Each year, nearly 356,000 cardiac arrests occur outside of a U.S. hospital. Cardiac arrest occurs when a persons cardiac function is suddenly lost, whether they have been diagnosed with heart illness or it might appear out of nowhere, or due to other symptoms. If the right measures arent performed immediately, cardiac arrest can be deadly.

Cardiac arrest occurs due to cardiomyopathy, a condition in which the heart muscle becomes dilated or thickens, leading to abnormal contractions of the heart. Another form of cardiac arrest can happen when plaque blockages constrict and thicken the coronary arteries, restricting blood flow to the heart. If untreated, this might result in heart failure (HF) or arrhythmias, both of which can result in cardiac arrest. Signs of sudden cardiac arrest may vary from chest discomfort to sudden collapse.

The available technology for the prevention of cardiac arrest includes an electrocardiogram (ECG), which detects the hearts electrical activity using sensors (electrodes) placed on the patients chest and occasionally, limbs. An ECG can indicate cardiac rhythm problems or aberrant electrical patterns such as a prolonged QT interval that elevate the risk of sudden death,. Some blood and imaging tests are also conducted to check the level of chemicals in the body; for example, potassium, magnesium, hormones, and other substances that might alter the hearts capacity to function may be measured in a blood sample. Other blood tests can reveal cardiac damage and heart attacks that have occurred recently.

A physician uses a chest X-ray to examine the size and form of the heart and blood vessels. Utilizing coronary catheterization, a technique in which a liquid dye is injected into the hearts arteries employing a long, thin tube (catheter) that is pushed via an artery, generally in the arm, to the hearts arteries. The arteries become apparent on X-ray and videotape as the dye fills them, exposing regions of obstruction. In addition, a nuclear scan, which is frequently combined with a stress test, aids in the detection of blood flow issues in the heart.

A piece of computerized medical equipment is called an automated external defibrillator (AED). It uses adhesive defibrillator pads that are put to the chest to let an electrical current flow through to the heart, resetting the hearts natural electrical activity. The heart muscle to contract and circulate blood to the body requires a normal, regular, or ordered electrical rhythm. The most effective strategy to enhance sudden cardiac death (SCA) survival rates is to have an automated external defibrillator in every house and public location. AEDs are small, portable devices that give an electric shock to the heart, and they have been shown to save many lives when utilized promptly.

The rising frequency of different coronary heart disorders such as cardiomyopathy, which is leading to cardiac arrest fatalities, attractive reimbursement schemes, and an increase in the geriatric population, are the primary drivers driving the markets growth. According to the American Heart Association, nearly half of adults in the United States have a cardiovascular condition, resulting in increased demand for highly efficient, immediate treatment and more technologically advanced cardiovascular devices.

The processing of data is a critical stage in the development of prognostic models. Nonlinear prediction models, a high number of patients, and various predictors with intricate connections are all obstacles in data processing. These difficulties are tough to overcome in typical hypothesis-driven statistical analysis. Many patients who would benefit from preventative care are missed by current methods for predicting cardiovascular risk, while others receive unnecessary treatment. As a result, applying AI technologies such as machine learning (ML) and deep learning techniques to tackle the issues is becoming increasingly necessary.

AI and ML approaches can increase cardiovascular risk prediction accuracy and reduce unnecessary use of medicine. By updating existing diagnostic and therapeutic support systems, machine learning approaches may improve heart failure outcomes and management while saving money. ML techniques can improve accuracy by utilizing complicated linkages between risk factors and be used to predict sudden cardiac death.

A variety of AI algorithms have been created to forecast the risk of abnormal cardiac diseases such as heart failure (HF) and atrial fibrillation. Machine learning algorithms have also been used to diagnose and forecast the likelihood of readmission and death following HF using just risk variables. While some recent study suggests utilizing AI to predict heart failure using both a collection of risk variables and 12-lead ECG data, there is seldom a comparison time frame, and if there is, it is for a relatively limited period, such as present to five years. Recent research has used ECG waveform data to train AI networks to detect cardiac problems, including ejection fraction, left ventricular systolic dysfunction, and mitral regurgitations.

The ubiquitous use of smartphones and smart speakers give a once-in-a-lifetime opportunity to discover the audio biomarker and connect cardiac arrest victims to emergency medical services or anyone who can administer cardiopulmonary resuscitation (CPR).

