Category Archives: Artificial Intelligence

AI Can Deliver Specialty-Level Diagnosis In Primary Care Setting

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A system designed by a University of Iowa ophthalmologist that uses artificial intelligence (AI) to detect diabetic retinopathy without a person interpreting the results earned Food and Drug Administration (FDA) authorization in April, following a clinical trial in primary care offices.

Results of that study were published Aug. 28 online in Nature Digital Medicine, offering the first look at data that led to FDA clearance for IDx-DR, the first medical device that uses AI for the autonomous detection of diabetic retinopathy.

The clinical trial, which also was the first study to prospectively assess the safety of an autonomous AI system in patient care, compared the performance of IDx-DR to the gold standard diagnostic for diabetic retinopathy, which is the leading cause of vision loss in adults and one of the most severe complications for the 30.3 million Americans living with diabetes.

IDx-DR exceeded all pre-specified superiority endpoints in sensitivity, the ability to correctly identify a patient with disease; specificity, the ability to correctly classify a person as disease-free; and imageability, or the capability to produce quality images of the retina and determine the severity of the disease.

“The AI system’s primary role is to identify those people with diabetes who are likely to have diabetic retinopathy that requires further evaluation by an eye-care provider. The study results demonstrate the safety of autonomous AI systems to bring specialty-level diagnostics to a primary care setting, with the potential to increase access and lower cost,” says Michael Abràmoff, MD, PhD, the Robert C. Watzke Professor of Ophthalmology and Visual Sciences with UI Health Care and principal investigator on the study. He is founder and president of IDx, the company that created the IDx-DR system and funded the study.

Early detection may prevent vision loss

More than 24,000 people in the U.S. lose their sight to diabetic retinopathy each year. Early detection and treatment can reduce the risk of blindness by 95 percent, but less than 50 percent of patients with diabetes schedule regular exams with an eye-care specialist.

In the study, 900 adult patients with diabetes–but no history of diabetic retinopathy–were examined at 10 primary care sites across the U.S. Retinal images of the patients were obtained using a robotic camera, with an AI assisting the operator in getting good quality images. Once the four images were complete, the diagnostic AI then made a clinical diagnosis in 20 seconds. The diagnostic AI detects disease just as expert clinicians do, by having detectors for the lesions characteristic for diabetic retinopathy, including microaneurysms, hemorrhages, and lipoprotein exudates.

Camera operators in the study were existing staff of the primary care clinics, but not physicians or trained photographers.

“This was much more than just a study testing an algorithm on an image. We wanted to test it in the places where it will be used, by the people who will use it, and we compared it to the highest standard in the world,” says Abràmoff, who also holds faculty appointments in the UI College of Engineering.

AI measured against gold standard

Study participants also had retinal images taken at each of the primary care clinics using specialized widefield and 3D imaging equipment without AI operated by experienced retinal photographers certified by the Wisconsin Fundus Photograph Reading Center (FPRC)–the gold standard in grading the severity of diabetic retinopathy.

Complete diagnostic data accomplished by both the AI system and FPRC readers was available for 819 of the original 900 study participants. FPRC readers identified 198 participants with more than mild diabetic retinopathy who should be further examined by a specialist; the AI was able to correctly identify 173 of the 198 participants with disease, resulting in a sensitivity of 87 percent. Among the 621 disease-free participants identified by FPRC readers, AI identified 556 participants, for a specificity of 90 percent. The AI had a 96 percent imageability rate: of the 852 participants who had an FPRC diagnosis, 819 had an AI system diagnostic output.

In June, following FDA clearance, providers at the Diabetes and Endocrinology Center at UI Health Care-Iowa River Landing in Coralville, Iowa, were the first in the nation to begin using IDx-DR to screen patients.

“We are hoping to do this also for early detection of diseases like glaucoma and macular degeneration. We are working on those algorithms already. The goal is to get these specialty diagnostics into primary care and retail, which is where the patients are,” Abràmoff says.

IDx is working with the American Medical Association to ensure that there is clear coding guidance for billing of IDx-DR. Providers, physicians, and suppliers should contact their third-party payers for specific and current information on their coding, coverage, and payment policies. IDx is a licensed distributor of the robotic camera used in the study.

