Recently, a book of recipes called Hyperfoods was released, comprised of recipes generated through the assistance of AI and machine learning algorithms. The recipes in the book are based on foods with anti-cancer properties.
Artificial intelligence is seeing increasing use in the creation of food recipes. For example, companies like Analytical Flavor Systems have been using AI to analyze the flavor and textures of drinks and attempt to design drinks catering to specific locales. Meanwhile, Plant Jammer is an app that leverages artificial intelligence to suggest recipes based on the ingredients you have in your house.
As reported by the Imperial College London news, a researcher at Imperial College London and a chef have teamed up together to employ artificial intelligence to identify foods with anti-cancer properties and then compile these foods into a recipe collection. Dr. Kirill Veselkov is a researcher from Imperial College London’s Department of Surgery and Cancer. Dr. Veselkov and his team of researchers analyzed the molecular data of over 8000 food items. A large number of the molecules analyzed by the AI were flavonoids, responsible for giving vegetables and fruits their color. The research made use of the distributed computing application DreamLab, enabling members of the public to participate in the research and help identify around 110 anti-cancer molecules.
According to Veselkov, conditions like cancer, heart disorder and neurological diseases are tied to poor diet. Some studies suggest that poor diets could contribute to around a fifth of all deaths around the globe every year. Veskelov states that around half of cancer cases could potentially be averted by better lifestyle choices and better diets.
Veselkov worked alongside chef Jozef Youssef to create a recipe book made out of food anti-cancer foods. Youssef believes that while we are still a long way from personalized diets, the research conducted through DreamLab is still a critical step in advancing our understanding of dietary health and assisting people in changing their diet to something healthier. Youssef explained that the recipes in the book were designed to instruct people in methods of using ingredients to create meals that could help avert cancer and other forms of disease.
Michael Bronstein, another Imperial College London researcher, worked alongside Veseklov to conduct the research. Bronstein stated that the Hyperfoods project is the first known attempt to use neural networks to examine the effects of food molecule’s on people’s health. As Bronstein was quoted by ICL news:
“By modelling the ‘network effects’ of the interactions between food-based molecules and biomolecules in our body, we can identify which foods contain ingredients that might work in a similar way to medical drugs and have the potential to prevent or beat diseases. Our ambition is to provide a quantum leap in how our food is ‘prescribed’, designed and prepared – and this is a great first step.”
Although the researchers are excited by the results of their studies and have released the cookbook to the public, they also caution that the book should not be used in place of medical advice from a medical professional. The researchers were careful to state that the relationship between health and food molecules still needs more research. There is some evidence that the right diet and exercise can prevent certain types of cancer. However, effect sizes are often modest and it’s unclear if other food types can elicit similar effects.
The DreamLab app will continue supporting the research of Imperial College London, helping researchers there investigate the possibilities for combinations of food and drugs to treat cases of COVID-19, and any findings from the research project will be made available to the medical community to undergo clinical trials if promising.
AI Used To Identify Gene Activation Sequences and Find Disease-Causing Genes
Artificial intelligence is playing a larger role in the science of genomics every day. Recently, a team of researchers from UC San Diego utilized AI to discover a DNA code that could pave the way for controlling gene activation. In addition, researchers from Australia’s national science organization, CSIRO, employed AI algorithms to analyze over one trillion genetic data points, advancing our understanding of the human genome and through localization of specific disease-causing genes.
The human genome, and all DNA, comprises four different chemical bases: adenine, guanine, thymine, and cytosine, abbreviated as A, G, T, and C respectively. These four bases are joined together in various combinations that code for different genes. Around one-quarter of all human genes are coded by genetic sequences that are roughly TATAAA, with slight variations. These TATAAA derivatives comprise the “TATA Box”, non-coding DNA sequences that play a role in the initialization of transcription for genes comprised of TATA.. It’s unknown how the other approximately 75% of the human genome is activated, however, thanks to the overwhelming number of possible base sequence combinations.
As reported by ScienceDaily, researchers from UCSD have managed to identify a DNA activation code that is employed as often as the TATA box activations, thanks to their use of artificial intelligence. The researchers refer to the DNA activation code as the “downstream core promoter region” (DPR). According to the senior author of the paper detailing the findings, UCSD Biological Sciences professor James Kagonaga, the discovery of the DPR reveals how somewhere between one quarter to one-third of our genes are activated.
