research is needed to examine how humans and this technology interact, Berzin says. “The most interesting research in this space will not be about comparing ‘physician versus AI,’ but will be focused on understanding the nuances of ‘physician plus AI.’”

    In the U.S., there are at least three FDA-approved AI algorithms for polyp detection, and more are being developed, Parasa says. 

    “In addition, other applications which are currently available on the European market might be available in the U.S. market in the near future, including polyp characterization.”

    “As the field matures, we will likely see more AI augmentation tools that will assist us in detecting and diagnosing GI conditions in real time,” she adds. “A suite of algorithms like this will definitely improve patient care and outcomes.”

    Even though AI is somewhat of a work in progress in medicine, Berzin expects the combination of doctor and AI technology “will translate into the highest possible protection from colon cancer in the long term.”

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