Another well-known device for cardiac patients is a pacemaker. It is placed surgically into the abdominal or chest cavities, and is intended for patients who have an arrhythmia or an erratic heartbeat, which indicates that the heart is beating too quickly, too slowly, or unevenly. The data generated by a pacemaker is significant and could be used in many ways to learn and predict behavior.

One great invention includes a fat radiomic profile (FRP) fingerprint that captures the amount of risk created using machine learning. In the SCOT-HEART experiment, the performance of this perivascular fingerprint was examined in 1,575 participants, revealing that the FRP had an incredible value in predicting heart attacksmuch above anything now available in clinical practice. Professor Charalambos Antoniades of the University of Oxfords Department of Cardiovascular Medicine and BHF Senior Clinical Fellow said, Weve developed a fingerprint to discover poor traits surrounding peoples arteries by leveraging the power of AI. This has enormous promise for detecting early indicators of illness and taking the necessary precautions before a heart attack occurs, perhaps saving lives.

The use of machine learning has its own critics as it is considered to be one of the most expensive techniques used for cardiac-related issues. Moreover, it requires a large amount of data for accurate results. Biases in the training data, model overfitting, insufficient statistical correction for several testing, and limited transparency around the procedures by which DL algorithms reach their output (black box systems) are just a few of the AI pitfalls that can have severe consequences for patients and necessitate careful consideration by researchers, clinicians and regulatory bodies.

Physicians have a significant opportunity and responsibility to actively watch the continual development of AI approaches and use and apply them according to their needs to discover essential supporting tools for their clinical practices. AIs arrival in the cardiovascular profession brings a plethora of new opportunities for providing innovative, tailored treatment. The way we practice cardiology, particularly in cardiac imaging, is changing, and physicians must be prepared. mHealth and telemedicine are forming new links between patients and doctors, transforming healthcare from a passive to a ubiquitous activity. Physicians should not be terrified of AIs incorporation into cardiology; instead, they should welcome it, because their specialist knowledge is always necessary.

Opportunities for intelligent computer systems span widely, including extensive use in medical science. Artificial intelligence enhances cognition analysis of complex health issues and improves the diagnoses. However, there are still some challenges in terms of data quality, regulations, market penetration

A recent paper released by Duke University cites the promise of AI, but urges policy changes in order to bring AI-enabled clinical decision software to fruition.

Expanded designs that enable clinicians to leverage data in making healthcare decisions, but privacy challenges remain.

The race to apply AI to medical treatment is rapidly accelerating in China and Japan.

See the article here:
Potential for Artificial Intelligence in the Prevention and Detection of Cardiac Arrest - MedTech Intelligence

University of Florida researchers are looking to create an Artificial Intelligence Connoisseur, a model that tells researchers which chemical compounds (the volatiles, sugars, acids, and other chemical compounds) produce the best flavors in fruits and vegetables.

In a new study, UF/IFAS plant breeder and geneticist Marcio Resende and other scientists used artificial intelligence to gather smell and taste data on tomatoes and blueberries. Resende led the new research that shows ways to get data from volatiles in blueberries and tomatoes into a statistical model. The research findings are now limited to those two fruits but will later be expanded to other crops UF/IFAS researchers develop.

To conduct their new study, UF/IFAS researchers used tomato and blueberry breeding program data from the past decade. They gave a diverse set of tomato and blueberry varieties to consumer panels at the UF Sensory Lab in Gainesville. The scientists then collected ratings on flavor attributes such as liking, sweetness, sourness, flavor intensity, and umami.

UF/IFAS researchers tested the range of scores that tell them how much a consumer likes a flavor. As it turns out, volatiles explained up to 56% of the like scores, which reinforces evidence that volatiles are important in determining how much consumers like the fruit. Volatiles are also important in quantifying and estimating the importance of fruit flavor, Resende says.

For more, continue reading at blogs.ifas.ufl.edu.

The news source of University of Florida's Institute of Food and Agricultural Sciences (UF/IFAS). See all author stories here.

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Artificial Intelligence Tasked To Find Better Tasting Fruits and Veggies - Growing Produce

Artificial Intelligence:

Artificial Intelligence

The SAS Virtual Health Artificial Intelligence (AI) Summit on Cancer Research1 was held this year to share best practices, ongoing challenges, and future opportunities for advancing cancer treatment through analytics. Innovations in applying computer vision to medical images and using machine learning (ML) to build predictive models may help clinicians assess therapeutic results more efficiently, thereby enhancing personalized approaches to cancer treatment.

