Monday, April 10, 2017

How to Sell Machine Learning Algorithms to Healthcare Providers

The explosion of the computer aided diagnosis techniques is a reality in our days. There are thousands of new publications every year about new automatic measurements that turn into bio-markers. 

Image result for medical imaging
As we said in From Lab to Specialist (here), the challenge is put that technology in the medical specialist hands. Definitively they need to get involved because they know the science and the most important thing, the real state of the patient (for example, there is pain?) 

How can we continue developments and also help to have better treatments? If the technology is ready for validate drugs why it's not being used? I there is an FDA approval  or CE marked app, why is not used to choose the best treatment for the patient?

Image result for image biomarkers
Medimsight was created exactly for that, while others were working on exclusive plugins with expensive license models, Medimsight defined an open solution with a pay per use model so even a teenager in the basement and a senior designer in a big company, they have the same change. We want to stay in challenges, promote research and accelerate certifications. So clinicians get more innovation. It lowers costs. It increases creativity. Everyone can contribute. A catalyst for innovation.

Simon Harris wrote an article about what are the real options out there.

Here is a part of the article
One of the greatest commercial challenges for developers of medical image analysis algorithms is how to take their products to market. Most independent software vendors (ISVs) of image analysis solutions only offer a handful of algorithms for specific use-cases, e.g. coronary calcium scoring, bone age prediction, detection of lung nodules, etc. However, most generalist radiologists require a comprehensive “analytical tool kit” with a broad portfolio of algorithms that can detect a wide range of conditions for multiple body sites and across multiple modalities. Locating, evaluating and sourcing image analysis algorithms on a piecemeal basis from multiple vendors will be a cumbersome and time consuming process for healthcare providers. Not to mention the challenges associated with integrating the algorithms with the providers’ existing healthcare IT infrastructure. Whilst this may be a viable option for the larger academic hospitals and IDNs, most providers will not have the necessary resources for this and instead will prefer to deal with a small number of vendors, and ideally a single supplier.
And what they say about us:
Medimsight offers a cloud-based computer-aided diagnosis marketplace for biomarker quantification. The platform features 39 applications, including algorithms from LAIMBIO, FMRIB (Oxford Centre for Functional MRI of the Brain) and Martinos Center for Biomedical Imaging.  
You can read the whole one in Simon's page: Link 

About Simon HarrisManaging Director at Signify Research.

Wednesday, April 5, 2017

How Computer Vision Is Transforming Medicine

Medimsight It's been highlighted in the list of companies transforming medicine thanks to the use of computer vision techniques.

There is a big number of companies building solutions around computer vision, especially using deep learning. It's been an incredible revolution and we can see that in most of the challenges to get the best biomarker values, deep learning techniques lead in term of results.

Here is a part of the article
Some of the most innovative biotech startups are developing technologies using computer vision to process medical images. These technologies help doctors detect malign changes such as tumors and hardening of the arteries and provide highly accurate measurements of organs and blood flow. With the exponential growth in computer performance which is increasing the speed at which computers process data, computer vision is becoming an increasingly valuable decision making tool in medicine.
You can read the whole one in Margaretta's page: Link 

About Margaretta: Native San Franciscan with 30 years of experience working for software companies in Silicon Valley. She joined 6 early stage tech startups since 1992. She is building an ecosystem of entrepreneurs, engineers, researchers and investors with innovative ideas. Her interests include AI, machine learning, life extension, biotechnology, quantified self and precision medicine. She mentors startup founders in San Francisco, Los Angeles, New York, Boston, Austin, Washington D.C., Albuquerque, Barcelona, Bangkok, Nairobi, Seoul, Bosnia, Uganda, Morocco and The Russian Federation.