Artificial Intelligence Beats Some Radiologists at Spotting Bleeds in the Brain

Dennis Thompson wrote . . . . . . . . .

Computer-driven artificial intelligence (AI) can help protect human brains from the damage wrought by stroke, a new report suggests.

A computer program trained to look for bleeding in the brain outperformed two of four certified radiologists, finding abnormalities in brain scans quickly and efficiently, the researchers reported.

“This AI can evaluate the whole head in one second,” said senior researcher Dr. Esther Yuh, an associate professor of radiology at the University of California, San Francisco. “We trained it to be very, very good at looking for the kind of tiny abnormalities that radiologists look for.”

Stroke doctors often say that “time is brain,” meaning that every second’s delay in treating a stroke results in more brain cells dying and the patient becoming further incapacitated.

Yuh and her colleagues hope that AI programmed to find trouble spots in a brain will be able to significantly cut down treatment time for stroke patients.

“Instead of having a delay of 20 to 30 minutes for a radiologist to turn around a CT scan for interpretation, the computer can read it in a second,” Yuh said.

Stroke is the fifth-leading cause of death in the United States, and is a leading cause of disability, according to the American Stroke Association.

There are two types of strokes: ones caused by burst blood vessels in the brain (hemorrhagic), and others that occur when a blood vessel becomes blocked (ischemic).

Yuh’s AI still needs to be tested in clinical trials and approved by the U.S. Food and Drug Administration, but other programs are already helping doctors speed up stroke treatment, said Dr. Christopher Kellner. He is director of the Intracerebral Hemorrhage Program at Mount Sinai, in New York City.

“We are already using AI-driven software to automatically inform us when certain CAT scan findings occur,” he said. “It’s already become, in just the last year, an essential part of our stroke work-up.”

An AI created by a company called Viz.ai is being used at Mount Sinai to detect blood clots that have caused a stroke by blocking the flow of blood to the brain, Kellner said.

Yuh and her team used a library of nearly 4,440 CT scans to train their AI to look for brain bleeding.

These scans are not easy to read, she said. They are low-contrast black-and-white images full of visual “noise.”

“It takes a lot of training to be able to read these — doctors train for years to be able to read these correctly,” Yuh said.

Her team trained its algorithm to the point that it could trace detailed outlines of abnormalities it found, demonstrating their location in a 3-D model of the brain being scanned.

They then tested the algorithm against four board-certified radiologists, using a series of 200 randomly selected head CT scans.

The AI slightly outperformed two radiologists, and slightly underperformed against the other two, Yuh said.

The AI found some small abnormalities that the experts missed. It also provided detailed information that doctors would need to determine the best treatment.

The computer program also provided this information with an acceptable level of false positives, Yuh said. That would minimize how much time doctors would need to spend reviewing its results.

Yuh suspects radiologists always will be needed to double-check the AI, but Kellner isn’t so sure.

“There will definitely be a point where there’s no human involved in the evaluation of the scans, and I think that’s not too far off, honestly,” he said. “I think, ultimately, a computer will be able to scan that faster and send out an alert faster than a human can.”

The new study was published in the Proceedings of the National Academy of Sciences.

Source: HealthDay


Today’s Comic

Artificial Intelligence (AI) Technology for Advanced Heart Attack Prediction

Lisa Jones wrote . . . . . . . . .

Researchers at the University of Oxford have developed a new biomarker, or ‘fingerprint’, called the fat radiomic profile (FRP), using machine learning. The FRP detects biological red flags in the space lining blood vessels which supply blood to the heart. It identifies inflammation, scarring and changes to these blood vessels, which are all pointers to a future heart attack.

When someone goes to hospital with chest pain, a standard component of care is to have a coronary CT angiogram (CCTA). This is a scan of the coronary arteries to check for any narrowed or blocked segments. If there is no significant narrowing of the artery, which accounts for about 75 per cent of scans, people are sent home, yet some of them will still have a heart attack at some point in the future. There are no methods used routinely by doctors that can spot all of the underlying red flags for a future heart attack.

