Google designed the suite to ease radiologists’ workload and enable easy and secure sharing of critical medical imaging; technology may eventually be adapted to pathologists’ workflow
Clinical laboratory and pathology group leaders know that Google is doing extensive research and development in the field of cancer diagnostics. For several years, the Silicon Valley giant has been focused on digital imaging and the use of artificial intelligence (AI) algorithms and machine learning to detect cancer.
Now, Google Cloud has announced it is launching a new medical imaging suite for radiologists that is aimed at making healthcare data for the diagnosis and care of cancer patients more accessible. The new suite “promises to make medical imaging data more interoperable and useful by leveraging artificial intelligence,” according to MedCity News.
In a press release, medical technology company Hologic, and healthcare provider Hackensack Meridian Health in New Jersey, announced they were the first customers to use Google Cloud’s new suite of medical imaging products.
“Hackensack Meridian Health has begun using it to detect metastasis in prostate cancer patients earlier, and Hologic is using it to strengthen its diagnostic platform that screens women for cervical cancer,” MedCity News reported.
“Google pioneered the use of AI and computer vision in Google Photos, Google Image Search, and Google Lens, and now we’re making our imaging expertise, tools, and technologies available for healthcare and life sciences enterprises,” said Alissa Hsu Lynch (above), Global Lead of Google Cloud’s MedTech Strategy and Solutions, in a press release. “Our Medical Imaging Suite shows what’s possible when tech and healthcare companies come together.” Clinical laboratory companies may find Google’s Medical Imaging Suite worth investigating. (Photo copyright: Influencive.)
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Easing the Burden on Radiologists
Clinical laboratory leaders and pathologists know that laboratory data drives most healthcare decision-making. And medical images make up 90% of all healthcare data, noted an article in Proceedings of the IEEE (Institute of Electrical and Electronics Engineers).
More importantly, medical images are growing in size and complexity. So, radiologists and medical researchers need a way to quickly interpret them and keep up with the increased workload, Google Cloud noted.
“The size and complexity of these images is huge, and, often, images stay sitting in data siloes across an organization,” said Alissa Hsu Lynch, Global Lead, MedTech Strategy and Solutions at Google, told MedCity News. “In order to make imaging data useful for AI, we have to address interoperability and standardization. This suite is designed to help healthcare organizations accelerate the development of AI so that they can enable faster, more accurate diagnosis and ease the burden for radiologists,” she added.
According to the press release, Google Cloud’s Medical Imaging Suite features include:
Imaging Storage: Easy and secure data exchange using the international DICOM (digital imaging and communications in medicine) standard for imaging. A fully managed, highly scalable, enterprise-grade development environment that includes automated DICOM de-identification. Seamless cloud data management via a cloud-native enterprise imaging PACS (picture archiving and communication system) in clinical use by radiologists.
Imaging Lab: AI-assisted annotation tools that help automate the highly manual and repetitive task of labeling medical images, and Google Cloud native integration with any DICOMweb viewer.
Imaging Datasets and Dashboards: Ability to view and search petabytes of imaging data to perform advanced analytics and create training datasets with zero operational overhead.
Imaging AI Pipelines: Accelerated development of AI pipelines to build scalable machine learning models, with 80% fewer lines of code required for custom modeling.
Imaging Deployment: Flexible options for cloud, on-prem (on-premises software), or edge deployment to allow organizations to meet diverse sovereignty, data security, and privacy requirements—while providing centralized management and policy enforcement with Google Distributed Cloud.
First Customers Deploy Suite
Hackensack Meridian Health hopes Google’s imaging suite will, eventually, enable the healthcare provider to predict factors affecting variance in prostate cancer outcomes.
“We are working toward building AI capabilities that will support image-based clinical diagnosis across a range of imaging and be an integral part of our clinical workflow,” said Sameer Sethi, Senior Vice President and Chief Data and Analytics Officer at Hackensack, in a news release.
