Catching abnormalities on a medical image is important, but case backlogs often mean radiologists are cut short on how long they can spend with each one. Enter Aidoc, a 4-year-old Israel-based startup providing artificial intelligence tools for radiologists. The company secured an additional $20 million for its Series B funding led by Square Peg Capital, which initially led the round that began in April 2019. The new funds bring the Series B round to $47 million and gives Aidoc a total of $60 million raised to date, according to Crunchbase data. If the AI detects something, the tools alert the radiologist, Aidoc co-founder and CEO Elad Walach told Crunchbase News. "What has happened in recent history is that scanners have become cheaper, so now there is more imaging, which is overloading a radiologist's workflow," he said.
Research on artificial intelligence (AI), and particularly the advances in machine learning (ML) and deep learning (DL)1 have led to disruptive innovations in radiology, pathology, genomics and other fields. Modern DL models feature millions of parameters that need to be learned from sufficiently large curated data sets in order to achieve clinical-grade accuracy, while being safe, fair, equitable and generalising well to unseen data2,3,4,5. For example, training an AI-based tumour detector requires a large database encompassing the full spectrum of possible anatomies, pathologies, and input data types. Data like this is hard to obtain, because health data is highly sensitive and its usage is tightly regulated6. Even if data anonymisation could bypass these limitations, it is now well understood that removing metadata such as patient name or date of birth is often not enough to preserve privacy7.
GAN or Generative Adversarial Network is one of the most fascinating inventions in the field of AI. All the amazing news articles we come across every day, related to machines achieving splendid human-like tasks, are mostly the work of GANs! For instance, if you ever heard of AI bots which create human-like paintings, it is essentially GANs behind the awe-inspiring strokes. Or if you have heard of AI bots which create human faces from scratch, faces which do not even exist yet, that too is entirely the imaginative work of powerful GANs. GANs have a lot of applications and one is often led to wonder how simple machines can achieve such fascinating and in fact, extensively creative accomplishments so efficiently. If you are an observer of the real world, you might have noticed that an individual, whether it be an individual from the animal or plant kingdom, often grows stronger when it faces any sort of competition.
Weekly outpatient MRI appointment no-show rates for 1 year before (19.3%) and 6 months after (15.9%) implementation of intervention measures in March 2019, as guided by XGBoost prediction model. September 10, 2020 -- According to ARRS' American Journal of Roentgenology (AJR), artificial intelligence (AI) predictive analytics performed moderately well in solving complex multifactorial operational problems -- outpatient MRI appointment no-shows, especially -- using a modest amount of data and basic feature engineering. "Such data may be readily retrievable from frontline information technology systems commonly used in most hospital radiology departments, and they can be readily incorporated into routine workflow practice to improve the efficiency and quality of health care delivery," wrote lead author Le Roy Chong of Singapore's Changi General Hospital. To train and validate their model, Chong and colleagues extracted records of 32,957 outpatient MRI appointments scheduled between January 2016 and December 2018 from their institution's radiology information system, while acquiring a further holdout test set of 1,080 records from January 2019. Overall, the no-show rate was 17.4%.
Andy OramFans of data in health care often speculate about what clinicians and researchers could achieve by reducing friction in data sharing. What if we had easy access to group repositories, expert annotations and labels, robust and consistent metadata, and standards without inconsistencies? Since 2017, the Radiological Society of North America (RSNA) has been displaying a model for such data sharing. That year marked RSNA's first AI challenge. RSNA has worked since then to make the AI challenge an increasingly international collaboration.
Dutch women's imaging software developer ScreenPoint Medical has installed its artificial intelligence (AI) software to improve early detection and treatment of breast cancer at Florida's Diagnostic Centers of America and Boca Radiology Group. The AI clinical decision-support software Transpara is available for regular 2D mammography as well as 3D mammography, also known as digital breast tomosynthesis (DBT). Diagnostic Centers of America is an outpatient imaging center with eight locations in Palm Beach County, FL. Boca Radiology Group has one location in Boca Raton, FL, with more than 35 radiologists.
Machine learning has suddenly grabbed attention of the tech crowd, much credit goes to OpenAI's GPT-3 that can even automate creative writing! Such is the untapped potential of machine learning that is eyeing enterprise's eyeballs and their investments! Machine learning or ML in short has applications in real life so common that we often tend to overlook! From opening your phone by facial recognition to the more complex recommender algorithms that influences your decision what you would watch or shop next, machine learning is making quite a noise for now. ML is defined as making machines learn to initiate human actions, through complex coding initiated in Python, R, C, C#, Java and so on.
AI has arrived, with the potential for enormous change in the delivery of health care, but are we ready? Artificial intelligence (AI) is the trigger for the next great transformation of society: the fifth Industrial Revolution. AI has already arrived in health care, but are we ready for the kind of changes that it will introduce? In this article, we map out the current areas where AI has begun to permeate and make predictions about the kind of changes it will make to health care. AI comprises any digital system "that mimics human reasoning capabilities, including pattern recognition, abstract reasoning and planning".1 It includes the concept of machine learning, where machines are able to learn from experience in ways that mimic human behaviour, but with the ability to assimilate much more data and with potential for greater accuracy and speed.
In the following sections, I will elaborate on how we rapidly built a COVID-19 solution using deep learning tools. The ideas and methods presented here can be used for any new virus or disease with imaging features in CT, especially in the initial phase when data is almost not available. CT scan include a series of slices (for those who are not familiar with CT read short explanation below). Since we had a very limited number of COVID-19 patient's scans, we decided to use 2D slices instead of 3D volume of each scan. This allowed us to multiple our data set and to overcome the first obstacle of a small dataset.
MR imaging is a powerful and diverse imaging technique employed to investigate and diagnose a range of diseases in different body areas. MRI scans are acquired by employing specific parameters in a "sequence" to encode in data in arbitrary space known as "k-space". Image is reconstructed by applying mathematical transforms (mainly Fourier) to the k-space data. To obtain images of particular contrast (T1w, T2w, T2* etc) optimal sequence settings must be employed. Briefly, image contrast arise from magnetic property of the hydrogen atoms, that can varied setting such as echo time and Tr in sequence setting.