In this study, Professor Charalambos Antoniades and his team firstly used fat biopsies from 167 people undergoing cardiac surgery. They analysed the expression of genes associated with inflammation, scarring and new blood vessel formation, and matched these to the CCTA scan images to determine which features best indicate changes to the fat surrounding the heart vessels, called perivascular fat.

Next, the team compared the CCTA scans of the 101 people, from a pool of 5487 individuals, who went on to have a heart attack or cardiovascular death within 5 years of having a CCTA with matched controls who did not, to understand the changes in the perivascular space which indicate that someone is at higher risk of a heart attack. Using machine learning, they developed the FRP fingerprint that captures the level of risk. The more heart scans that are added, the more accurate the predictions will become, and the more information that will become ‘core knowledge’.

They tested the performance of this perivascular fingerprint in 1,575 people in the SCOT-HEART trial, showing that the FRP had a striking value in predicting heart attacks, above what can be achieved with any of the tools currently used in clinical practice.

The team hope that this powerful technology will enable a greater number of people to avoid a heart attack, and plan to roll it out to health care professionals in the next year, with the hope that it will be included in routine NHS practice alongside CCTA scans in the next 2 years.

Professor Charalambos Antoniades, Professor of Cardiovascular Medicine and BHF Senior Clinical Fellow at the University of Oxford, said:

“Just because someone’s scan of their coronary artery shows there’s no narrowing, that does not mean they are safe from a heart attack. By harnessing the power of AI, we’ve developed a fingerprint to find ‘bad’ characteristics around people’s arteries. This has huge potential to detect the early signs of disease, and to be able to take all preventative steps before a heart attack strikes, ultimately saving lives.

“We genuinely believe this technology could be saving lives within the next year.”

Professor Metin Avkiran, our Associate Medical Director said:

“Every 5 minutes, someone is admitted to a UK hospital due to a heart attack. This research is a powerful example of how innovative use of machine learning technology has the potential to revolutionise how we identify people at risk of a heart attack and prevent them from happening. This is a significant advance. The new ‘fingerprint’ extracts additional information about underlying biology from scans used routinely to detect narrowed arteries. Such AI-based technology to predict an impending heart attack with greater precision could represent a big step forward in personalised care for people with suspected coronary artery disease.”

Source: British Heart Foundation


Today’s Comic

Artificial Intelligence Could Use EKG Data to Measure Patient’s Overall Health Status

In the near future, doctors may be able to apply artificial intelligence to electrocardiogram data in order to measure overall health status, according to new research published in Circulation: Arrhythmia and Electrophysiology, a journal of the American Heart Association.

An electrocardiogram, also known as an EKG or ECG, is a test used to measure the electrical activity of the heart. While it’s known that a patient’s sex and age could affect an EKG, researchers hypothesized that artificial intelligence could determine a patient’s gender and estimate their ’physiologic age’ — a measure of overall body function and health status distinct from chronological age.

Using EKG data of almost 500,000 patients, a type of artificial intelligence known as a convolutional neural network was trained to find similarities among the input and output data. Once trained, the neural network was tested for accuracy on the data of an additional 275,000 patients by predicting the output when only given input data.

The neural network estimated a patient’s chronological age as higher after experiencing adverse health situations such as heart attack, low ejection fraction and coronary artery disease, and lower age if they experienced few or no adverse events.

“While physicians already consider whether a patient ‘appears [their] stated age’ as part of their baseline physical examination, the ability to more objectively and consistently assess this may impact healthcare on multiple levels,” said study author Suraj Kapa, M.D., assistant professor of medicine and director for Augmented and Virtual Reality Innovation at Mayo Clinic in Rochester, Minnesota.

“Being able to more accurately assess overall health status may help doctors determine which patients they should examine further to determine if there are asymptomatic or currently silent diseases that could benefit from early diagnosis and intervention. For people at large, an AI-enhanced electrocardiogram could better show there may be something going on such as a new health issue or comorbid condition that they were otherwise unaware of,” continued Kapa.