The New Jersey healthcare network said in a statement that its work with Google Cloud includes use of AI and machine learning to enable notification of newborn congenital disorders and to predict sepsis risk in real-time.
Hologic, a medical technology company focused on women’s health, said its collaboration integrates Google Cloud AI with the company’s Genius Digital Diagnostics System.
“By complementing our expertise in diagnostics and AI with Google Cloud’s expertise in AI, we’re evolving our market-leading technologies to improve laboratory performance, healthcare provider decision making, and patient care,” said Michael Quick, Vice President of Research and Development and Innovation at Hologic, in the press release.
Hologic says its Genius Digital Diagnostics System combines AI with volumetric medical imaging to find pre-cancerous lesions and cancer cells. From a Pap test digital image, the system narrows “tens of thousands of cells down to an AI-generated gallery of the most diagnostically relevant,” according to the company website.
Hologic plans to work with Google Cloud on storage and “to improve diagnostic accuracy for those cancer images,” Hsu Lynch told MedCity News.
Medical image storage and sharing technologies like Google Cloud’s Medical Imaging Suite provide an opportunity for radiologists, researchers, and others to share critical image studies with anatomic pathologists and physicians providing care to cancer patients.
One key observation is that the primary function of this service that Google has begun to deploy is to aid in radiology workflow and productivity, and to improve the accuracy of cancer diagnoses by radiologists. Meanwhile, Google continues to employ pathologists within its medical imaging research and development teams.
Assuming that the first radiologists find the Google suite of tools effective in support of patient care, it may not be too long before Google moves to introduce an imaging suite of tools designed to aid the workflow of surgical pathologists as well.
There was cautious optimism about the ability of Canada’s medical laboratories to innovate in ways that advance patient care, while recognizing the ongoing challenge of adequate lab staffing and budget constraints
TORONTO, ONTARIO, CANADA—This week, more than 150 leaders representing clinical laboratories, anatomic pathology labs, in vitro diagnostics (IVD) companies, and provincial health officials gathered for the first “Canadian Diagnostic Executive Forum” (CDEF) since 2019. It would be apt to say that the speakers objectively addressed all the good, the bad, and the ugly of Canada’s healthcare system and its utilization of medical laboratory testing services.
Over the two days of the conference, speakers and attendees alike concurred that the two biggest issues confronting clinical laboratories in Canada were inadequate staffing and an unpredictable supply chain. There also was agreement that the steady increase in prices, fueled by inflation, is exacerbating continuing cost increases in both lab salaries and lab supplies.
Canada’s Health System Has Several Unique Attributes
Canada’s healthcare system has two unique attributes that differentiate it from those of other nations. First, healthcare is mandated by a federal law, but generally each of Canada’s 13 provinces and territories operates its own health plan. Thus, the health system in each province and territory may cover a different mix of clinical services, therapeutic drugs, and medical procedures. The federal government typically pays 40% of a province’s health costs and the province funds the balance.
Second, it is a fact that 90% of the Canadian population lives within 150 miles of the United States border. Yet there are provinces with large populations that have geography that ranges from the US border to north of the Arctic Circle. These provinces have a major challenge to ensure equal access to healthcare regardless of where their citizens live.
During day one of the conference, several presentations addressed innovations that supported those labs’ efforts to deliver value and timely insights during the COVID-19 pandemic. For example, a lab team in Alberta launched a research study involving SARS-CoV-2 virus surveillance from the earliest days of the outbreak. This study was presented by Mathew Diggle, PhD, FRCPath, Associate Professor and Program Lead for the Public Health Laboratory (ProvLab) Medical-Scientific Staff at Alberta Precision Laboratories in Edmonton, Alberta.
Study Designed to Identify Coinfections with COVID-19
While performing tens of thousands of COVID-19 tests from the onset of the pandemic, and identifying the emergence of variants, the ProvLab team also tracked co-infection involving other respiratory viruses.