SOURCE: www.scienceblog.com/503020

From molecule to medicine, the importance of persistence

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Here, Dr Sheuli Porkess, deputy chief scientific officer, Association of the British Pharmaceutical Industry (ABPI), outlines how the pharmaceutical industry takes a substance from molecule to medicine and how the process requires persistence.

A report last week from the Office of Health Economics (OHE) shows the amazing impact medicines have had on the NHS and more widely. The antipsychotic chlorpromazine, first used in the NHS in 1954, paved the way for deinstitutionalisation and community-based care for people with mental illness. In 1948, there were almost 400,000 cases of measles in England and Wales, and 327 people died. By 2015 the number of cases of measles in England and Wales had fallen below 1,200.

These medicines, and others, had a variety of benefits including better clinical outcomes, saving lives, improving quality of life, greater health service efficiency and wider societal impacts. But making medicines is a complicated and costly business. It costs billions of pounds and can take decades. Successes can change the world; failures are an inevitable part of the discovery and development process. But when medicines get through the development process, they can clearly change millions of lives.

There are broadly three stages to creating a new medicine: research, development and approval. Here’s how it works:

Drug discovery and development

The process usually starts with chemical compounds or biological molecules. With advances in technology over the last few years, we can screen compounds that have the potential to become treatments faster than ever before. AstraZeneca — a British pharmaceutical company — launched a new screening robot in 2016 called ‘NiCoLA-B’ which is able to test 300,000 compounds a day. Its job is to find those chemicals that show the slightest potential of being useful as a medicine.

The research stage benefits hugely from collaborative partnerships between the pharmaceutical industry, charities and universities, all working together to find a potential medicine. This stage can take four to five years and takes about 22% of the total budget it takes to find a treatment. Each compound has a less than 0.01% chance of success.

Preclinical research

From a batch of about 10,000 compounds screened in the drug discovery phase, only about 10–20 go into the pre-clinical phase, where scientists determine how safe a medicine might be through testing in cells and animals as well as using computational models.

Clinical research

If any of those 10-20 compounds show real potential of being turned into something useful, they’re developed in to a medicine that will move into clinical trial stage. There are three steps: Phase I involves about 20 to 100 volunteers. If medicines are successful here, they will move onto Phase II where they are tested in people with the disease.

Phase III can include up to 5,000 patients. Going through the three phases can take six or seven years. Over half, or about 65%, of the money it takes to make a medicine is spent in the development stage.

Phase IV clinical trials are after the medicine has a licence and are there to help monitor the medicine’s safety and help clinicians better understand how the medicine works in everyday life, not just in clinical trials.

Approval

The final stage is when regulators review the medicine and it can get ‘market authorisation’ — which shows the medicine is safe and effective. By this point, the manufacturing of the medicine has been scaled up. Only one medicine of 5,000–10,000 compounds discovered will make it to this stage.

The approval processes last anywhere from six months to two years. The medicine is continually monitored once it starts being prescribed for patients.

Researching and developing medicines takes a lot of time and work along the way; there is no guarantee that any particular medicine will make it through the various stages of this highly regulated process. The process is fascinating and once medicines get through this system, their impact can be huge.

Of course, the pharmaceutical industry is pioneering new ways to find treatments. The future looks exciting and how we detect, diagnose and treat disease is set to change significantly.

Advances in medical technology and the miniaturisation of diagnostics, wearables and devices will have a huge impact on our lives and could help people with chronic diseases to remain out of hospital.

Advances in understanding how cells monitor and repair damaged DNA enables us to develop game-changing treatments for cancer. Progress in immuno-oncology sees patients own immune cells used to attack cancer cells, and stem cell therapy is treating rare sight conditions.

We see AI and synthetic biology used for treating malaria, HIV and hepatitis. Gene-editing technology is happening in labs right now, identifying new disease targets, accelerating the discovery of novel treatments.

Passionate pioneers, such as those who invented the groundbreaking treatments in the report, have always been at the heart of our industry and it’s exciting to imagine what their successors could achieve in the next 70 years.

