Credit: Vascular Development Laboratory and EM Unit

A blood test that can detect over 50 cancer types is big news this week.

Theres a lot of excitement around the latest research, published in Annals of Oncology. And its easy to see why.

Scientists have used machine learning to help identify if someone has cancer based on tiny bits of tumour DNA floating in their blood. Which could open the door to a blood test that can detect and identify multiple types of cancer.

But its not there yet. And in the blood test buzz, some news articles have missed out crucial details.

The team looked for differences in the DNA shed from cancer cells and healthy cells into the blood.

They focused on differences in a chemical tag that sit on top of DNA in cells, called methyl groups. These groups are usually spread evenly across the DNA in cells, but in cancer cells they tend to cluster at different points. And its this distinction scientists wanted to exploit.

They trained a machine learning algorithm a type of artificial intelligence that pick up patterns and signals to detect differences between methylation patterns in DNA from cancer and non-cancer cells.

The algorithm was trained on 3,052 samples from people with and without cancer from two large databases.

And once the program was fired up and ready to go, the team tested its cancer-spotting ability on a different set of 1,264 samples f, half of which were from people with cancer.

Any test with the goal of being able to detect cancers at their earliest stages in people without symptoms must strike the right balance between picking up cancer (sensitivity) and not giving false positives (specificity). Weve blogged before about what makes a good cancer test, as well as the efforts to develop a cancer blood test.

How do you assess a cancer test?

Researchers look at 3 main things when assessing a new diagnostic test.

Firstly the good news: fewer than 1% of people without cancer were wrongly identified as having the disease. Which is a good sign for the specificity of this test.

And when it came to detecting cancer, across all types of cancer, the test correctly identified the disease in 55% of cases. This is a measure of the tests sensitivity.

But there was a huge variation in sensitivity depending on the type of cancer and how advanced the disease was. The test was better at picking up more advanced cancer, which makes sense more advanced cancers typically shed more DNA into the bloodstream.

If we look at the numbers, across all cancer types the test correctly detected the disease in 93% of those with stage 4 cancer, but only 18% of early, stage 1 cancers.

An important consideration is that the study was only testing if the algorithm could detect cancer in patients who were already known to have cancer. According to the researchers, these figures may change if the test was used on a wider, general population.

Encouragingly for a multicancer test, when the researchers looked at a smaller number of samples to explore if the test helped them identify where the cancer was growing, the algorithm was able to predict the location in 96% of samples, and it was accurate in 93%.

First things first, although the samples numbers are big, they become a lot smaller when you break them down by cancer type and cancer stage. Some cancer types were particularly poorly represented, with only 1 or 2 samples included in the final analysis so theres more work to do there. Based on this, its a bit too soon to say that the test can pick up 50 cancer types.

And if the plan is to use this as a screening tool, then the researchers will need to do more to study people who didnt have symptoms when they were diagnosed. The current study included people who were symptomatic as well as people without symptoms.

And the participant data lacked variation in age, race and ethnicity. Between 83 and 87% of all the samples used to train and test the algorithm were Caucasian.

The big conclusion is that these results are encouraging and should be taken forward into bigger studies. But its important to put the results in context theyre a step in the right direction. There are a lot of steps between this study and a fully-fledged cancer test.

According to the research team, they plan to validate the results using samples from US and UK studies, and well as to begin to examine if the test could be used to screen for cancer. We look forward to seeing the results.

Our head of early detection research, Dr David Crosby, sums it up nicely: Although this test is still at an early stage of development, the initial results are encouraging. And if the test can be fine-tuned to be more efficient at catching cancers in their earliest stages, it could become a tool for early detection.

But more research is needed to improve the tests ability to catch early cancers and we still need to explore how it might work in a real cancer screening scenario.

Lilly

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New blood test study uses artificial intelligence to identify cancer. But its not ready for patients yet. - Cancer Research UK - Science Blog

BOSTON, March 31, 2020 /PRNewswire/ -- Artificial intelligence (AI) is revolutionizing medical diagnostics. The state-of-the-art results have already demonstrated that software can achieve fast and accurate image-based diagnostics on various conditions affecting the skin, eye, ear, lung, breast, and so on. These technological advancements can help automate the diagnosis and triage processes, accelerating the process to speed up the referral process especially in urgent cases, freeing up expert resources, offering the best accuracy everywhere regardless of skill levels, and making the processes more widely available. This is a ground-breaking development with far-reaching consequences. Naturally, many innovators are scrambling to capitalize on these advancements.

The report "Digital Health & Artificial Intelligence 2020: Trends, Opportunities, and Outlook" from emerging technology research firm IDTechEx, has examined this trend. This report considers the trend towards digital and AI applications in health. It outlines the state-of-the-art in AI-based diagnosis of various conditions affecting the skin, eye, heart, breast, brain, lung, blood, genetic disorders and so on. The data sources employed are diverse including dermoscopic images, fundus images, OCT, CT, CTA, echocardiograms, electrocardiogram, mammography, pathology slides, low-res mobile phone pictures and more. This report then identifies and highlights companies seeking to capitalize on these technology advances to automate the diagnostic and triage process.