“This is one of the largest eCoV [endemic coronavirus] studies performed during the COVID-19 pandemic,” Diggle said. “This broad testing approach helped to address a pivotal diagnostic gap amidst the emergence of a novel pathogen: cross-reactivity with other human coronaviruses that can cause similar clinical presentations. This broad surveillance enabled an investigation of cross-reactivity of a novel pathogen with other respiratory pathogens that can cause similar clinical presentations.
“Fewer than 0.01% of specimens tested positive for both SARS-CoV-2 and an eCoV,” he explained. “This suggested no significant cross-reactivity between SARS-CoV-2 and eCoVs on either test and provided a SARS-CoV-2 negative predictive value over 99% from an eCoV-positive specimen … The data we collected was highly compelling and the conclusion was that there was no coinfection.”
Chairing the two days of presentations at this weeks’ Canadian Diagnostic Executive Forum was Kevin D. Orr (above), Senior Director of Hospital Business at In-Common Laboratories. He also served on the program for this national conference serving clinical laboratories, anatomic pathology labs, and in vitro diagnostics (IVD) companies throughout Canada. This was the first gathering of this conference since 2019. Attendees were enthusiastic about the future of medical laboratory services in Canada, despite lab staffing shortages and rising costs due to inflation. (Photo copyright The Dark Report.)
Clinical Laboratory Regionalization in Quebec
One of Canada’s largest projects to regionalize and harmonize clinical laboratory services is proceeding in Quebec. Leading this effort is Ralph Dadoun, PhD, Project Director for OPTILAB Montreal, which is part of the Ministry of Health and Social Services in Quebec. The ambitious goal for this project is to move the 123 clinical laboratories within the province into 12 clusters. Initial planning was begun in 2013, so this project is in its ninth year of implementation.
During his presentation, Dadoun explained that the work underway in the 12 clusters involves creating common factors in these categories:
Implementation consistent with and respecting ISO-15189 criteria.
Another notable achievement in Quebec is the progress made to implement a common laboratory information system (LIS) within all 12 clusters. The first three laboratory clusters are undergoing their LIS conversions to the same platform during the next 180 days. The expectation is that use of a common LIS across all clinical laboratory sites in Quebec will unlock benefits in a wide spectrum of lab activities and work processes.
The 2022 CDEF featured speakers from most of the provinces. The common themes in these presentations were the shortage of lab personnel across all technical positions, disruptions in lab supplies, and the need to support the usual spectrum of lab testing services even as lab budgets are getting squeezed.
At the same time, there was plenty of optimism. Presentations involving adoption of digital pathology, advances in early disease detection made possible by new diagnostic technologies, and the expansion of precision medicine showed that clinical laboratories in Canada are gaining tools that will allow them to contribute to better patient care while helping reduce the downstream costs of care.
The Canadian Diagnostics Executive Forum is organized by a team from In-Common Laboratories in North York, Toronto, Ontario. Founded in 1967, it is a private, not-for-profit company that works with public hospitals and laboratory medicine providers. Information about CDEF can be found at its website, where several of this year’s presentations will be available for viewing.
Labcorp, the commercial laboratory giant headquartered in Burlington, N.C., has billions of diagnostic test results archived. It takes samplings of those results and runs them through a machine learning algorithm that compares the data against a condition of interest, such as chronic kidney disease (CKD). Machine learning is a subdiscipline of AI.
Based on patterns it identifies, the machine learning algorithm can predict future test results for CKD based on patients’ testing histories, explained Stan Letovsky, PhD, Vice President for AI, Data Sciences, and Bioinformatics at Labcorp. Labcorp has found the accuracy of those predictions to be better than 90%, he added.
Labcorp also has created an AI-powered dashboard that—once layered over an electronic health record (EHR) system—allows physicians to configure views of an individual patient’s existing health data and add a predictive view based on the machine learning results.
For anatomic pathologists, this type of setup can quickly bring a trove of data into their hands, allowing them to be more efficient with patient diagnoses. The long-term implications of using this technology are significant for pathology groups’ bottom line.