SOURCE: www.epmmagazine.com/opinion

Pfizer eyes AI-powered drug discovery and development software

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Pfizer has partnered with a ‘computation-driven’ pharmaceutical technology company to develop a drug discovery platform powered by artificial intelligence (AI).

Its collaboration with Cambridge, Massachusetts-based XtalPi will see the firms work on molecular modelling software that can be applied to drug-like small molecules.

Charlotte Allerton, Pfizer’s head of medicine design, said: “The XtalPi collaboration is an opportunity to enhance our computational modelling capabilities.

“We are looking forward to potentially utilising new tools to increase our effectiveness in small molecule drug discovery and development.”

In addition to supporting its own efforts, Pfizer plans to make available to the wider academic community some of the molecular mechanics parameters it will generate with public-domain compounds.

The new software platform will combine quantum mechanics, machine learning algorithms and cloud computing architecture to help Pfizer predict pharmaceutical properties that would be relevant for drug discovery and development.

Shuhao Wen, XtalPi’s co-founder and chairman of the board, said: “The collaboration allows us to apply our expertise in molecular modelling, AI, and cloud computing towards improving existing computational methods while exploring new algorithms to address a wide range of drug design challenges.

“We look forward to helping expedite research into novel therapeutics as our intelligent digital drug discovery and development platform continues to expand and succeed.”

The deal builds on XtalPi’s existing work with Pfizer on crystal structure prediction (CSP), with that project aiming to advance the partners’ capabilities in computation-based rational drug design and solid-form selection.

Founded in 2014 by a group of quantum physicists from MIT, XtalPi’s team combines expertise in physics, chemistry, pharmaceutical R&D, and algorithm design.

The company, which counts Google and Chinese internet conglomerate Tencent among its investors, is one of a swathe of AI players looking to innovate drug discovery and development processes.

These include BenevolentAI, Hitachi and Scotland’s Exscientia, with the latter working with the likes of GlaxoSmithKline, Sanofi and Evotec.

At stake is a share in market for healthcare AI applications that’s predicted to be worth more than $10 billion by 2024, driven by the rise in precision medicine and the need to reduce healthcare costs.

SOURCE: www.pharmaphorum.com/news

BRITISH AI FIRM RAISES £80M, VALUING IT AT £1.4BN

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London-based BenevolentAI has closed an £80m funding round which included investment from the US as well as from existing backers.

British artificial intelligence firm BenevolentAI has raised £80.8m, valuing the group at £1.4 bn and marking one of the largest funding rounds in the AI pharmaceutical sector.

The firm secured the funding from investors largely based in the US, as well as from existing backers such as Woodford Investment Management.

BenevolentAI is applying artificial intelligence to develop new medicines for hard to treat diseases. To date, it has raised more than £141m in funding since its launch in 2013.

The company will use this latest cash injection to ramp up its drug development, broadening the disease areas on which it focuses and advance these programmes to the clinic.

The group – Europe’s largest private AI company – will also use the funds to further develop its self-learning system, while also helping the firm to expand outside the pharmaceutical sector to other science-based industries, such as energy storage and agriculture.

Ken Mulvany, founder and chairman of BenevolentAI, said: “We are very pleased with the response to the fundraising. It reflects the rapidly growing global interest in the AI pharmaceutical sector and the recognition of our place as the dominant player within it.

“We have come a very long way since we founded the business in 2013. The capabilities of our technology didn’t exist six years ago.

“We are pioneering this sector and have evolved into a fully integrated, AI enabled drug development company with the ability to deliver better medicines at previously unimaginable speeds – this ultimately means patients will receive the right medicines, at a lower cost, in less time.”

BenevolentAI currently employs 165 people who work in a unique, cross functional environment that incorporates leading edge data scientists, computer scientists, mathematicians and drug development R&D scientists working side by side.

The company is headquartered in London with further offices in New York and Belgium. It also has a research facility at Babraham Science Park in Cambridge.

SOURCE: www.bqlive.co.uk/london-the-south/2018/04/19

The third wave of AI in pharma R&D

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The capabilities of artificial intelligence are advancing and its ‘third wave’ offers the ability to analyse enormous sets of data, identify patterns, and generate algorithms to explain them, to the benefit of researchers.

