The advancements of Artificial Intelligence have startled the commoners with its high-tech abilities and the abilities to do things which human usually cannot do. From e-commerce to healthcare its capabilities are reshaping various business operations for the greater good. Owing to its increasing fame, many companies and startups are adopting AI capabilities to enhance efficiency, effectiveness, and productivity. Moreover, many investors are also keening looking to invest their sums in Artificial Intelligence realizing the worth of it. Many AI companies have risen from nowhere to everywhere across the market and the most obvious reason behind their growth is the hefty investments and finances they gain from VC firms and investors.
Recently, I had an opportunity to speak with Martin Dubuc, who leads Biogen Digital Health, and his colleague, Belgian neurologist Dr. Shibeshih Belachew, who leads Biogen Digital Health Sciences, to gain additional insights for the rapid evolution of digital technologies – particularly related to neuroscience. According to Belachew and Dubuc, neurological conditions, like Alzheimer's disease, Parkinson's disease, or amyotrophic lateral sclerosis (Lou Gehrig's disease), provide an interesting case study to this effect. For far too long, these debilitating disease areas have been difficult to diagnose and monitor. Despite the ultimately life-threatening nature of these conditions, the typical markers used to diagnose and measure them are often subjective and infrequent – leaving neurologists unable to pick up small changes that could help them to treat individuals sooner.
A team of researchers are developing the use of an artificial intelligence (AI) algorithm with the aim of diagnosing deep vein thrombosis (DVT) more quickly and as effectively as traditional radiologist-interpreted diagnostic scans, potentially cutting down long patient waiting lists and avoiding patients unnecessarily receiving drugs to treat DVT when they don't have it. DVT is a type of blood clot most commonly formed in the leg, causing swelling, pain and discomfort--if left untreated, it can lead to fatal blood clots in the lungs. Researchers at Oxford University, Imperial College and the University of Sheffield collaborated with the tech company ThinkSono (which is led by Fouad Al-Noor and Sven Mischkewitz), to train a machine learning AI algorithm (AutoDVT) to distinguish patients who had DVT from those without DVT. The AI algorithm accurately diagnosed DVT when compared to the gold standard ultrasound scan, and the team worked out that using the algorithm could potentially save health services $150 per examination. "Traditionally, DVT diagnoses need a specialist ultrasound scan performed by a trained radiographer, and we have found that the preliminary data using the AI algorithm coupled to a hand-held ultrasound machine shows promising results," said study lead Dr. Nicola Curry, a researcher at Oxford University's Radcliffe Department of Medicine and clinician at Oxford University Hospitals NHS Foundation Trust.
Dementias are characterised by the build-up of different types of protein in the brain, which damages brain tissue and leads to cognitive decline. In the case of Alzheimer's disease, these proteins include beta-amyloid, which forms'plaques', clumping together between neurons and affecting their function, and tau, which accumulates inside neurons. Molecular and cellular changes to the brain usually begin many years before any symptoms occur. Diagnosing dementia can take many months or even years. It typically requires two or three hospital visits and can involve a range of CT, PET and MRI scans as well as invasive lumber punctures.
Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. This database is also available through the UW CS ftp server: ftp ftp.cs.wisc.edu Also can be found on UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/Breast The mean, standard error and "worst" or largest (mean of the three largest values) of these features were computed for each image, resulting in 30 features.
Praduman Jain is CEO and founder of Vibrent Health, a digital health technology company powering the future of precision medicine. There has been quite a bit of hype over the last several years about how artificial intelligence (AI) would transform health care. Translating the predictive power of AI algorithms into research methods and clinical practice, however, has proved challenging, which inevitably leads to disillusionment. But rather than getting frustrated with AI and machine learning, I would argue that strategic and ethical deployment of artificial intelligence will, by necessity, be central to the success of precision health research over the next decade. Several factors are coming together to make AI more critical to progress.
Life during the Covid-19 pandemic would be even more difficult without the Internet and automation, making this year timely for the upcoming "Dune" film to portray a distant future where humanity is devastated by our dependence on machines. Director Dennis Villeneuve is set to release his adaptation of the science fiction epic on October 22, both on HBO Max and in movie theaters. The release was delayed from last December to make it safer for people to view and hear the space fantasy in theaters. WarnerMedia aims to distribute Villeneuve's vision of the first "Dune" novel in two films, but has not yet scheduled a release date for the second film after the first half is released. Frank Herbert's "Dune" novel begins in the far distant future, thousands of years after humans were enslaved by robots, fought a revolutionary crusade and banned artificial intelligence with a new anti-tech religion.
The term AI means a lot of things to many people. Unfortunately, most of them are bad. Pop culture has almost always portrayed AI as something bad, something that will spell the end of humanity if it's not kept in check. But there is one good thing about it that people might not be too aware of: its potential in modernizing healthcare. And the world has seen what artificial intelligence can really do in the medical field, with experts believing that artificial intelligence in healthcare will grow at an almost 50% rate between 2017 and 2023, according to Business Insider.
The Technische Universität (TUU) KIWI biolab, which has been designated as one of three international artificial intelligence (AI) future laboratories by the German government, uses AI to design experiments with the aim of understanding how cells behave. "We cultivate various clones in parallel, and computer-controlled robots perform the fed-batch experiments and analyses automatically," explains Peter Neubauer, PhD, who heads the department of bioprocess engineering at TU Berlin. Experimental data is used to create "digital twins" of the cells that can be used for computer-based process development, he says. Neubauer developed the automated laboratory to multiply the number of cell lines he could analyze in parallel. "Currently in our facility we can do this for a large number of cells–for 48 different clones," he adds.
Whenever a patient has symptoms of cancer, the cancer tumour is taken out and sequenced. Genetic information in the tumor cell is stored in the form of DNA. It is then transcribed to form RNA which is then translated to form proteins/amino acids. In case of a mutation, or a mistake in DNA sequence, the resultant amino acid is affected giving rise to a variation for the particular gene. Thousands of genetic mutations may be present in the sequence. We need to distinguish the malignant mutations (drivers leading to tumour growth) from the benign (passenger) ones.