One of the world’s fastest growing medical laboratory companies in India is using digital pathology systems and AI to replace older diagnostic technologies
Artificial intelligence (AI) is gaining acceptance around the world and use of AI to analyze digital pathology images is expected to be a major disruptor to the profession of anatomic pathology. Internationally, several pathology companies already use AI-powered solutions to diagnose cancer.
One such example is Neuberg Diagnostics, a fast-growing clinical laboratory company in Chennai, India. Neuberg has been using AI to review digital pathology images for several years, according to Chairman and Managing Director GSK Velu, PhD, BPharm.
“We already use AI in our laboratories,” Velu said in an exclusive interview with Dark Daily. “Our main reference laboratories currently use digital pathology systems to support the pathologists and many of them are using AI with these digital pathology systems.
“AI and data analytics tools are being used in other departments too, such as in our wellness department where we use AI for predictive analytics,” he added. “We also use AI in our genomics division, and we are introducing AI into other divisions slowly and steadily.”
Neuberg operates 120 laboratories in an extensive network in India, South Africa, and the United Arab Emirates (UAE), and now in the US as well.
“Our idea is to enhance the access and affordability for next-generation techniques, meaning molecular diagnostics, genomics, pathology, digital pathology, proteomics, metabolomics, and all that. This is the spirit behind Neuberg Diagnostics,” said GSK Velu, PhD, BPharm (above), Chairman and Managing Director of Neuberg Diagnostics, in an exclusive interview with The Dark Report. Clinical laboratories that are considering investing in digital pathology technologies may want to follow its development at Neuberg’s Centre for Genomic Medicine in Raleigh, NC, which opened in May. (Photo copyright: Neuberg Diagnostics.)
Replacing Older Pathology Technologies
As has been happening at other anatomic pathology centers around the world, Neuberg has been using digital pathology systems to replace older technologies. “One of our largest labs is our Bangalore Reference Lab,” Velu said. “There, we do not use microscopes for histopathology, and that lab has used digital pathology for routine review of specimens for several years now.
“But because artificial intelligence is still emerging, we can’t rely on AI with all of our digital pathology systems,” he added. “Although, of course, AI is certainly an aid to everything we do with digital pathology.
“For a variety of reasons, the adaptation of artificial intelligence in anatomic pathology is not happening as effectively nor as fast as we would like,” he noted. “So, for now, we need to wait and watch a bit longer, either because adaptation by pathologists is slow, or because AI tools are still a bit of a worry for some pathologists.
Younger Pathologists Adapt Faster to Digital Pathology
One reason could be that conventional pathologists worry about relying completely on AI for any diagnosis, Velu noted. “I’m certain that the more recent generation of pathologists who are now in their 30s, and the new people coming into pathology, will start adapting more quickly to digital pathology and to AI faster than the older generation of pathologists have done.
“The younger pathologists have a greater appreciation for the potential of digital pathology, while the older pathologists don’t want to let go of conventional diagnosis methods,” he added.
“For example, we have not yet seen where pathologists are reviewing breast image scans,” he commented. “But, at the same time, AI has been well-accepted among radiologists who are reviewing breast mammography scans.”
In India and in other markets worldwide, radiologists have adapted AI tools for breast mammography scans to diagnose breast cancer, he noted. “But that’s not happening even among pathologists who are doing cancer screening,” he said.
Velu suggested that another reason for the slow adoption of AI tools in pathology is that these systems are relatively new to the market. “Maybe the AI tools that are used with digital pathology are not as reliable as we hoped they would be, or they are not fully robust at the moment,” he speculated. “That’s why I say it will take some time before the use of AI for diagnosis becomes more widespread among pathologists. So, for now, we must wait until digital pathology and AI tools work together more seamlessly.
Replacing Conventional Pathology Technologies and Methods
“When those two technologies—AI and digital pathology systems—are linked more closely, their use will take hold in a substantial way,” Velu predicted. “When that happens, they are likely to replace conventional pathology methods completely.
“Currently, we are in the early stages of a transformation,” he added. “In our labs, you can see that the transformation is ongoing. We are using digital pathology systems even in our smaller labs. Then, the staff in our smaller labs do the processing of slides to convert them to digital images and send them to our labs in the larger cities. There, the professional staff uses AI to review those digital images and issue reports based on those images.
“Using our digital pathology systems and AI in that way means that we can make that technology available even in smaller towns and villages that have access only to our smaller labs,” he commented.
Velu added that wider use of digital pathology systems could improve the quality of care that pathologists deliver to patients in a significant way, particularly in rural areas. “Here in India, we are not seeing a huge shortage of pathologists, except in rural areas and villages,” he explained. “In those places, we could run short of pathologists.