The digital revolution vastly accelerated the research, development, and production of new drugs. Digital technology has augmented the natural capabilities of researchers and scientists in a variety of ways. Now, artificial intelligence (AI) is poised to take this augmentation to the next level.

One of the most significant ways in which AI technology augments human capacity – particularly in an R&D context – is by automating repetitive, lower-level cognitive functions that once had to be carried out manually. This liberates drug researchers to focus on higher-level thinking. This advantage was identified early in the development of AI technology by J C R Licklider, who wrote back in 1960 in Man-Computer Symbiosis:

“About 85% of my ‘thinking’ time was spent getting into a position to think, to make a decision, to learn something I needed to know. Much more time went into finding or obtaining information than into digesting it.”… “Several hours of calculating were required to get the data into comparable form. When they were in comparable form, it took only a few seconds to determine what I needed to know.”

A related idea was expressed by Herbert A Simon with the concept of ‘bounded rationality. He wrote that humans’ decision making can be optimised when they are provided with a limited quantity of relevant, focused information and sufficient time to process it.

With the advent of contemporary AI technologies, the bounds of human rationality have been expanded. AI provides drug researchers with a greater breadth and depth of data that is simultaneously more focused and relevant than the data sets of the past, enabling researchers to optimise their decision-making capacities.

The continued advancement of AI will augment humans’ power of critical thinking in three key areas that are relevant to the medical and pharmaceutical industries: computing advanced mathematical problems, analyzing complex statistics, and generating novel hypotheses. These areas correspond to the ‘three waves’ of AI development throughout the 20th and 21st centuries.

The first and second waves

The first wave of AI development brought us ‘knowledge engineering’ optimisation programs, which solved real-world problems efficiently.

While applications specific to the pharmaceutical industry were scarce, the broader medical field benefits from first-wave AI technologies every day. Take the Framingham Risk Score Calculator, which utilises AI to predict the heart disease risk of any patient.

Machine learning programs were brought along by the second wave of AI. These solve complex pattern recognition problems using statistical analysis. Unlike their first-wave predecessors, second-wave AI programs perceive and learn – often as well as humans do.

Clinical decision support systems use second-wave pattern-recognition programs to analyse and evaluate medical test results. Similar machine-learning programs are beginning to be used by leading pharma firms in a variety of research and development contexts to predict drug effectiveness, to discover new compounds with pharmaceutical qualities, and to develop new combinations of existing drugs.

Second-wave AI is powerful, but it demands well-organised, consistently-coded, and complete data sets in order to accurately conduct its analyses. This limitation is now being overcome by the third wave of AI.

The third wave

We have now entered the third wave of AI development. Third-wave AI programs have the capacity to analyse enormous sets of data, identify patterns, and generate algorithms to explain them. These programs normalise the context of disparate data points and generate original, novel hypotheses at a faster rate and with greater accuracy than human researchers can.

Only in this third wave have AI programs reached a sufficiently advanced state to effectively analyse the vast and complex web of unstructured biological data. Until recently, biological data had to be manually cleaned and organised through extensive and costly human effort. Now, AI programs use a combination of machine learning, natural language processing, and text analytics to analyse unstructured data in real-time.

Through context normalisation, third-wave AI technology dramatically increases the quantity of data that can be analysed in the course of the drug discovery and testing process. Furthermore, it enables the simultaneous generation and testing of new hypotheses at a rate that would be impossible without such immense computing power.

Aided by this technology, drug researchers can arrive at a higher quantity of more accurate hypotheses and can test these hypotheses with unprecedented speed. The result is a significantly faster, and less expensive, discovery process, with lower risk and more effective results. Firms such as Pfizer and Johnson & Johnson are employing such methods to great effect.

Given that R&D consumes as much as 20% of pharma firms’ revenues, and that the total price of developing a new drug has ballooned by 250% in the last 30 years, it’s not surprising that firms are eager to embrace third-wave AI as a means of accelerating drug development.

SOURCE: www.pharmaphorum.com

AI predicts heart attack risk factors from retinal scans

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Artificial Intelligence can be used to predict heart attack risk using retinal images, according to new research backed by Google.