AI is the application of digital devices and computers to enhance human intelligence.2 In this article, we focus on the use of AI to develop ML and deep learning (DL) models. Whereas ML is the subfield of AI using mathematical and statistical approaches to derive models from data, DL is a specific class of ML that leverages complex networks in its learning process (Figure 1).3

Relationship Among AI, ML, and DL

1. Applying Response Evaluation Criteria for Solid Tumors (RECIST 1.1) criteria to solid tumors involves measuring the largest diameter of a tumor, but tumor volume and morphology give a more comprehensive assessment of treatment response,4,5 so there is an opportunity to improve RECIST 1.1 with AI, ML, and DL.6 AI can determine volumetric changes in the three-dimensional morphology of cancers that are not simple spherical or elliptical structures, while eliminating subjectivity and observer variability7-10 and reducing time assessing tumor response. The combination of clinical data features, such as AI-assisted interpretation of test reports and longitudinal patient level data, can train DL and ML models to improve the diagnostic accuracy of radiographic studies.11,12

AI may also be used to give objective histopathological results, as in a recent study of patients with pancreatic cancer. Digitized, segmented images of tumors were used to segment residual tumor burden after chemotherapy to aid follow-up therapy.13

2. AI and ML enable personalized medicine through disease detection and risk assessment models. For early detection of cancer, AI was used to examine biochemical parameters such as serum albumin and platelets. Data presented showed that low albumin levels were associated with higher cancer risk than high albumin.14 Elevated platelet count may also be associated with high risk for lung or colorectal cancer.15

In another model, ML was used to develop a risk score for severe and febrile neutropenia in patients receiving chemotherapy. This would aid clinicians in deciding whether to prescribe filgrastim and health care systems in constructing clinical pathways to guide use of such drugs. Point-of-care electronic medical record data were used to train and validate a variety of ML models (Figure 2). Of six ML models studied, the preferred one requires only 20 clinical features; the model offers interpretability and a low data extraction burden, addressing two common barriers to adoption.16

Training the Severe and Febrile Neutropenia Model. AUC, area under the curve; Epic/Clarity, Epic Systems database; Medimpute, software solution for approximating missing data (Machine Learning 2021;110(1):185-248)

3. Health disparities arising from various demographic factors have been well-documented, the 111 percent higher risk of Black men dying from prostate cancer compared with White men being just one example.17 Contributing factors include socioeconomic status, access to health care and treatment, culture, genetic variants, and molecular differences in tumors.17,18 AI can integrate the impact of social determinants on cancer rates and treatment outcomes.19 These data can help address gaps in health care through policy, preventative care programs, and target clinical intervention.

Often the introduction of a new technological intervention, e.g., mammography, may inadvertently lead to disparities. Currently, Black women are 40 percent more likely to die from breast cancer than White women.20 Care must be taken so that AI is trained on unbiased datasets to correct for possible unequal access to diagnostics, so that the resulting clinical decision technology is generalizable to the entire population.21 Thus, AI requires strategies from the start to address equitable use, so that the health of the many will be improved, instead of the few.

4. AI, ML, and DL have implications on patient data privacy and equitable care delivery, concerns that must be addressed before broader adoption. Privacy is particularly relevant when considering the growing role of companies outside of the traditional health ecosystem.22 The UK has already experienced breaches of patient privacy due to lax procedures by a technology company.22 Such incidents demonstrate the urgent need for regulation and security standards, but these generally advance more slowly than the development of AI tools. Currently the European Medicines Agency is drafting guidance,23 while the FDA is creating an action plan for AI and ML.24,25 ML researchers are also working to solve these problems, such as using federated learninga method by which models are trained without sharing private datawhich could offer a privacy-preserving solution.26

AI, ML, and DL applied to high-quality datasets, which ideally would be large and from diverse groups of individuals, will be increasingly used to interpret medical imaging, automate analyses, build predictive models, transform written text into coded data, and improve population health.

Questions remain about integrating AI into health care systems. As new AI tools are developed, what are the best ways to prospectively validate models, deploy AI in the clinic, and modify RECIST criteria in clinical trials? Who is responsible for the governance of analytic models used in clinical practice, and will reimbursement policies be barriers to adopting new solutions that leverage AI to improve healthcare outcomes?