Furthermore, this report considers the trend of digital health more generally. It provides a detailed overview of the ecosystem and offersinsights into the key trends, opportunities and outlooks for all aspects of digital health, including:Telehealth and telemedicine, Remote patient monitoring, Digital therapeutics / digiceuticals / software as a medical device, Diabetes management, Consumer genetic testing, Smart home as a carer and AI in diagnostics.

Ground-breaking technology

Significant funding is flowing to start-ups and R&D teams of large corporations who develop AI tools to accelerate and/or improve the detection and classification of various diseases based on numerous data sources ranging from RGB images to CT scans, ECG signals, mammograms and to pathological slides. The state-of-the-art results demonstrate that software can do these tasks faster, cheaper, and often more accurately than trained experts and professionals.

This is an important development which, if successful, can have far-reaching consequences: it can make diagnostics much more widely available and it can free up medical experts' time to focus on more complex tasks which currently sit beyond the capabilities of AI-based automation. The technology is today making leaps forward, but technology is only a piece of the puzzle, and many other challenges will need to be overcome before such software tools are widely adopted. However, the direction of travel is clear.

This trend is today on the rise because (a) the availability of digitized medical data sources is rapidly increasing, offering excellent algorithm training feedstock, and (b) advancements in AI algorithms specially trained deep neural networking are enabling software to tackle tasks which it hitherto could not do.

Story continues

The IDTechEx report "Digital Health & Artificial Intelligence 2020: Trends, Opportunities, and Outlook" outlines many such advancements and identifies some of the key companies pursuing each opportunity. The remainder of this article briefly outlines two specific cases: eye disease and skin disease.

Eye Disease

Diabetic retinopathy is a complication that affects the eye. Researchers from India have recently shown that the software accurately interprets retinal fundus photographs to enable a large-scale screening program to detect diabetic retinopathy. The software is trained to make multiple binary classifications, allocating a risk level to each patient. The algorithm was trained and tuned on a total of more than 140k images. The machine matched and exceeded the sensitivity and selectivity level achieved by trained manual experts. The software achieved 92.1% and 95.2% sensitivity and selectivity, respectively.

Naturally, there is a strong business case here, and many are seeking to capitalize on it. One example is IDx, based out of Iowa in the US, who has designed and developed an algorithm to detect diabetic retinopathy. Their AI system achieves a sensitivity and specificity of 87% and 90%, respectively. In as early as 2017, it was tested at 10 sites across the US on 900 patients.

A very insightful test in eye clinics is the OCT (optical coherence tomography), which creates high-resolution (5um) 3D maps of the back of the eye and require expert analysis to interpret. OCT is now one of the most common imaging procedures with 5.35 million OCT scans performed in the US Medicare population in 2014 alone. This creates a backlog in processing and triage, and such delays can be harmful when they cause avoidable treatment delay for urgent cases.

DeepMind (Google) has demonstrated an algorithm that can automate the triage process based on 3D OCT image. Their algorithm design has some unique features. It consists of two stages: (1) a segmentation network and (2) a classification network. The first network will output a labelled tissue segmentation map. Based on the segmented maps, the second network will output a diagnosis probability for over 50 eye-threatening eye conditions and provide referral suggestion. The first part was trained on 877 sparely and manually segmented images and the second network on 14,884 training tissue maps with confirmed diagnosis and referral decision.This database is one of the best curated medical eye databases worldwide.

This two-stage design is beneficial in that when the OCT machine or image definition changes, only the first part will need to be retrained. This will help this algorithm become more universally applicable. In an end-to-end training network, the entire network would need to be retrained.

DeepMind demonstrated that performance of their AI in making a referral recommendation, reaches or exceeds that of experts on a range of sight-threatening retinal diseases. The error rate on referral decision is 5.5%, exceeding or matching specialists even when specialists are given fundus images as well as patient notes in addition to the OCT. Furthermore, the AI beat all retina specialists and optometrists on selectivity and sensitivity measures in referring urgent cases. This is clearly the first step, but an important one that truly opens the door.

Skin disease

Researchers at Heidelberg have already demonstrated that trained deep neural networks, in this case based on Google's Inception v4 CNN architecture, can recognize melanoma based on dermoscopy images. These researchers showed that the software achieves 10 percent more specificity than human clinicians when the sensitivity was set at a level matching human clinicians. The machine can achieve a high 95% sensitivity at a 63.8% specificity.