The researchers trained deep learning algorithms on data from thousands of patients recorded in a massive UK study, which was used with retinal scans to produce a program that can identify risk factors from the scan information alone.

This predicted cardiovascular risk factors not previously thought to be quantifiable using retinal images: these included age, gender, smoking status, gender, systolic blood pressure and major adverse cardiac events.

Reporting results in the journal Nature Biomedical Engineering, the team used a dataset from the UK’s Biobank, a study where 500,000 were recruited between 2006 and 2010, and agreed to have certain health measurements recorded.

Health outcomes such as hospitalisation, mortality and cause of death were also logged. Smoking status was obtained via survey using a touchscreen interface in the research backed by Google and Verily, Alphabet’s healthcare subsidiary.

Participants were also asked to identify whether they are a current smoker, former smoker, or had never smoked, and blood pressure readings were also taken.

A further 67,725 had paired images of their retina fundus taken, along with a second group that was used to create a training dataset with known risk factors.

The researchers asked a neural network to make an output prediction based on the fundus image.

It was able to analyse images from the group with unknown risk factors, compare it with the training data set and after the process was repeated, was eventually able to predict cardiovascular risk factors from new images.

This is just the latest in a series of studies showing the potential of AI to predict healthcare outcomes, saving lives and reducing costs by ensuring patients receive timely and sometimes life-saving treatments.

Early last month, the government’s life sciences tsar, Sir John Bell, said that similar techniques could save the NHS billions.

A team at Oxford’s John Radcliffe Hospital is using AI to identify abnormalities in ECG read-outs that could be missed by the human eye, for instance.

The hope is that this system could be used in hospitals across the country to prevent unnecessary hospitalisations caused by false positives, and prevent heart attacks where at-risk patients are sent home because doctors have failed to spot problems.

Another AI system outperformed a panel of experts when asked to diagnose breast cancer based on stained tissue samples, in a separate study published in the Journal of the American Medical Association last month.

SOURCE: www.pharmaphorum.com/news

Microsoft transforms diagnosis with AI systems

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The AI system aims to quicken the process of finding illnesses from blood tests.

Diagnosing patients just got a lot easier thanks to a Microsoft partnership with a biotech company that uses AI to check for multiple illnesses.

Taking the next step with technology in the healthcare sector, Microsoft has partnered with biotechnology company Adaptive to create a method for smarter blood tests using AI. The aim of using the technology is to check for hundreds of diseases at one given time, as a universal diagnostic tool.

The use of AI could drastically reduce the amount of time it takes for patients to be diagnosed, by having all types of cases catered for at one point, as well as putting patients through just one trawl of tests instead of multiple. In turn, the technology benefits doctors by saving them the job of carrying out multiple tests, when AI can do it in one swift move.

“We believe deeply in the potential for this partnership with Adaptive and have made a substantial financial investment in the company. We believe deeply in the potential for this partnership with Adaptive and have made a substantial financial investment in the company. With Microsoft’s help, adaptive biotechnologies will attempt to map the genetics of the human immune system or immunome,” Peter Lee, Corporate Vice President, AI and Research at Microsoft said.

Additionally, doctors hope that by checking for more than one disease at once will help with treatments by allowing doctors to look at correlations between different disease states and finding a better cure to target more than one illness.

The system will work by using AI to decode a patient’s immune system, come up with a diagnosis based on past trends and then treat disease with the best medication according to the system.

Adaptive Biotechnologies said to start with the two companies will focus on identifying diseases that are normally diagnosed at a later stage, such as pancreatic and ovarian cancer, and the AI system will help to identify these conditions much more easily and quickly in the future.

“Some conditions, like cancer or autoimmune disorders, can be difficult to diagnose,” said Chad Robbins, co-founder and chief executive officer of Adaptive Biotechnologies. “But this universal map of the immune system will enable earlier and more accurate diagnosis of disease.”

The partnership between the two companies is set to be discussed in further detail at the JP Morgan Healthcare Conference in San Francisco on Wednesday 10th January.

SOURCE: www.cbronline.com/news