Stan Letovsky, PhD (above), Vice President for AI, Data Sciences, and Bioinformatics at Labcorp, discussed AI developments in digital pathology during his keynote address at the 2022 Executive War College in New Orleans. “The best thing as a community that we can do for patients and their physicians with AI is to identify care gaps early on,” he said, adding, “If pathologists want to grow and improve their revenue, they have to be more productive.” (Photo copyright: Dark Intelligence Group).
Mayo Clinic Plans to Digitize 25 Million Glass Slides
In other AI developments, Mayo Clinic in Rochester, Minn., has started a project to digitally scan 25 million tissue samples on glass slides—some more than 100 years old. As part of the initiative, Mayo wants to digitize five million of those slides within three years and put them on the cloud, said pathologist and physician scientist Jason Hipp, MD, PhD, Chair of Computational Pathology and AI at Mayo Clinic.
“We want to be a hub within Mayo Clinic for digital pathology,” Hipp told Executive War College attendees during his keynote address.
Hipp views his team as the bridge between pathologists and the data science engineers who develop AI algorithms. Both sides must collaborate to move AI forward, he commented, yet most clinical laboratories and pathology groups have not yet developed those relationships.
“We want to embed both sides,” Hipp added. “We need the data scientists working with the pathologists side by side. That practical part is missing today.”
The future medical laboratory at Mayo Clinic will feature an intersection of pathology, computer technology, and patient data. Cloud storage is a big part of that vision.
“AI requires storage and lots of data to be practical,” Hipp said.
An estimated 80 pathologists will now work for larger pathology superlabs as part of the deals, bringing stiffer competition to independent anatomic pathology groups
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Consolidation among private practice anatomic pathology groups continues with news that two large regional pathology groups decided to sell to larger pathology companies. The first transaction announced was on Dec. 16, 2021, when Sonic Healthcare of Sydney, Australia, disclosed that it had acquired Dallas-based ProPath. Sales price and other terms were not announced.
The second transaction happened last month. On Jan. 24, Nashville-based PathGroup announced it had bought Pathology Consultants of Greenville, S.C. Price and terms of this transaction also were not disclosed.
Pathology Consolidation Continues
The decision by two of the nation’s leading regional pathology groups to sell themselves to larger pathology entities confirms that the trend of consolidation is continuing within the pathology profession. It is also a sign that smaller pathology groups will find it increasingly difficult to compete and stay profitable as new technologies transform the surgical pathology profession, such a digital pathology platforms.
ProPath was considered a financially strong regional super-group, as it operates facilities in three states and has 50 pathologists and 500 employees. Sonic noted that ProPath’s annual revenue was about $110 million.
Sonic Healthcare also purchased Aurora Diagnostics in 2018 for $540 million. That deal brought it 32 pathology practice sites and added 220 pathologists to its roster.
With its acquisition of Pathology Consultants, PathGroup adds 30 pathologists and 100 employees. Prior to this acquisition, PathGroup said it had 225 pathologists.
“We will continue providing highly specialized pathology expertise, as well as a broad range of clinical and molecular pathology services,” PathGroup CEO Ben Davis. MD, said of the Pathology Consultants acquisition. (Photo copyright: ProPath)
Maintaining Independence Gets Tougher
Anatomic pathologists will want to understand why two major regional pathology groups have decided to give up their independence and sell to a larger company. The reasons are several and include:
Need for cash to purchase the equity of retiring baby boomer pathologist partners in the group.
Challenges in recruiting new pathologists to the group.
Need for capital to acquire digital pathology capabilities and other needed advanced diagnostic technologies.
Access to managed care contracts as private health plans continue to narrow their provider networks.
It should be noted that graduating pathology residents and fellows are tech-savvy and want to work in practices that have all the latest technologies in histology, scanning, and digital pathology. This observation plays into the consolidation of the market.
