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FOSS in Healthcare: NYU, NHSX, Visikol, Clubfoot Brace, Optical Cardiography

Submitted by Roy Schestowitz on Sunday 24th of March 2019 12:13:17 PM Filed under
OSS
  • NYU open-sources breast cancer screening model trained on over 200,000 mammography exams

    Breast cancer is the second leading cancer-related cause of death among women in the U.S. It’s estimated that in 2015, 232,000 women were diagnosed with the disease and approximately 40,000 died from it. And while diagnostic exams like mammography have come into wide practice — in 2014, over 39 million breast cancer screenings were performed in the U.S. alone — they’re not always reliable. About 10 to 15 percent of women who undergo a mammogram are asked to return following an inconclusive analysis.

    That’s why researchers at New York University are investigating an AI-driven technique that promises much higher precision than today’s tests. In a newly published paper on Arxiv.org (“Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening“), they describe a deep convolutional neural network — a class of machine learning algorithm commonly used in image classification — that notches an area under the ROC curve (AUC) of 0.895 in predicting the presence of a cancerous breast tumor. Moreover, they claim that when averaged with the probability of malignancy predicted by a radiologist from the AI system’s results, the AUC is higher than either method achieves separately.

    [...]

    The team began by sourcing a data set comprising 229,426 digital screening mammography exams (1,001,093 images) from 141,473 patients, each of which contained at least four images corresponding to the four views typically used in mammography screenings (right craniocaudal, left craniocaudal, right mediolateral oblique, and left mediolateral oblique). They extracted labels from 5,832 exams with at least one biopsy performed within 120 days of the screening mammogram, and then recruited a team of radiologists — all of whom were provided supporting pathology reports — to indicate where the biopsies were taken “at the pixel level.”

  • Will this new openness to open source heed past lessons?

    NHSX has listed open source as one of its priorities. For Ewan Davis, there is a keen sense of déjà vu – and a desperate desire for the many lessons of the past to be heeded.

    Open source seems to be back on the NHS agenda, courtesy of NHSX. The body lists one of its responsibilities as: “Making sure that all source code is open by default so that anyone who wants to write code for the NHS can see what we need.”

    As a new organisation NHSX has no corporate memory and so I thought it might be helpful to share my observations on what happened last time the NHS got excited about open source.

    It started when Tim Kelsey (peace be upon him) – then NHS England national director for patients and information – went on a trip to the USA. He saw the open source EHR VistA operating in the US Veterans Administration, decided it would be good for the NHS, and wrote it into Tech Fund 1 like Brighton through a stick of rock.

    I got involved, working for NHS England with guys from World VistA and OSERA to establish what would be needed to localise VistA for the NHS. We came up with a plan but the cost was more than NHS England was willing to pay. NHS VistA was doomed.

    Our work did lead to the creation of an open source programme within NHS England, though, which rapidly absorbed and refactored Tim’s next idea: Code4Health.

  • Visikol Releases Open Source 3Screen™ Python Library for Training Convolutional Neural Networks for Use in Digital Pathology

    Over the last two years, Visikol has leveraged its internal 3Screen™ image analysis software to provide its pharmaceutical Clients with unprecedented insights from their tissues. As a leader in the image analysis space, Visikol not only provides best-in-class services to its Clients, but also periodically makes useful 3Screen™ tools available to the academic community. To help the academic community with more easily adopting neural networks in image analysis, Visikol is launching a Python library to assist with training.

  • Cost Sensitivity Analysis Performed for 3D Printed, Open Source Infant Clubfoot Brace

    Congential talipes equinovarus (CTEV), perhaps better known as clubfoot, is one of the most common congenital physical deformities, as it occurs at least once every 1,000 births. In countries like the US, CTEV is diagnosed at birth and treated while the patient is still a young child, using a method of weekly manipulation and casting, followed by an Achilles tenotomy. Then a foot abduction orthosis (FAO) is worn until the patient is about four years old so that the deformity will not reoccur. Unfortunately, these types of treatment options are not as readily available, or affordable, in developing countries like East Africa, where clubfoot can occur up to eight times every 1,000 births.

  • Open-source solution: Researchers 3D-print system for optical cardiography
  • Open-source solution for multiparametric optical mapping of the heart's electrical activity

    An international research team from the George Washington University, U.S., and the Moscow Institute of Physics and Technology, Russia, has developed an open-source solution for multiparametric optical mapping of the heart's electrical activity. The technique involves monitoring multiple parameters at once -- for example, both electrical excitation and the changes in the intracellular calcium concentration. This technique is a useful tool for enhancing our understanding of the mechanisms behind cardiac arrhythmias. The 3D models of the mapping system components and the source code for data analysis are openly available, enabling other research groups to benefit from the new solution. The study was published in Scientific Reports.

  • Open-source solution: Researchers 3D-print system for optical cardiography
»

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