“That is the reason we are trying to adapt the use of telepathology more widely,” he noted. “To do that, we might have technicians and histologists who will do just processing of slides so that they can send the digital images to our pathologists located in larger cities. Then, those surgical pathologists will review the cases and send the reports out. That’s the model that we are trying to slowly follow here.”
As use of digital pathology images increased, many predicted that specimens would flow from the US to India. This would happen because of the belief that the lower cost of surgical pathology in India would successfully draw business away from pathology groups here in the United States.
However, Neuberg turned the tables on that belief when it announced the opening of its Neuberg Centre for Genomic Medicine (NCGM), a state-of-the-art esoteric and genetic testing laboratory in Raleigh, NC. The NCGM lab is CLIA-certified and Neuberg says it is ready to compete with labs in this country on their home turf.
These are reasons why pathologists and pathology practice administrators in the United States may want to watch how Neuberg Diagnostics continues to develop its use of digital pathology platforms and AI-powered digital image analysis tools throughout its international network of laboratories.
The new method employs a pH sensitive dye and AI algorithms to ‘distinguish between cells originating from normal and cancerous tissue, as well as among different types of cancer’ the researchers said
Might a pH-sensitive dye in tandem with an image analysis solution soon be used to identify cancerous cells within blood samples as well within tissue? Recent research indicates that could be a possibility. If further studies and clinical trials confirm this capability, then anatomic pathologists could gain another valuable tool to use in diagnosing cancers and other types of disease.
Currently, surgical pathologists use a variety of hematoxylin and eosin stains (H/E) to bring out useful features in cells and cell structures. So, staining tissue on glass slides is a common practice. Now, thanks to machine learning and artificial intelligence, anatomic pathologists may soon have a similar tool for spotting cancer cells within both tissue and blood samples.
Researchers at the National University of Singapore (NUS) have developed a method for identifying cancer that uses a pH sensitive dye called bromothymol blue. The dye reacts to various levels of acidity in cancer cells by turning colors. “The pH inside cancer cells tends to be higher than that of healthy cells. This phenomenon occurs at the very early phases of cancer development and becomes amplified as it progresses,” Labroots reported.
In “Machine Learning Based Approach to pH Imaging and Classification of Single Cancer Cells,” published in the journal APL Bioengineering, the NUS researchers wrote, “Here, we leverage a recently developed pH imaging modality and machine learning-based single-cell segmentation and classification to identify different cancer cell lines based on their characteristic intracellular pH. This simple method opens up the potential to perform rapid noninvasive identification of living cancer cells for early cancer diagnosis and further downstream analyses.”
According to an NUS news release, the bromothymol blue dye is “applied onto patients’ cells” being held ex vivo in cell culture dishes. The dye’s color changes depending on the acidity level of the cancer cells it encounters. Microscopic images of the now-visible cancers cells are taken, and a machine-learning algorithm analyzes the images before generating a report for the anatomic pathologist.
The NUS researchers claim the test can provide answers in about half an hour with 95% accuracy, Labroots reported.
“The ability to analyze single cells is one of the holy grails of health innovation for precision medicine or personalized therapy. Our proof-of-concept study demonstrates the potential of our technique to be used as a fast, inexpensive and accurate tool for cancer diagnosis,” said Lim Chwee Teck, PhD, NUS Society Professor and Director of NUS’ Institute for Health Innovation and Technology, in the NUS news release.
The novel technique for differentiating cancer cells from non-cancerous cells being developed at the National University of Singapore (NUS) could eventually become useful in detecting cancer cells in tissue samples, either obtained from tumor biopsies or blood samples. “As the number of cells in these samples can be in millions or even billions, the ability to detect the very few cancer cells among the others will be useful for clinicians,” NUS Society Professor and Director of NUS’ Institute for Health Innovation and Technology, Lim Chwee Teck, PhD (above) told The Straits Times. (Photo copyright: The Straits Times.)
AI Cell Analysis versus Laborious Medical Laboratory Steps
By developing an AI-driven method, Professor Lim and the NUS team sought to improve upon time-consuming techniques for identifying cells that traditionally involve using florescent probes, nanoparticles, and labeling steps, or for cells to be fixed or terminated.
“Unlike other cell analysis techniques, our approach uses simple, inexpensive equipment, and does not require lengthy preparation and sophisticated devices. Using AI, we are able to screen cells faster and accurately,” Professor Lim told Labroots. “Furthermore, we can monitor and analyze living cells without causing any toxicity to the cells or the need to kill them.”
