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AI supported test for very early signs of glaucoma progression - Neuroscience News

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Summary: A new artificial intelligence algorithm can detect the progression of glaucoma up to 18 months earlier than conventional methods. A new test can detect glaucoma progression 18 months earlier than the current gold standard method, according to results from a UCL-sponsored clinical trial. The technology, supported by an artificial intelligence (AI) algorithm, could help accelerate clinical trials, and eventually may be used in detection and diagnostics, according to the Wellcome-funded study published today in Expert Review of Molecular Diagnostics. Lead researcher Professor Francesca Cordeiro (UCL Institute of Ophthalmology, Imperial College London, and Western Eye Hospital Imperial College Healthcare NHS Trust) said: "We have developed a quick, automated and highly sensitive way to identify which people with glaucoma are at risk of rapid progression to blindness." Glaucoma, the leading global cause of irreversible blindness, affects over 60 million people, which is predicted to double by 2040 as the global population ages.


AI Uncovers a Potential Treatment for Covid-19 Patients

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Late one January afternoon, British pharmacologist Peter Richardson ran out of his home office and told his wife, "Got it!" She asked what he was talking about and offered a cup of tea. Richardson explained that he had identified a drug that might help people infected with a new virus spreading in China. Richardson's dash was prompted by a finding from artificial intelligence software developed by his employer, BenevolentAI, a London startup where he is vice president of pharmacology. The company has created a kind of search engine on steroids that combines drug industry data with nuggets gleaned from scientific research papers.


Machine Learning May Predict Patient Satisfaction After Breast Reconstruction - Cancer Therapy Advisor

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Machine learning increasingly supports physician decisions by making it easier to detect patterns in data as a means of predicting patient outcomes. In breast cancer, that now could apply to every stage of the experience, from diagnostics to mastectomy and breast reconstruction. At the annual meeting of the American Society of Clinical Oncology -- which was virtual this year, due to the ongoing coronavirus pandemic -- a consortium of researchers presented an abstract detailing how machine learning algorithms were able to correctly predict how individual patients would feel about their breast reconstruction.1 Using this tool in a clinical setting could help physicians guide patients through the recovery process in a way that better anticipates, and subsequently supports, their emotional reaction to this intensely personal medical procedure. Physician-researchers across 11 institutions in the United States and Canada trained 4 different types of machine learning algorithms -- regularized regression, Support Vector Machine, Neural Network, Regression Tree -- to predict with 95% accuracy whether a specific patient would be satisfied or dissatisfied with their breast reconstruction 2 years after their operation.


Personalised cancer drug 'boosts the body's natural defences'

Daily Mail - Science & tech

A personalised cancer vaccine designed to boost the body's own natural defences when used alongside chemotherapy shows'promising signs' after a clinical trial. The treatment is created by taking a biopsy of a tumour and then using artificial intelligence to identify certain proteins not recognised by the immune system. They use these proteins to create tailor-made vaccines for each individual cancer patients and then administer them alongside immunotherapy drug atezolizumab. So far researchers have only tested it on patients with advanced cancers and just 8 per cent saw their tumours shrink - with 49 per cent seeing no change. An international team of researchers found the treatment, known as RO7198457, was'well tolerated' by patients and the they experienced'low-to-moderate' side effects. This is early days in the development of the treatment as the clinical trials were only designed to test its safety, further testing is needed to see how effective it is.


Using Small Datasets to Build Models DataRobot

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The world is going through extremely turbulent times. With the ongoing disruption of our lives, communities, and businesses from the COVID-19 pandemic, predictions from existing machine learning models trained prior to the pandemic become less reliable. There is plenty of historical data, but historical examples from before the pandemic may not provide the relevant examples needed to train a model that is useful today. We are left with only training data gathered in the most recent weeks, data which may, in turn, prove to be irrelevant even a few weeks from now as the situation continues to evolve. The patterns our existing models have uncovered have changed and are likely to continue to change.


What AI still can't do

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Machine-learning systems can be duped or confounded by situations they haven't seen before. A self-driving car gets flummoxed by a scenario that a human driver could handle easily. An AI system laboriously trained to carry out one task (identifying cats, say) has to be taught all over again to do something else (identifying dogs). In the process, it's liable to lose some of the expertise it had in the original task. Computer scientists call this problem "catastrophic forgetting."


AI is helping triage coronavirus patients. The tools may be here to stay.

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Rizwan Malik had always had an interest in AI. As the lead radiologist at the Royal Bolton Hospital, run by the UK's National Health Service (NHS), he saw its potential to make his job easier. In his hospital, patients often had to wait six hours or more for a specialist to look at their x-rays. If an emergency room doctor could get an initial reading from an AI-based tool, it could dramatically shrink that wait time. A specialist could follow up the AI system's reading with a more thorough diagnosis later.


A Machine Learning alternative to placebo-controlled clinical trials upon new diseases: A primer

arXiv.org Machine Learning

The appearance of a new dangerous and contagious disease requires the development of a drug therapy faster than what is foreseen by usual mechanisms. Many drug therapy developments consist in investigating through different clinical trials the effects of different specific drug combinations by delivering it into a test group of ill patients, meanwhile a placebo treatment is delivered to the remaining ill patients, known as the control group. We compare the above technique to a new technique in which all patients receive a different and reasonable combination of drugs and use this outcome to feed a Neural Network. By averaging out fluctuations and recognizing different patient features, the Neural Network learns the pattern that connects the patients initial state to the outcome of the treatments and therefore can predict the best drug therapy better than the above method. In contrast to many available works, we do not study any detail of drugs composition nor interaction, but instead pose and solve the problem from a phenomenological point of view, which allows us to compare both methods. Although the conclusion is reached through mathematical modeling and is stable upon any reasonable model, this is a proof-of-concept that should be studied within other expertises before confronting a real scenario. All calculations, tools and scripts have been made open source for the community to test, modify or expand it. Finally it should be mentioned that, although the results presented here are in the context of a new disease in medical sciences, these are useful for any field that requires a experimental technique with a control group.


Can AI Find a Cure for COVID-19?

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The novel coronavirus has been circulating among humans for barely three months, but several bio-tech firms have already created drugs that target the COVID-19 disease. One of the secret weapons for the fast response is artificial intelligence. The Chinese government initially was criticized for downplaying the severity of the coronavirus outbreak that originated in Wuhan last December. However, researchers around the world applauded the quick work of Chinese scientists in decoding the genetic sequence of the virus, dubbed SARS-CoV-2, and posting the results in a public database on January 10. Researchers quickly went to work.


Rapidly Personalizing Mobile Health Treatment Policies with Limited Data

arXiv.org Machine Learning

Mobile health (mHealth) interventions deliver treatments to users to support healthy behaviors. These interventions offer an opportunity for social impact in a diverse range of domains from substance abuse (Rabbi et al., 2017), to disease management (Hamine et al., 2015) to physical inactivity (Consolvo et al., 2008). For example, to help users increase their physical activity, an mHealth application might send a walking suggestions at times and in locations when a user is likely to be able to pursue the suggestions. The promise of mHealth hinges on the ability to provide interventions at times when users need the support and are receptive to it (Nahum-Shani et al., 2017). Consequently, in developing reinforcement learning (RL) algorithms for mHealth our goal is to be able to learn an optimal policy of when and how to intervene for a given user and context.