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AI in Neuroscience: A Bold Impact on Acute Stroke Care

When it comes to strokes, time is not only money… time is impacting potential brain damage. For every minute that a stroke limits blood supply, nearly two million brain cells are lost. However, this doesn’t have to be the case. Since 2015, the gold standard for treating certain strokes is the removal of the clot or obstruction using neuroradiology interventions. Unfortunately, only a small percentage of patients are able to receive this treatment in time. But Dr. Chris Mansi plans to change all of that. With his recently FDA-approved artificial intelligence neuroscience platform, Viz.AI, AI in neuroscience and stroke care is making a bold difference.

A Need for AI in Neuroscience Breakthroughs

As an FDA-approved platform, Viz LVO is a computer-aided triage and notification system offering artificial intelligence stroke care. Typically, large vessel stroke patients must receive imaging studies, have these interpreted, and be transferred to a specialist for treatment. But the delay in this process is a serious problem. In fact, the time from the imaging study to notification of a stroke team averages over an hour. Research has shown that for every minute of delay in stroke care, another week of disability is added.

Overall, stroke is the number one cause of disability in the U.S. Stroke is also the fifth leading cause of death. Estimates regarding the costs of stroke in the country exceed $220 billion annually.

And without treatment, strokes can cost as much as $1 million per event. But with treatment, this cost can be reduced ten-fold. These statistics are evidence of how AI neuroscience breakthroughs could be beneficial. This was Viz.AI’s vision when it introduced its AI stroke care application.

How Viz.AI is Changing AI Stroke Care

Viz.AI’s flagship platform in artificial intelligence neuroscience care is Viz LVO. But the company has several other machine learning platform applications as well. For example, Viz CTP provides physicians and researchers with color maps that correlate with the brain’s blood flow.

Viz Hub is a HIPAA-compliant telecommunications tool that helps coordinate care among providers.

And Viz View provides a non-diagnostic quality, mobile imaging platform for neuroimaging sharing. All of these are utilized as part of Viz.AI’s artificial intelligence stroke care platform.

The proof of Viz.AI’s artificial intelligence neuroscience platform is in its results. When used, Viz LVO reduces the time from imaging to specialist notification to an average of six minutes. How does it do this? CT scan images are interpreted by AI stroke care software. Then stroke team specialists are notified of the results, the patient’s exam, and the need for care.

All of this happens seamlessly on a mobile device using the Viz.AI application. The use artificial intelligence for CT image interpretation has been the revolutionary piece that offers tremendous potential for stroke care.

The Future of  AI and Neuroscience

Viz.AI, Inc. operates out of San Francisco and is led by CEO Chris Mansi, MD. A former neurosurgeon, Dr. Mansi practiced in London for many years before studying applied technologies at Stanford University. It was here that he met David Golan, a machine learning researcher. The two paired up and launched Viz.AI, Inc. Their initial vision was to revolutionize stroke care through artificial intelligence technologies. But Viz.AI plans to explore an array of AI neuroscience solutions in the future.

If their effects are comparable to Viz LVO’s impact on stroke care, the future of AI in neuroscience care looks very promising.

Big Data Oncology: Pioneering a New Wave of Cancer Treatment

The medical field has been making considerable advances in cancer research. The number of approved therapies continues to increase at an accelerated pace. Thanks to bold investments and innovation, patients are living longer with the disease than ever before. But greater treatment options and a higher number of patients living longer means a vastly greater dataset for researchers and doctors to draw inferences from. What to do with this cancer-specific “big data”? The answer is, of course, to merge big data and cancer research. Now is the perfect time to explore more solutions through big data oncology.

How Big Data is Transforming Oncology

To stay ahead in the fight against cancer, institutions are undergoing a digital transformation. Big data is at the core of this technological revolution. Its usage involves tapping into large pools of clinical, biological, and genomic information. As a result, researchers have been able to make advances in treatment and diagnosis.

“We are now capable of collecting amounts of data that we never thought were possible before,” said Dr. Bissan Al-Lazikani, head of data science at The Institute of Cancer Research. “We are starting to really understand the complexities of cancer at a level that is unprecedented. This has really revolutionized the way we do drug discovery.”

Using big data in cancer research is needed for better machine learning. As technology systems gather more data and profile more patients, the smarter the computer algorithms get.  Artificial intelligence allows medical institutions to gain real-time insights. Such insights from big data oncology can ultimately improve cancer care and outcomes even more.

Leaders in Using Big Data for Immuno-Oncology

In 2017, biotech company F1 Oncology raised $37 million for its work in immuno-oncology. Last January, the company announced an additional $10 million investment for two CAR-T products. These will be tested on patients with refractory, metastatic cancer. Big data will power the precision medicine-directed trial.

The company uses analytics for big data in cancer research instead of building off assumptions. This allows F1 to freely innovate and take more intellectual risks. Also, the company’s infrastructure and data transfer platform allow a wider selection of CAR constructs. This helps in choosing optimal compositions from thousands of options.

Gregory Frost, Ph.D. Chairman & CEO of F1 Oncology is hopeful about the company’s contribution to immuno-oncology. “The clinical investigation of these first tumor microenvironment-controlled CAR-T products, each with unique targets and activating domains and properties, will advance the field towards the objective of making CAR-T more broadly applicable to patients suffering from solid tumor malignancies,” he said.

Another company leveraging big data and artificial intelligence is Sophia Genetics. Along with DNA Sequencing, big data and AI help detect certain types of cancer. Results are then compared using machine learning. Through this process of big data oncology, patients are able to receive suggested treatment.

Mixing big data and cancer research has yielded positive results.
Big data and cancer research have come together to produce great strides in treating the disease.

Recent Milestones in Cancer Research

In the past couple of years, there are more choices for treating a range of cancers.  And there’s no stopping the growth of treatment options. In fact, the U.S. market for oncology therapeutic medicines will reach as much as $100 billion by 2022.

With the rise of immuno-oncology, researchers are testing different combinations of immune-boosting treatments. Combinations include either several similar drugs or drugs and older treatments like chemotherapy. These experiments can lead to more innovative developments.

The entry of novel cures guided by precision medicine is also notable.  Chimeric Antigen Receptor T-cell  (CAR-T) treatments resulted in 70% or higher cure rates for previously untreatable blood cancers.

All of this stems from the merging of big data and cancer research.

The Promising Future of Big Data Oncology

The healthcare industry is embracing the use of technology for finding cures. Even tech giant Google is exploring the medical applications of artificial intelligence.  This indicates that digital solutions have helped speed up progress in the field. With more innovations by bold institutions, society can have a better understanding of cancer treatment.

Experts must look at solutions on how to widen the acceptance of data sharing. This can take cancer research to the next level. Right now, more and more patients are opening up to the idea of releasing their data for analysis. Despite the negative perceptions, this can help researchers create better-personalized treatments.

The use of mobile apps and wearable devices to gather information is also likely to increase in the coming years. These can track activities of cancer patients and even help make day-to-day management easier.

Analysts say that medical applications of big data and cancer research remain in their infancy. Still, if improvements continue at the rate they’re currently going, the future of big data oncology seems promising. More and more patients will be able to get individualized, less invasive treatments that can improve their odds of survival.

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