PETER YU, MD, is with the Hartford HealthCare Cancer Institute, Hartford Hospital, Hartford, CT. GEERT KAZEMIER, MD, PHD, is with the Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Department of Surgery, Cancer Center Amsterdam. IVAN BRANDSLUND, MD, DMSC, is at the University of Southern Denmark. ASBA TASNEEM, PHD, works at Project Data Sphere, Morrisville, N.C. HOLLY WIBERG, BS, is at Operations Research Center of MIT. NINA J. WESDORP, MD, is with the Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Department of Surgery, Cancer Center Amsterdam. MARK LAMBRECHT, PHD, is at the SAS Institute, Inc. HAI HU, PHD, works at the Chan Soon-Shioing Institute of Molecular Medicine at Windber. ROBERT A. WINN, MD, is at the VCU Massey Cancer Center, Richmond, Va. STEVE KEARNEY, PHARMD, is at the SAS Institute, Inc. JOOST HUISKENS, MD, PHD, is at the SAS Institute, Inc. ELDER GRANGER, MD, is at at The 5 Ps LLC in Centennial, CO.

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Artificial Intelligence Comes of Age in the Fight Against... : Oncology Times - LWW Journals

More than 20 years ago, Baheya Wahbi a member of a prominent Egyptian family was diagnosed with breast cancer. She found that she couldnt get the care she needed in Egypt, necessitating travel to the United States and Europe for treatment. While abroad, she worried about the many other Egyptian women suffering from breast cancer but without the resources to enable them to travel.

This concern stayed with her and so, before her death, she asked her sons & daughters to buy and bring to Egypt the state-of-the-art radiation therapy machine that she was able to access when abroad. They agreed, but did even more. Instead of bringing just that one machine, they decided to honor their mother and her vision to help other women in Egypt by establishing an entire hospital in her name.

Located at the site of her house, the Baheya Foundation Hospital today is dedicated to the prevention, early diagnosis and treatment of women with breast cancer. To ensure all women in Egypt have access to world-class care, the hospital provides its service for free.

The foundation and hospital were established in 2015 and currently provide more than 7,000 early detection of breast cancer and 1500 chemotherapy sessions per month, and 3,000 radiation therapy session treatments a month in addition to 350 surgeries per month. These numbers will grow significantly when a new branch of the hospital opens in 2023 in the Sheikh Zayed west of Cairo.

In addition to providing direct care to women, Baheya also conducts research to continually enhance the services it offers, said Dr. Mohamed Emara, CEO Baheya Hospital. As Baheya is center of excellence providing most advance technology with high reputation companies to keep pace with global development as unique hospital in Egypt & MENA region.

Most recently, this includes a collaborative program with GE Healthcare that builds on GE Healthcares experience in womens health, artificial intelligence (AI) and contrast-enhanced spectral mammography (CESM), and Baheyas clinical expertise in breast imaging.

As part of a newly announced agreement, the two partners will work to develop and validate the use of AI to assess and predict the response of neoadjuvant chemotherapy in locally advanced breast cancer.

We are enthusiastic about how this research collaboration has the potential to transform future outcomes for breast cancer patients, said Agnes Berzsenyi, President & CEO of Womens Health at GE Healthcare. By combining our expertise with that of the Baheya Foundation and layering it with AI, we are one step closer to delivering on our mission of increasing early detection and helping to save more lives.

Prof. Dr. Mohamed Gomaa, radio-diagnosis consultant & head of radiology department in Baheya center for early detection and treatment of breast cancer added: As part of our commitment to ongoing research in this field, our collaboration with GE Healthcare seeks to deploy the power of AI combined with clinical experience to expand our knowledge of the treatment pathways for neoadjuvant therapy to improve outcomes for patients at Baheya and beyond.

Various diagnostic imaging technologies are currently used to predict and assess a patients response to neoadjuvant therapy, and AI has the potential to give clinicians an earlier indication that a patient is not responding to a given treatment regimen, thereby allowing a timely change of treatment. The result would be a reduction in unnecessary toxicity to the patient and a lower treatment cost for the hospital.