This is a promising result that shows such diagnostics can be automated. Indeed, multiple companies are automating detection of cancer diseases. One example is SkinVision, from the Netherlands, which seeks to offer a risk rating of skin cancer based on relatively low-quality smartphone images. They trained their algorithm on more than 131k images from 31k users in multiple countries. The risk ranking of the training images were annotated by dermatologists. Studies show that the algorithm can score a 95.1% sensitivity in detecting (pre)malignant conditions with 78.3% specificity. These are good results although the specificity may need to improve as it could unnecessarily alarm some patients.

The business cases are not just limited to cancer detection. Haut.AI is an Estonian company that proposes to use images to track skin dynamics and offer recommendations. One example is that their AI can be a simple and accurate predictor of chronological age using just the anonymized images of eye corners. The networks were trained on 8414 anonymized highresolution images of eye corners labelled with the correct chronological age. For people within the age range of 20 to 80 in a specific population, the machine reaches a mean absolute error of 2.3 years.

There are naturally many more start-ups active in this field. Some firms are focused on health diagnostic whilst others are seeking to use the AI to create tailored skincare regimes and product recommendation. The path to market, and the regulatory barriers, for each target function will naturally be different.

To learn more about this exciting field, please see IDTechEx's report "Digital Health & Artificial Intelligence 2020: Trends, Opportunities, and Outlook" by visitingwww.IDTechEx.com/digitalhealth. This report outlines the state-of-the-art in the use of AI in diagnosing a range of medical conditions. It also identifies and discusses the progress of various companies seeking to commercialize such technological advances. Furthermore, this report considers the trend of digital health more generally. It provides a detailed overview of the ecosystem and offers insights into the key trends, opportunities and outlooks for all aspects of digital health, including: Telehealth and telemedicine, Remote patient monitoring, Digital therapeutics / digiceuticals / software as a medical device, Diabetes management, Consumer genetic testing, Smart home as a carer and AI in diagnostics.

To connect with others on this topic, register for The IDTechEx Show! USA 2020, November 18-19 2020, Santa Clara, USA. Presenting the latest emerging technologies at one event, with six concurrent conferences and a single exhibition covering 3D Printing and 3D Electronics, Electric Vehicles, Energy Storage, Graphene & 2D Materials, Healthcare, Internet of Things, Printed Electronics, Sensors and Wearable Technology. Please visit http://www.IDTechEx.com/USAto find out more.

IDTechEx guides your strategic business decisions through its Research, Consultancy and Event products, helping you profit from emerging technologies. For more information on IDTechEx Research and Consultancy contact research@IDTechEx.com or visit http://www.IDTechEx.com.

Media Contact:

Jessica AbineriMarketing Coordinatorpress@IDTechEx.com +44-(0)-1223-812300

View original content:http://www.prnewswire.com/news-releases/artificial-intelligence-idtechex-research-on-state-of-the-art-and-commercialisation-status-in-diagnostics-and-triage-301032810.html

SOURCE IDTechEx

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Artificial Intelligence: IDTechEx Research on State-Of-The-Art and Commercialisation Status in Diagnostics and Triage - Yahoo Finance

DLBA Naval Architects created a new artificial intelligence concept, that makes it possible for some systems to be operated without any human interference.

DLBA has selected a 58m superyacht concept to develop internally as an autonomous yacht. The result, TEMPO, will be a study in all vessel systems where artificial intelligence can be used to enhance an owners experience onboard.

There are three main areas where autonomous technology can be used in the maritime world - navigational autonomy, equipment health monitoring, and mechanical and electrical systems automation. All reduce the need for human input while increasing efficiency.

Navigation autonomy relieves the workload on the vessel operator, and unmanned vessels have been operating in the commercial and military space for years. Hull, mechanical and electrical automation is like having an onboard engineering team at your fingertips. By ensuring elements at the sub-system level are AI-ready, the vessel can be kept operating at peak performance, efficiently.

The number and complexity of auxiliary systems and equipment onboard yachts is increasing year-on-year, and with that comes the increasing demand on crews time to interpret feedback from the systems - equipment health monitoring lessens this demand on crew time.

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58m Tempo by DLBA: superyacht optimized with artificial intelligence in every system - Yacht Harbour

Written by AZoRoboticsApr 1 2020

Businesses are often tempted to employ a range of technologies, including artificial intelligence (AI), to enhance performance, reduce labor costs, and improve the bottom linea fact that is logical.

Image Credit: Rensselaer Polytechnic Institute.

However, before opting for automation that can potentially risk the jobs of humans, business owners should carefully assess their operations.

According to Chris Meyer, a professor of practice and the director of undergraduate education at the Lally School of Management at Rensselaer Polytechnic Institute, the same method should not be used when applying AI to each business.

Meyer had studied this topic and has now detailed this in a recent conceptual paperpublishedin an exclusive issue of the Journal of Service Management on AI and Machine Learning in Service Management.

