Boston University School of Medicine has developed computer models based on artificial intelligence that significantly improve the analysis of routine kidney biopsy images. BU researchers, who conducted a proof-of-principle study on kidney biopsy sections, contend that their AI-based models have both diagnostic and prognostic applications and could lead to the development of software for diagnosing kidney disease as well as predicting kidney survival. In the study, images processed from renal biopsy samples were collected on 171 patients treated at the Boston Medical Center and were analyzed by convolutional neural networks (CNN) models and nephropathologists, who specialize in the analysis of kidney biopsy images. "With respect to kidney disease, biopsy is one of the gold standard procedures," says Vijaya Kolachalama, lead author and assistant professor of medicine at Boston University School of Medicine. "Most of the clinical decisions today are made based on information that nephropathologists can see from the biopsy."
"An important strength of the study is that the machine learning technology was applied to trichrome-stained histologic images of routine kidney biopsy samples without any special processing or manipulation other than digital scanning," the team noted, "which allowed us to directly compare the results of the machine learning analysis with those derived from the clinical pathological report on the same specimens."
Video: DeepMind and healthcare: How is the NHS using artificial intelligence? Back in 2014, Google bought UK artificial intelligence outfit DeepMind for a rumoured £400m. Since then, DeepMind has been expanding its AI capabilities into new areas including gaming and, most notably, healthcare. Google's DeepMind and the NHS: A glimpse of what AI means for the future of healthcare The Google subsidiary has struck a series of deals with organisations in the UK health service -- so what's really happening? The Royal Free, a teaching hospital located in the Hampstead area of London, was one of DeepMind's first healthcare partners.
The authors conducted a prospective trial to assess the feasibility of real time central molecular assessment of kidney transplant biopsy samples from 10 North American or European centers. Biopsy samples taken 1 day to 34 years posttransplantation were stabilized in RNAlater, sent via courier overnight at ambient temperature to the central laboratory, and processed (29 h workflow) using microarrays to assess T cell– and antibody-mediated rejection (TCMR and ABMR, respectively). Of 538 biopsy samples submitted, 519 (96%) were sufficient for microarray analysis (average length, 3 mm). Automated reports were generated without knowledge of histology and HLA antibody, with diagnoses assigned based on Molecular Microscope Diagnostic System (MMDx) classifier algorithms and signed out by one observer. Agreement between MMDx and histology (balanced accuracy) was 77% for TCMR, 77% for ABMR, and 76% for no rejection.
'The idea of understanding a disease from an evolutionary viewpoint to inform drug design still resonates today in how Exscientia is approaching the design of anticancer agents. 'I spent a season at the GlaxoWellcome labs in Stevenage making the compounds I'd designed, and vividly remember the excitement of discovering the first molecule we'd made was active.' These included topics such as the druggable genome, ligand efficiency and network pharmacology – all of which are familiar topics to drug discovery chemists today. An early success involved feeding historical data for the project that discovered erectile dysfunction drug tadalafil (Cialis) into the evolutionary drug design model.
By building a neural network, Google's algorithms can interpret huge amounts of genetic, health, and environmental data to predict a persons health status, such as their level of risk of heart attack (stock image) It was created after Google bought University College London spinout, DeepMind, for £400 million in 2014. By building a neural network, these algorithms can interpret huge amounts of genetic, health, and environmental data to predict a persons health status, such as their level of risk of heart attack. Google announced the first of its NHS collaborations in February 2016, saying it was building an app to help hospital staff monitor patients with kidney disease. ', With personalisation as their ultimate'goal', Google intend to use the machine learning algorithms which track our digital footprint and target users with personalised advertising based on their preferences.
Google's DeepMind, the British artificial intelligence firm behind the human-besting AlphaGo software, launched a healthcare platform in partnership with the U.K.'s Moorfields Eye Hospital and Royal Free London in 2015. Starting this month, doctors and nurses at Musgrove Park will get DeepMind's Streams app for iPhone, which helps spot early signs of acute kidney injury. "This is all about early detection of seriously unwell patients so that we can immediately escalate care, ensure a very rapid response, and make sure they are treated quickly by the right specialist doctor," Luke Gompels, a consultant in medicine at Musgrove Park Hospital, told the BBC. Last year, it acquired Hark, a task management app optimized for hospital environments that was co-developed by students from Imperial College London and the National Institute for Health Research.
A deal between Google's artificial intelligence firm DeepMind and the UK's NHS had serious "inadequacies", an academic paper has suggested. More than a million patient records were shared with DeepMind to build an app to alert doctors about patients at risk of acute kidney injury (AKI). In a statement, the ICO told the BBC: "Our investigation into the sharing of patient information between the Royal Free NHS Trust and Deep Mind is close to conclusion. It revealed that more than 26 doctors and nurses at the Royal Free are now using Streams and that each day it alerts them to 11 patients at risk of AKI.
A former employee of the Japanese unit of Swiss pharmaceutical giant Novartis AG was found not guilty Thursday of exaggerating advertising claims for the blood pressure-lowering drug Diovan. Besides clearing 66-year-old Nobuo Shirahashi of a violation under a pharmaceutical affairs law that bans fraudulent and exaggerated advertising, the Tokyo District Court also found the Tokyo-based sales arm Novartis Pharma K.K. While acknowledging that clinical trial data for the drug were manipulated, presiding Judge Yasuo Tsujikawa determined that the drugmaker's published research paper based on the data was not an advertisement that falls under the purview of the pharmaceutical law. He supplied the research team with manipulated data concerning patients who were not administered the drug.
Google's AI-powered health tech subsidiary, DeepMind Health, is planning to use a new technology loosely based on bitcoin to let hospitals, the NHS and eventually even patients track what happens to personal data in real-time. Dubbed "Verifiable Data Audit", the plan is to create a special digital ledger that automatically records every interaction with patient data in a cryptographically verifiable manner. This means any changes to, or access of, the data would be visible. DeepMind has been working in partnership with London's Royal Free Hospital to develop kidney monitoring software called Streams and has faced criticism from patient groups for what they claim are overly broad data sharing agreements. Critics fear that the data sharing has the potential to give DeepMind, and thus Google, too much power over the NHS.