DeepMind hopes its unrivaled collection of data, enabled by artificial intelligence, may advance development of precision medicines, new medical laboratory tests, and therapeutic treatments
‘Tis the season for giving, and one United Kingdom-based artificial intelligence (AI) research laboratory is making a sizeable gift. After using AI and machine learning to create “the most comprehensive map of human proteins,” in existence, DeepMind, a subsidiary of Alphabet Inc. (NASDAQ:GOOGL), parent company of Google, plans to give away for free its database of millions of protein structure predictions to the global scientific community and to all of humanity, The Verge reported.
Pathologists and clinical laboratory scientists developing proteomic assays understand the significance of this gesture. They know how difficult and expensive it is to determine protein structures using sequencing of amino acids. That’s because the various types of amino acids in use cause the [DNA] string to “fold.” Thus, the availability of this data may accelerate the development of more diagnostic tests based on proteomics.
“For decades, scientists have been trying to find a method to reliably determine a protein’s structure just from its sequence of amino acids. Attraction and repulsion between the 20 different types of amino acids cause the string to fold in a feat of ‘spontaneous origami,’ forming the intricate curls, loops, and pleats of a protein’s 3D structure. This grand scientific challenge is known as the protein-folding problem,” a DeepMind statement noted.
Enter DeepMind’s AlphaFold AI platform to help iron things out. “Experimental techniques for determining structures are painstakingly laborious and time consuming (sometimes taking years and millions of dollars). Our latest version [of AlphaFold] can now predict the shape of a protein, at scale and in minutes, down to atomic accuracy. This is a significant breakthrough and highlights the impact AI can have on science,” DeepMind stated.
Release of Data Will Be ‘Transformative’
In July, DeepMind announced it would begin releasing data from its AlphaFold Protein Structure Database which contains “predictions for the structure of some 350,000 proteins across 20 different organisms,” The Verge reported, adding, “Most significantly, the release includes predictions for 98% of all human proteins, around 20,000 different structures, which are collectively known as the human proteome. By the end of the year, DeepMind hopes to release predictions for 100 million protein structures.”
According to Edith Heard, PhD, Director General of the European Molecular Biology Laboratory (EMBL), the open release of such a dataset will be “transformative for our understanding of how life works,” The Verge reported.
“I see this as the culmination of the entire 10-year-plus lifetime of DeepMind,” company CEO and co-founder Demis Hassabis (above), told The Verge. “From the beginning, this is what we set out to do: to make breakthroughs in AI, test that on games like Go and Atari, [and] apply that to real-world problems, to see if we can accelerate scientific breakthroughs and use those to benefit humanity.” The release of DeepMind’s entire protein prediction database will certainly do that. Clinical laboratory scientists worldwide will have free access to use it in developing new precision medicine treatments based on proteomics. (Photo copyright: BBC.)
Free Data about Proteins Will Accelerate Research on Diseases, Treatments
Research into how protein folds and, thereby, functions could have implications to fighting diseases and developing new medicines, according to DeepMind.
“This will be one of the most important datasets since the mapping of the human genome,” said Ewan Birney, PhD, Deputy Director General of the EMBL, in the DeepMind statement. EMBL worked with DeepMind on the dataset.
DeepMind protein prediction data are already being used by scientists in medical research. “Anyone can use it for anything. They just need to credit the people involved in the citation,” said Demis Hassabis, DeepMind CEO and Co-founder, in The Verge.
In a blog article, Hassabis listed several projects and organizations already using AlphaFold. They include:
“As researchers seek cures for diseases and pursue solutions to other big problems facing humankind—including antibiotic resistance, microplastic pollution, and climate change—they will benefit from fresh insights in the structure of proteins,” Hassabis wrote.
Because of the deep financial backing that Alphabet/Google can offer, it is reasonable to predict that DeepMind will make progress with its AI technology that regularly adds capabilities and accuracy, allowing AlphaFold to be effective for many uses.
This will be particularly true for the development of new diagnostic assays that will give clinical laboratories better tools for diagnosing disease earlier and more accurately.