Researchers discovered that the artificial intelligence was able to accurately determine a patient’s gender 90% of the time and could determine the chronological age group of a patient with 72% accuracy.

“This evidence — that we might be gleaning some sort of ‘physiologic age’ — was certainly both surprising and exciting for its potential role in future outcomes research, and may foster a new area of science where we seek to better understand the biologic underpinnings of such a finding,” Kapa said.

While the study was able to draw from a large sample size, all individuals in the study were patients, and EKGs were administered for another clinical indication. Future studies with an overtly healthy population are needed to revalidate the neural network’s determination. Additionally, gender in the study was self-identified by patients and may not represent the sex of all individuals in the study.

Source: American Heart Association

Video: Domino’s Has a New AI Tool to Assess Pizza Quality

In Australia and New Zealand, the company is debuting its DOM Pizza Checker, which is a smart scanning device that hangs above the cut bench at Domino’s locations and uses AI, machine learning, and sensor tech to assess the quality of the pizzas before they’re sliced and boxed up.

When the pizza arrives at the cut bench, DOM compares its quality to existing pizza images stored in its database and grades the pie based on whether it’s the right kind (e.g., thin crust versus thick) with the right toppings which are evenly distributed. If the pizza doesn’t pass muster, DOM will notify the team of human workers, who will make the pie again.

Watch video at You Tube (1:21 minutes) . . . . .

Deep Aging Clocks: The Emergence of AI-based Biomarkers of Aging and Longevity

There are two kinds of age: chronological age, which is the number of years one has lived, and biological age, which is influenced by our genes, lifestyle, behaviour, the environment and other factors. Biological age is the superior measure of true age and is the most biologically relevant feature, as it closely correlates with mortality and health status. The search for reliable predictors of biological age has been ongoing for several decades, and until recently, largely without success.

Since 2016 the use of deep learning techniques to find predictors of chronological and biological age has been gaining popularity in the aging research community. Advances in artificial intelligence, combined with the availability of large datasets, have led to a boom in the field, increasing the variety of biomarkers that could be considered candidates as potential age predictors. One promising development that considers multiple combinations of these different predictors could shed light on the aging process and provide further understanding of what contributes to healthy aging.

In the paper titled “Deep Aging Clocks: The Emergence of AI-Based Biomarkers of Aging and Longevity” in Cell Trends in Pharmacological Sciences, Polina Mamoshina, Senior Scientist at Insilico Medicine, and Alex Zhavoronkov, the Founder of Insilico Medicine, summarise current findings on the main types of deep aging clocks and their broad range of applications in pharmaceutical industry.

“Humans are very good at guessing each other’s age using images, videos, voice, and even smell. Deep neural networks can do it better and we can now interpret what factors are most important. Very often when someone looks older than their chronological age, they are sick. A trained doctor can guess the health status of a patient just by looking at him or her. At Insilico we developed a broad range of deep biomarkers of aging that can be used by the pharmaceutical and insurance companies, as well as by the longevity biotechnology community. In this paper we describe the recent progress in this emerging field and outline a range of non-obvious applications,” said Alex Zhavoronkov, Ph.D, Founder and CEO of Insilico Medicine.

Deep biological aging clocks can be used for data quality control, biological target identification and even the evaluation of the biological relevance and value of various data types and combinations. The recent perspective on the value of human data recently appeared in Cell Trends in Molecular Medicine.

“Deep biomarkers of aging developed utilizing a variety of data types of aging are rapidly advancing the longevity biotechnology industry. Using biomarkers of aging to improve human health, prevent age-associated diseases and extend healthy life span is now facilitated by the fast-growing capacity of data acquisition, and recent advances in AI. They hold a great potential for changing not only aging research, but healthcare in general,” said Polina Mamoshina, Senior Scientist at Insilico Medicine.

Source : EurekAlert!