Kadonaga initially discovered a gene activation sequence corresponding to portions of DPR when working with fruit flies in 1996. Since that time, Kadonaga and colleagues have been working on determining which DNA sequences were correlated with DPR activity. The research team began by creating half a million different DNA sequences and determining which sequences displayed DPR activity. Around 200,000 DNA sequences were used to train an AI model that could predict whether or not DPR activity would be witnessed within chunks of human DNA. The model was reportedly highly accurate. Kadonaga described the model’s performance as “absurdly good” and its predictive power “incredible”. The process used to create the model proved so reliable that the researchers ended up creating a similar AI focused on discovering new TATA box occurrences.
In the future, artificial intelligence could be leveraged to analyze DNA sequence patterns and give researchers more insight into how gene activation happens in human cells. Kadonaga believes that, much like how AI was able to help his team of researchers identify the DPR, AI will also assist other scientists in discovering important DNA sequences and structures.
In another use of AI to explore the human genome, as MedicalExpress reports, researchers from Australia’s CSIRO national science agency have used an AI platformed called VariantSpark in order to analyze over 1 trillion points of genomic data. It’s hoped that the AI-based research will help scientists determine the location of certain disease-related genes.
Traditional methods of analyzing genetic traits can take years to complete, but as CSIRO Bioinformatics leader Dr. Denis Bauser explained, AI has the potential to dramatically accelerate this process. VarianSpark is an AI platform that can analyze traits such as susceptibility to certain diseases and determine which genes may influence them. Bauer and other researchers made use of VariantSpark to analyze a synthetic dataset of around 100,000 individuals in just 15 hours. VariantSpark analyzed over ten million variants of one trillion genomic data points, a task that would take even the fastest competitors using traditional methods thousands of years to complete.
As Dr. David Hansin, CEO of CSIRO Australian E-Health Research Center explained via MedicalExpress:
“Despite recent technology breakthroughs with whole-genome sequencing studies, the molecular and genetic origins of complex diseases are still poorly understood which makes prediction, application of appropriate preventive measures and personalized treatment difficult.”
Bauer believes that VariantSpark can be scaled up to population-level datasets and help determine the role genes play in the development cardiovascular disease and neuron diseases. Such work could lead to early intervention, personalized treatments, and better health outcomes generally.
Research Shows How AI Can Help Reduce Opioid Use After Surgery
Research coming out of the University of Pennsylvania School of Medicine last month demonstrated how artificial intelligence (AI) can be utilized to fight against opioid abuse. It focused on a chatbot which sent reminders to patients who underwent surgery to fix major bone fractures.
The research was published in the Journal of Medical Internet Research.
Christopher Anthony, MD, is the study’s lead author and the associate director of Hip Preservation at Penn Medicine. He is also an assistant professor of Orthopaedic Surgery.
“We showed that opioid medication utilization could be decreased by more than a third in an at-risk patient population by delivering psychotherapy via a chatbot,” he said. “While it must be tested with future investigations, we believe our findings are likely transferable to other patient populations.”
Opioid Use After Surgery
Opioids are an effective treatment for pain following a severe injury, such as a broken arm or leg, but the large prescription of the drugs can lead to addiction and dependence for many users. This is what has caused the major opioid epidemic throughout the United States.
The team of researchers believe that a patient-centered approach with the use of the AI chatbot can help reduce the number of opioids taken after such surgerys, which can be a tool used against the epidemic.
Those researchers also included Edward Octavio Rojas, MD, who is a resident in Orthopaedic Surgery at the University of Iowa Hospitals & Clinics. The co-authors included: Valerie Keffala, PhD; Natalie Ann Glass, PhD; Benjamin J. Miller, MD; Mathew Hogue, MD; Michael Wiley, MD; Matthew Karam, MD; John Lawrence Marsh, MD, and Apurva Shah, MD.
The research involved 76 patients who visited a Level 1 Trauma Center at the University of Iowa Hospitals & Clinics. They were there to receive treatment for fractures that required surgery, and those patients were separated into two groups. Both groups received the same prescription for opioids to treat pain, but only one of the groups received daily text messages from the automated chatbot.
The group that received text messages could expect two per day for a period of two weeks following their procedure. The automated chatbot relied on artificial intelligence to send the messages, which went out the day after surgery. The text messages were constructed in a way to help patients focus on coping better with the medication.
The text messages, which were created by a pain psychologist specialized in pain and commitment therapy (ACT), did not directly go against the use of the medication, but they attempted to help the patients think of something other than taking a pill.
Six Core Principles
The text messages could be broken down into six “core principles,” : Values, Acceptance, Present Moment Awareness, Self-As-Context, Committed Action, and Diffusion.