The project will explore the use of AI in CESM to assess response to neoadjuvant chemotherapy in advanced breast cancer as accurate indicator and predictor of response mimic postoperative pathological results and can precisely predict residual tumors percentage .

In patients with locally advanced breast cancer, neoadjuvant therapy is used before surgery to help reduce the size of a cancerous tumor. Initially targeted to patients whose cancer are not eligible for surgery, the uses of neoadjuvant therapy have extended to include patients whose cancers are operable but would be more feasible to surgery after this therapy.

As Baheya Foundation grows its operations and expands the number of patients it serves, this collaboration with GE Healthcare likewise seeks to expand clinical treatment options and further improve patient outcomes and experiences.

Distributed by APO Group on behalf of GE Healthcare.

Follow GE Healthcareon:Facebook: https://bit.ly/3h41JmMLinkedIn: https://bit.ly/3uwAg4qTwitter: https://bit.ly/34BWruUInstagram:https://bit.ly/3B4QmmXInsights:https://bit.ly/3hl6zLdfor the latest news, orVisit our websitewww.GEHealthcare.comfor more information.

Follow Baheya Foundation/Hospitalon:Facebook: https://bit.ly/3BaHZGrLinkedIn: https://bit.ly/3sfKDXeTwitter: https://bit.ly/3gtfhqYInstagram: https://bit.ly/3Lj1dybWebsite: http://www.Baheya.org

About GE Healthcare: GE Healthcare is the $17.7 billion healthcare business of GE (NYSE: GE). As a leading global medical technology, pharmaceutical diagnostics and digital solutions innovator, GE Healthcare enables clinicians to make faster, more informed decisions through intelligent devices, data analytics, applications and services, supported by its Edison intelligence platform. With over 100 years of healthcare industry experience and around 47,000 employees globally, the company operates at the center of an ecosystem working toward precision health, digitizing healthcare, helping drive productivity and improve outcomes for patients, providers, health systems and researchers around the world.

About Baheya Foundation/Hospital:Baheya foundation as leading NGO of women health was established in 2015 and it consists of 6 floors and devices, we received more than 180.000 women till now for early detection & all stages of treatment as center of excellence.

Baheya center proudly announces that it has accredited by (JCI), the highest accreditation for healthcare quality, this accreditation proves the keenness of all medical, technical and administrative staff to fully comply with international healthcare standards and recommendations.

Our vision: Baheya Foundation is the premier destination for womens health and safety.

Our message: We are an association that provides innovative programs specialized in awareness, early detection, and treatment of breast cancer, and psychological support for women using the latest technologies and qualified medical and administrative staff

Baheya Hospital at Zayed city/ Giza as largest breast cancer hospital will be operated in Q1 2023 with 35000 square meters in 7 floors with all services of oncology hospital

This Press Release has been issued by APO. The content is not monitored by the editorial team of African Business and not of the content has been checked or validated by our editorial teams, proof readers or fact checkers. The issuer is solely responsible for the content of this announcement.

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Leveraging Artificial Intelligence (AI) in Egypt: Collaboration for Improved Breast Cancer Outcomes - African Business Magazine

Photo: Adam Berry (Getty Images)

One of the core tenants running throughout The Age of AI is also, undoubtedly, one of the least controversial. With artificial intelligence applications progressing at break-neck speed, both in the U.S. and other tech hubs like China and India, government bodies, thought leaders, and tech giants have all so far failed to establish a common vocabulary or a shared vision for whats to come.

As with most issues discussed in The Age of AI, the stakes are exponentially higher when the potential military uses for AI enter the picture. Here, more often than not, countries are talking past each other and operating with little knowledge of what the other is doing. This lack of common understanding, Kissinger and Co. wager, is like a forest of bone-dry kindling waiting for an errant spark.

Major countries should not wait for a crisis to initiate a dialogue about the implicationsstrategic. doctrinal, and moralof these [AIs] evolutions, the authors write. Instead, Kissinger and Schmidt say theyd like to see an environment where major powers, both government and business, pursue their competition within a framework of verifiable limits.

Negotiation should not only focus on moderating an arms race but also making sure that both sides know, in general terms, what the other is doing. In a general sense, the institutions holding the AI equivalent of a nuclear football have yet to even develop a shared vocabulary to begin a dialogue.

Link:
Here's What Henry Kissinger Thinks About the Future of Artificial Intelligence - Gizmodo