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Top 10 Applications of Machine Learning in Healthcare - FWS

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Healthcare is an important industry which offers value-based care to millions of people, while at the same time becoming top revenue earners for many countries. Today, the Healthcare industry in the US alone earns a revenue of $1.668 trillion. The US also spends more on healthcare per capita as compared to most other developed or developing nations. Quality, Value, and Outcome are three buzzwords that always accompany healthcare and promise a lot, and today, healthcare specialists and stakeholders around the globe are looking for innovative ways to deliver on this promise. Technology-enabled smart healthcare is no longer a flight of fancy, as Internet-connected medical devices are holding the health system as we know it together from falling apart under the population burden.


The Future of AI Part 1

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It was reported that Venture Capital investments into AI related startups made a significant increase in 2018, jumping by 72% compared to 2017, with 466 startups funded from 533 in 2017. PWC moneytree report stated that that seed-stage deal activity in the US among AI-related companies rose to 28% in the fourth-quarter of 2018, compared to 24% in the three months prior, while expansion-stage deal activity jumped to 32%, from 23%. There will be an increasing international rivalry over the global leadership of AI. President Putin of Russia was quoted as saying that "the nation that leads in AI will be the ruler of the world". Billionaire Mark Cuban was reported in CNBC as stating that "the world's first trillionaire would be an AI entrepreneur".


Medical Startup Curai Offers AI-Based Telehealth

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As a child, Neal Khosla became engrossed by the Oakland Athletics baseball team's "Moneyball" approach of using data analytics to uncover the value and potential of the sport's players. A few years ago, the young engineer began pursuing similar techniques to improve medical decision-making. It wasn't long after Khosla met Xavier Amatriain, who was looking to apply his engineering skills to a higher mission, that the pair founded Curai. The three-year-old startup, based in Palo Alto, Calif., is using AI to improve the entire process of providing healthcare. The scope of their challenge -- transforming how medical care is accessed and delivered -- is daunting.


Deep learning (AI) - enhancing automated inspection of medical devices?

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Earl Yardley, director at Industrial Vision Systems, writes about the recent impact of automation on the factory floor. Integrated quality inspection processes continue to make a significant contribution to medical device manufacturing production, including the provision of automated inspection capabilities as part of real-time quality control procedures. Long before COVID-19, medical device manufacturers were rapidly transforming their factory floors by leveraging technologies such as artificial intelligence (AI), machine vision, robotics, and deep learning. These investments have enabled them to continue to produce critical and high-demand products during these current times, even ramping up production to help address the pandemic. Medical device manufacturers must be lean, with high-speeds, and an ability to switch product variants quickly and easily, all validated to'Good Automated Manufacturing Practice' (GAMP).


Future Of Healthcare Through Deep Learning & 3D-Printed Organoids

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Organoids 3D printing has quickly become one of the leading segments of the 3D printing industry in terms of innovation. Until recently, the market was primarily focused on North America, however many companies, laboratories, and universities around the world are exploring this field as well. Thanks to 3D printing techniques, cells and biomaterials can be combined and deposited layer by layer to create biomedical developments that have the same properties as living tissues. During this process, various bio-links can be used to create these tissue-like structures, which have applications in the fields of medical and tissue engineering. Of course, it is more than knowing that the goal of all these developments is to successfully bioprint a fully functional human organ.


Machine learning, meet human emotions: How to help a computer monitor your mental state

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Researchers from Skoltech, INRIA and the RIKEN Advanced Intelligence Project have considered several state-of-the-art machine learning algorithms for the challenging tasks of determining the mental workload and affective states of a human brain. Their software can help design smarter brain-computer interfaces for applications in medicine and beyond. The paper was published in the IEEE Systems, Man, and Cybernetics Magazine. A brain-computer interface, or BCI, is a link between a human brain and a machine that can allow users to control various devices, such as robot arms or a wheelchair, by brain activity only (these are called active BCIs) or can monitor the mental state or emotions of a user and categorize them (these are passive BCIs). Brain signals in a BCI are usually measured by electroencephalography, a typically noninvasive method of recording electrical activity of the brain.


MLCN 2020: Accepted Papers

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First, the very high variability in the morphology of the tissues can be incompatible with the prior knowledge embedded within the algorithms. Second, the availability of MR images of distorted brains is very scarce, so the methods in the literature have not addressed such cases so far. In this work, we present the first evaluation of state-of-the-art automatic tissue segmentation pipelines on T1-weighted images of brains with different severity of congenital or acquired brain distortion. We compare traditional pipelines and a deep learning model, i.e. a 3D U-Net trained on normal-appearing brains. Unsurprisingly, traditional pipelines completely fail to segment the tissues with strong anatomical distortion. Surprisingly, the 3D U-Net provides useful segmentations that can be a valuable starting point for manual refinement by experts/neuroradiologists.


A.I. Can Tell Good Surgeons Just By Scanning Their Brains

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Could a brain scan be the best way to tell a top-notch surgeon? Researchers at Rensselaer Polytechnic Institute and the University at Buffalo have developed Brain-NET, a deep learning A.I. tool that can accurately predict a surgeon's certification scores based on their neuroimaging data. This certification score, known as the Fundamentals of Laparoscopic Surgery program (FLS), is currently calculated manually using a formula that is extremely time and labor-consuming. The idea behind it is to give an objective assessment of surgical skills, thereby demonstrating effective training. "The Fundamental of Laparoscopic Surgery program has been adopted nationally for surgical residents, fellows and practicing physicians to learn and practice laparoscopic skills to have the opportunity to definitely measure and document those skills," Xavier Intes, a professor of biomedical engineering at Rensselaer, told Digital Trends.


AI nurturing Healthcare: Big Data Computing and TeleHealth

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AI is an enabler in transforming healthcare delivery in terms of treatment modalities and their outcomes, electronic health records-based prediction, diagnosis and prognosis and precision medicine. This course will introduce you to the cutting edge advances in AI concerning healthcare by exploiting deep learning architectures. The course aims to provide students from diverse backgrounds with both conceptual understanding and technical grounding of leading research on AI in healthcare.


Hyun Kim, CEO and Co-Founder, Superb AI – Interview Series

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Huyn Kim is the CEO and Co-Founder of Superb AI, a company that provides a new generation machine learning data platform to AI teams so that they can build better AI in less time. The Superb AI Suite is an enterprise SaaS platform built to help ML engineers, product teams, researchers and data annotators create efficient training data workflows. What initially attracted you to the field of AI, Data Science and Robotics? As an undergraduate majoring in Biomedical Engineering at Duke, I was passionate about genetics and how we can engineer our DNA to cure diseases or create genetically engineered organisms. I remember one wet-lab experiment distinctly that kept failing for like 6 months straight. The most frustrating part of it was that there was a lot of repetitive manual work, and in hindsight that was probably the root of some many potential errors.