One message under the Acceptance principle was: “feelings of pain and feelings about your experience of pain are normal after surgery. Acknowledge and accept these feelings as part of the recovery process. Remember how you feel now is temporary and your healing process will continue. Call to mind pleasant feelings or thoughts you experienced today.”
The results showed that the patients who did not receive the automated messages took, on average, 41 opioid pills following the surgeries, while the group who did receive the messages averaged 26. The 37 percent difference was impressive, and those who received messages also reported less overall pain two weeks after the surgery.
The automated messages were not personalized for each individual, which demonstrates success without over-personalization.
“A realistic goal for this type of work is to decrease opioid utilization to as few tablets as possible, with the ultimate goal to eliminate the need for opioid medication in the setting of fracture care,” Anthony said.
The study received funding by a grant from the Orthopaedic Trauma Association.
Samsung Medison & Intel Collaborate to Improve Fetal Safety
According to the World Health Organization, approximately 295,000 women died during and following pregnancy and childbirth in 2017, even as maternal mortality rates have been decreasing. While every pregnancy and birth is unique, most maternal deaths are preventable. Research from the Perinatal Institute found that tracking fetal growth is essential for good prenatal care and can help prevent stillbirths when physicians are able to recognize growth restrictions.
Samsung Medison and Intel are collaborating on new smart workflow solutions to improve obstetric measurements that contribute to maternal and fetal safety and can help save lives. Using an Intel® Core™ i3 processor, the Intel® Distribution of OpenVINO™ toolkit and OpenCV toolkit, Samsung Medison’s BiometryAssist™ automates and simplifies fetal measurements, while LaborAssist™ automatically estimates the fetal angle of progression (AoP) during labor for a complete understanding of a patient’s birthing progress, without the need for invasive digital vaginal exams.
According to Professor Jayoung Kwon, MD PhD, Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, Yonsei University College of Medicine, Yonsei University Health System in Seoul, Korea: “Samsung Medison’s BiometryAssist is a semi-automated fetal biometry measurement system that automatically locates the region of interest and places a caliper for fetal biometry, demonstrating a success rate of 97% to 99% for each parameter.Such high efficacy enables its use in the current clinical practice with high precision.”
“At Intel, we are focused on creating and enabling world-changing technology that enriches the lives of every person on Earth,” said Claire Celeste Carnes, strategic marketing director, Health and Life Sciences, Intel. “We are working with companies like Samsung Medison to adopt the latest technologies in ways that enhance the patient safety and improve clinical workflows, in this case for the important and time-sensitive care provided during pregnancy and delivery.”
How It Works
BiometryAssist automates and standardizes fetal measurements in approximately 85 milliseconds with a single click, providing over 97% accuracy. This enables doctors to allocate more time to talking with their patients while also standardizing fetal measurements, which have historically proved challenging to provide with accuracy. With BiometryAssist, physicians can quickly verify consistent measurements for high volumes of patients.
“Samsung is working to improve the efficiency of new diagnostic features, as well as healthcare services, and the Intel Distribution of OpenVINO toolkit and OpenCV toolkit have been a great ally in reaching these goals,” said Won-Chul Bang, corporate vice president and head of Product Strategy, Samsung Medison.
During labor, LaborAssist helps physicians estimate fetal AOP and head direction. This enables both the physician and patient to understand the fetal descent and labor process and determine the best method for delivery. There is always risk with delivery and a slowing progress could result in issues for the baby. Obtaining more accurate and real-time progression of labor can help physicians determine the best mode of delivery and potentially help reduce the number of unnecessary cesarean sections.
“LaborAssist provides automatic measurement of the angle of progression as well as information pertaining to fetal head direction and estimated head station. So it is useful for explaining to the patient and her family how the labor is progressing, using ultrasound images which show the change of head station during labor. It is expected to be of great assistance in the assessment of labor progression and decision-making for delivery,” said Professor Min Jeong Oh, MD, PhD, Department of Obstetrics and Gynecology, Korea University Guro Hospital in Seoul, Korea.
BiometryAssist and LaborAssist are already in use in 80 countries, including the United States, Korea, Italy, France, Brazil and Russia. The solutions received Class 2 clearance by the FDA in 2020.
Intel and Samsung Medison will continue to collaborate to advance the state of the art in ultrasounds by accelerating AI and leveraging advanced technology in Samsung Medison’s next-generation ultrasound solutions, including Nerve Tracking, SW Beamforming and AI Module.
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