AI has the potential to upend our ideas about what tasks are uniquely suited to humans, but poorly implemented or strategically inappropriate service automation can alienate customers, and that will hurt businesses in the long term.

Chris Meyer, Professor of Practice and Director of Undergraduate Education, The Lally School of Management, Rensselaer Polytechnic Institute

Based on Meyers findings, the option to utilize AI or automation has to be a strategic decision. For example, if a companys business competes by providing an array of service offerings that shift from one client to another, or by offering a considerable amount of human interaction, then its business will experience a lower success rate if human experts are replaced with AI technologies.

Meyer further observed that the reverse is also true: Businesses that restrict customer interaction and choice will witness better success if they decide to automate.

Business leaders planning to migrate to automation should cautiously assess their strategies for handling knowledge resources. Before investing in AI, companies should first understand whether it is a strategically viable option to use algorithms and digital technologies in the place of human interaction and judgment.

The ideas are of use to managers, as they suggest where and how to use automation or human service workers based on ideas that are both sound and practical. Managers need guidance. Like any form of knowledge, AI and all forms of service automation have their place, but managers need good models to know where that place is.

Chris Meyer, Professor of Practice and Director of Undergraduate Education, The Lally School of Management, Rensselaer Polytechnic Institute

Meyer also established that in businesses where reputation and trust are vital factors in fostering and sustaining a client base, individuals will probably be effective than that of automated technologies.

On the other hand, in businesses where human biases are specifically dangerous to the service provision, AI will serve as a comparatively better tool for companies.

Meyer further stressed that several businesses will eventually be utilizing a combination of automation and peoples skills to compete effectively. Even AI, which can manage highly complicated jobs, works optimally alongside humansand the other way round.

Automation and human workers can and should be used together. But the extent of automation must fit with the businesss strategic approach to customers.

Chris Meyer, Professor of Practice and Director of Undergraduate Education, The Lally School of Management, Rensselaer Polytechnic Institute

Source: https://rpi.edu/

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The Limitations of Artificial Intelligence in Businesses - AZoRobotics

The agency is looking at how to best apply curated data sets to new use cases.

The Department of Veterans Affairs is closer to expanding its use of artificial intelligence and developing novel use cases.

In looking back on the early stages of the VAs newly launched artificial intelligence program, the department's Director of AI Gil Alterovitz noted ongoing questions about how to best leverage AI data sets for secondary uses.

One of the interesting challenges is often that data is collected for maybe one reason, and it may be used for analyzing and finding results for that one particular reason. But there may be other uses for that data as well. So when you get to secondary uses you have to examine a number of challenges, he said at AFCEA's Automation Transformation conference.

Some of the most pressing concerns the VAs AI program hasencountered include questions of how to best apply curated data sets to newfound use cases, as well as how to properly navigate consent of use for proprietary medical data.

Considering the specificity of use cases, particularly for advanced medical diagnostics and predictive analytics, Alterovitz has proposed releasing broader ecosystems of data sets that can be chosen and applied depending on the demands of specific AI projects.

Theres a lot to think about data sets and how they work together. Rather than release one data set, consider releasing an ecosystem of data sets that are related," he said."Imagine, for example, someone is searching for a trial you have information about. Consider the patient looking for the trial, the physician, the demographics, pieces of information about the trial itself, where its located. Having all that put together makes for an efficient use case and allows us to better work together."

Alterovitz also discussed the value of combining structured and unstructured data sets in AI projects, a methodology that Veterans Affairs has found to provide stronger results than using structured data alone.

When you look at unstructured data, there have been a number of studies in health care looking at medical records where if you look at only structured data or only unstructured data individually, you dont get as much of a predictive capability whether it be for diagnostics or prognostics as by combining them, he said.

Beyond refining and expanding these data applications methodologies, the VA also appears attentive to how to best leverage proprietary medical data while protecting personally identifying information.

The solution appears to lie in creating synthetic data sets that mimic the statistical parameters and overall metrics of a given data set while obscuring the particularities of the original data set it was sourced from.

How do you make data available considering privacy and other concerns?" Alterovitz said."One area is synthetic data, essentially looking at the statistics of the underlying data and creating a new data set that has the same statistics, but cant be identified because it generates at the individual level a completely different data set that has similar statistics."

Similarly, creating select variation within a given data set can serve to remove the possibility of identifying the patient source, You can take the data, and then vary that information so that its not the exact same information you received, but is maybe 20% different. This makes it so you can show its statistically not possible to identify that given patient with confidence.

Going forward, the VA appears intent on solving these quandaries so as to best inform expanded AI research.

A lot of the data we have wasnt originally designed for AI. How you make it designed and ready for use in AI is a challenge and one that has a number of different potential avenues, Alterovitz concluded

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VA Looking to Expand Usage of Artificial Intelligence Data - GovernmentCIO Media