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Algorithms begin to show practical use in diagnostic imaging

From www.healthdatamanagement.com - October 18, 2018 11:25 PM

Algorithms based on machine learning and deep learning, intended for use in diagnostic imaging, are moving into the commercial pipeline.

However, providers will have to overcome multiple challenges to incorporate these tools into daily clinical workflows in radiology.

There now are numerous algorithms in various stages of development and in the FDA approval process, and experts believe that there could eventually be hundreds or even thousands of AI-based apps to improve the quality and efficiency of radiology.

The emerging applications based on machine learning and deep learning primarily involve algorithms to automate such processes in radiology as detecting abnormal structures in images, such as cancerous lesions and nodules. The technology can be used on a variety of modalities, such as CT scans and X-rays. The goal is to help radiologists more effectively detect and track the progression of diseases, giving them tools to enhance speed and accuracy, thus improving quality and reducing costs.

While the number of organizations incorporating these products into daily workflows is small today, experts expect many providers to adopt these solutions as the industry overcomes implementation challenges.

Data dump
Radiologists’ growing appreciation for AI may result from the technology’s promise to help the profession cope with an explosion in the amount of data for each patient case.

Radiologists also are grappling with the growth in data from sources outside radiology, such as lab tests or electronic medical records. This is another area where AI could help radiologists by analyzing data from disparate sources and pulling out key pieces of information for each case,.

There are other issues that AI could address as well, such as “observer fatigue,” which is an “aspect of radiology practice and a particular issue in screening examinations where the likelihood of finding a true positive is low,” wrote researchers from Massachusetts General Hospital and Harvard Medical School in a 2018 article in the Journal of the American College of Radiology.

These researchers foresee the utility of an AI program that could identify cases from routine screening exams with a likely positive result and prioritize those cases for radiologists’ attention.

AI software also could help radiologists improve worklists of cases in which referring physicians already suspect that a medical problem exists.

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DeepMind furthering AI cancer research program with new partnership in Japan to refine breast cancer detection algorithms

From venturebeat.com - October 5, 2018 5:15 AM

Deep mind will use data available to it via a new partnership with Jikei University Hospital in Japan to refine its artificially intelligent (AI) breast cancer detection algorithms.

Google AI subsidiary DeepMind has partnered with Jikei University Hospital in Japan to analyze mammagrophy scans from 30,000 women.

DeepMind is furthering its cancer research efforts with a newly announced partnership.

The London-based Google subsidiary said it has been given access to mammograms from roughly 30,000 women that were taken at Jikei University Hospital in Tokyo, Japan between 2007 and 2018.

Deep mind will use that data to refine its artificially intelligent (AI) breast cancer detection algorithms.

Over the course of the next five years, DeepMind researchers will review the 30,000 images, along with 3,500 images from magnetic resonance imaging (MRI) scans and historical mammograms provided by the U.K.’s Optimam (an image database of over 80,000 scans extracted from the NHS’ National Breast Screening System), to investigate whether its AI systems can accurately spot signs of cancerous tissue.

read the original and complete story at https://venturebeat.com/2018/10/04/deepmind-expands-ai-cancer-research-program-to-japan/

nrip's insight:

Healthcare data is increasingly being analyzed and complex algorithms created to help various aspects of the healthcare ecosystem.

A big problem is the availability of huge data sets, and where available, the prevention of their misuse. Its great that Deepmind is able to source data sets , (being a sub of Google, am sure plays a role), and hopefully they will put their deep mind ;) to good use and be able to improve detection algorithms.

I have written previously on this, and it will be useful for patients and if the data sets do help create both faster as well as more accurate detection algorithms in the future.

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Tencent partners with Medopad to improve Parkinson's disease treatment with AI

From venturebeat.com - October 9, 2018 7:22 AM

Roughly 600,000 people in the U.S. are diagnosed with Parkinson’s every year, contributing to the more than 10 million people worldwide already living with the neurodegenerative disease. Early detection can result in significantly better treatment outcomes, but it’s notoriously difficult to test for Parkinson’s.

Tencent and health care firm Medopad have committed to trialing systems that tap artificial intelligence (AI) to improve diagnostic accuracy. They announced a collaboration with the Parkinson’s Center of Excellence at King’s College Hospital in London to develop software that can detect signs of Parkinson’s within minutes. (Currently, motor function assessments take about half an hour.)

This technology can help promote early diagnosis of Parkinson’s disease, screening, and daily evaluations of key functions.

Medopad’s tech, which uses a smartphone camera to monitor patients’ fine motor movements, is one of several apps and wearables the seven-year-old U.K. startup is actively developing.

It instructs patients to open and close a fist while it measures the amplitude and frequency of their finger movements, which the app converts into a graph for clinicians. The goal is to eventually, with the help of AI, teach the system to calculate a symptom severity score automatically.

Tencent and Medopad are far from the only firms applying AI to health care. Just last week, Google subsidiary DeepMind announced that it would use mammograms from Jikei University Hospital in Tokyo, Japan to refine its AI breast cancer detection algorithms. And last month Nvidia unveiled an AI system that generates synthetic scans of brain cancer

read the original story at https://venturebeat.com/2018/10/08/tencent-partners-with-medopad-to-improve-parkinsons-disease-treatment-with-ai/

nrip's insight:

Healthcare data is increasingly being analyzed and complex algorithms created to help various aspects of the healthcare ecosystem.

A big problem is the availability of huge data sets, and where available, the prevention of their misuse. Its promising that Tencent has already been working on computer vision software that can diagnose skin cancer from pictures taken with a phone, and its AIMIS system already has the capability to detect esophageal cancer, lung sarcoidosis, and diabetic retinopathy from medical images

I have written previously on this, and it will be useful for patients and if the data sets do help create both faster as well as more accurate detection algorithms in the future.

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Comparing medical images better

From www.cwi.nl - July 17, 2017 11:10 PM

Combining multiple medical images from one patient can provide important information. This is not always easy to do with the naked eye. This is why we need software that can compare different medical images. To do so, so-called image registration methods are used, which basically compute which point in one image corresponds to which point in another image.

Current solutions are often not always suitable for use in a medical setting, which is why AMC and CWI together with companies Elekta and Xomnia will develop a new image registration method.

Suppose you have multiple CT and/or MRI images of a patient, made at different points in time. Medical staff wants to compare these images, for example to see how certain irregularities have developed over time. But these images are often fundamentally different (e.g., patients never lie in a scanner in the exact same manner) and when different imaging methods are used this is even more complicated.

So how can one determine precisely what has changed?

With the software that is currently available this can be very hard, or even impossible, to accomplish in practice.

This project has 2 major challenges. The models and algorithms for large deviations have to be improved. Next to that, the software has to be designed so that it is intuitive to use and helps medical practitioners get the results they want. By combining new deformable image registration models and algorithms with machine learning, the software can be trained on example cases to work even better. The focus of the project will be on supporting better radiotherapy treatment, with validations in the real world (i.e., the clinic), but the method will also be applicable to other (medical) areas.

more at https://www.cwi.nl/news/2017/comparing-medical-images-better

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