Last month, Medgadget announced the development of the Magnifi iPhone adapter from start-up Arcturus Labs (Palo Alto, CA), which connects your iPhone 4 or 4S to optical instruments ranging from microscopes to binoculars and telescopes.

To learn more about the evolution of the Magnifi, we spoke with newlyweds Xianne and Isaac Penny who came up with the rough idea for the device while in grad school at Stanford University. They began the development of the product after graduating. Isaac Penny also worked as an engineer at Intuitive Surgical (Sunnyvale, CA), where he helped create the daVinci Single-Site line of instruments.

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The eye has its own connectome, the neuronal network of the retina that processes the incoming signals before it sends them off to the brain. Much of its structure is yet unknown, and now researchers are looking into the powers of crowdsourcing to process the vast amounts of data they have acquired on the structure. Eyewire, developed by neuroscientists at MIT, comprises a game that requires the players to connect the neurons in a small piece of the retina.

The data for analysis consists of a retinal volume with a size of 350×300×60 μm3 that was imaged using serial electron microscopy at the Max Planck Institute for Medical Research in Heidelberg, Germany. In total it amounts to about one terabyte of data. Although the analysis of these images to find connectomes can be automated to some extent, a lot of it is still manual work and this can be very time-consuming. By incorporating a game-like element and engaging a crowd from all over the world the researchers hope to speed up their analyses.

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Illumina has launched the MyGenome iPad app, an app which visualizes the human genome. It allows you to explore a real human genome and view reports about important genetic variations. In its current iteration it acts mainly as an educational tool, however in future version Illumina foresees it becoming a clinical tool for use by physicians with their patients to improve communication of genetic data.

The MyGenome app features an actual genome, a genome Map, health cards and reports and a video journey into the genome. The included genome belongs to Illumina’s own CEO Jay Flatley. More interestingly however, in future versions it will be possible to explore your own genome after you have had your genome sequenced by Illumina’s Individual Genome Sequencing service. Illumina plans to deliver genetic data first to the ordering physician via the app, providing direct access to the consumer only after the doctor has discussed the result with the consumer. The app is available for $0.99 from the Apple app store.

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Toshiba has received FDA clearance for its Adaptive Iterative Dose Reduction 3D (AIDR 3D) technology. This is Toshiba’s third generation iterative dose reconstruction software that uses an algorithm designed to work in both the raw data and image data space, reducing noise while attempting to maintain image quality.

AIDR promises to reduce image noise and improve spatial resolution while lowering radiation exposure. AIDR 3D processing uses a scanner model and a statistical noise model considering both photon and electronic noise to eliminate noise. When AIDR is enabled in the scan protocol, the calculated exposure is automatically reduced by up to 75% when compared to a scan performed with traditional filtered back projection (FBP) reconstruction.

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We have been following the Quantified Self movement closely over the last number of years as a medical/wellness technology related trend that may someday hook into more personalized delivery of healthcare. The movement has been enabled by the wide range of data logging consumer and medical devices and sensors, which allow people to accumulate and analyze data from many aspects of their lives from productivity to sleep patterns.

The team over at the Quantified Self Blog have been a large force in promoting this movement and have just released a great video presentation from a medical self-tracker, Dr. Mark Drangsholt. Mark teaches evidence-based medicine at the University of Washington and talks through a number of self tracking experiments he carried out to get a handle on his heart condition. What is particularly interesting about this talk is how Mark describes the quantified self movement in the context of evidence based medicine and clinical decision making.

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IBM Research – Haifa, Israel has developed a new clinical decision support tool that correlates a patients’ unique disease profile against various clinical guidelines and a wide range of previously acquired clinical data from a multitude of patients. The tool, called Clinical Genomics (Cli-G), is designed to provide clinicians with actionable results that outline how to address individual patients’ conditions.

The system is currently being tested at the Fondazione IRCCS Istituto Nazionale dei Tumori, a research and cancer treatment center in Italy.

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Last month HP announced that Central Logic Core, a web-based bed management system developed by Utah-based company Central Logic, Inc., will be integrated and sold exclusively with HP hardware, including HP Z Workstations and HP touchscreen monitors, to help hospitals “optimize patient care by providing real-time end-to-end patient flow information.” Medgadget had a chance to ask Central Logic’s senior product manager Matt Meservey a few questions about the system and the new partnership:

Medgadget: Can you tell us how hospitals might benefit from HP Z600 Workstations integrated with Central Logic Core web-based patient management system?

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There are many ways to skin a cat. There are also many ways to cause terrible mutations. These range from point mutations and InDels (insertions and deletions) in DNA to splicing changes in mRNA, which account for about 10 percent of the mutations in the Human Gene Mutation Database. Now, a team from Brown University has released a free, online program that will allow researchers to predict whether genetic mutations are likely to disrupt mRNA splicing and lead to disease.

Called Spliceman, the program has successfully predicted the known effects of many mutations and shown that “perhaps as many as a third of the disease-causing mutations in the Human Genome Mutation Database do so by causing errant gene splicing.” The press release describes the theory of splicing mutations and foundation for how Spliceman works:

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“Big data” is the phrase that has the high-tech and healthcare industries buzzing. The ability to collect and intelligently analyze patient data, from their DNA sequence to their vital signs, promises to revolutionize both the delivery of healthcare and understanding of the human body. Aggregating information from millions of patients may greatly accelerate population health discoveries as well as guide individual treatment decisions. This was one of the more exciting applications of IBM’s Watson, but there is still a long way to go before the powerful machine is unleashed on the healthcare system, let alone the general public.

Symcat is a versatile and also very powerful tech solution that combines aggregated data from patient health records with user symptoms and demographics to inform diagnoses. The platform, which is already accessible online, is being developed by two medical student entrepreneurs from Johns Hopkins, Craig Monsen and David Do, and is part of the first class of Blueprint Health. This editor met up with Symcat’s founders to discuss the platform and its future:

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Researchers at the Minneapolis Heart Institute at Abbott Northwestern Hospital in Minneapolis, Minn. have reported that commercially available implantable cardioverter-defibrillator (ICD) monitoring software could identify problems with ICDs earlier than current monitoring practices.

Modern ICD devices are capable of sophisticated sensing and data logging techniques which aim to both optimize therapies and monitor device performance. This logged data can easily be read by a clinician in an outpatient setting using existing ICD monitoring software.  The study, which was published in Circulation: Cardiovascular Quality and Outcomes, aimed to identify whether this existing data could be used to predict device failures at an earlier stage.

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