The new technique may have implications for cancer detection in tumor tissue as well as in liquid biopsies.
“We are also exploring the possibility of performing the real-time analysis on circulating cancer cells suspended in blood,” Professor Lim said in the NUS news release. “One potential application for this would be in liquid biopsy where tumor cells that escaped from a primary tumor can be isolated in a minimally-invasive fashion from bodily fluids such as blood.”
Diagnosing Cancer in Real Time
The NUS’ method requires more research and clinical studies before it could become an actual tool for anatomic pathologists and other cancer diagnosticians. Additionally, the NUS researchers acknowledged that the focus on only four cell lines (normal cells, benign breast tumor cells, breast cancer cells, and pancreatic cancer cells) limited their study, as did lack of comparison with conventional florescent pH indicators.
Still, the NUS scientists are already planning more studies to advance their concept to different stages of cell malignancy. They envision a “real-time” version of the technique to enable recognition of cells and fast separation of those that need to be referred to clinical laboratories for molecular testing and/or genetic sequencing.
Medical laboratory leaders may want to follow the NUS study. An inexpensive AI-driven method that can accurately detect and classify cancer cells based on pH within the cells is provocative and may be eventually become integrated with other cancer diagnostics.
Fear that immunity-resistant mutations of SARS-CoV-2 will emerge are real and the scientific community is paying close attention
Detection of an increasing number of new variants of the SARS-CoV-2 coronavirus raises the possibility that a new strain of COVID-19 might emerge that brings new problems to the management of the pandemic. Public health officials and clinical laboratory scientists are on the alert to determine if any new COVID-19 variant is more virulent or more easily transmissible.
Pathologists, along with the rest of the scientific community worldwide, are following reports of increasing coronavirus mutations with growing concern. The Alpha variant (Lineage B.1.1.7) accounted for most of the COVID-19 cases in April of 2021 in the US, though it was first identified in the United Kingdom. That was followed by the Iota variant (Lineage B.1.526) first identified in New York City. A series of other variants were to follow. Scientists were not surprised. It is normal for viruses to mutate, so they logged and tracked the mutations.
Then, the Delta variant (Lineage B.1.617.2) emerged during a severe outbreak in India. At first, it did not seem more threatening than any other variant, but that changed very quickly. Delta was different.
“The speed with which it dominated the pandemic has left scientists nervous about what the virus will do next. The variant battles of 2021 are part of a longer war, one that is far from over,” The Washington Post reported, which added, “Today, [Delta] has nearly wiped out all of its rivals. The coronavirus pandemic in America has become a Delta pandemic. By the end of July, it accounted for 93.4% of new infections, according to the Centers for Disease Control and Prevention.”
Why is Delta the Worst COVID-19 Variant So Far?
The Delta variant has two advantages that scientists know about:
Stickier spike protein than the spike on the original SARS-CoV-2 coronavirus, as well as on the other, earlier variants. This means that the Delta variant stands a better chance of remaining in a person’s nose or throat long enough to reproduce.
Faster replication. When a virus mutation has more opportunity to reproduce, it quickly becomes the main viral strain. This is the case with the Delta variant. Experts say that the viral load in patients with Delta is around 1,000 times higher than in patients with the original virus.
The image above is a “Colorized scanning electron micrograph of an apoptotic cell (tan) heavily infected with SARS-COV-2 virus particles (orange), isolated from a patient sample,” Newsweek reported. (Photo copyright: National Institute of Allergy and Infectious Diseases/Newsweek.)
Will More Dangerous SARS-CoV-2 Variants Appear?
“The great fear is that nature could spit out some new variant that completely saps the power of vaccines and upends the progress we’ve made against the pandemic. But to virologists and immunologists, such a possibility seems very unlikely,” STAT reported.
That is because, unlike Influenza, which is also a coronavirus, SARS-CoV-2 variants are not able to share genetic materials and recombine into deadlier variants. Thus, scientists are skeptical that a variant could appear and wipe out the progress made with vaccines and treatments.
One of the reasons the Flu vaccine changes every year is Influenza’s ability to recombine into variants that can evade immunity. Therefore, scientists are beginning to suspect that SARS-CoV-2, like the Flu, will likely be around for a while.
“I don’t think eradication is on the table. But I think we could come up with something that’s better than what we have for the flu,” Sharone Green, MD, Associate Professor of Medicine, Division of Infectious Diseases and Immunology and Infection Control Officer at University of Massachusetts Medical School, told Newsweek.
Limiting Infections and Replication
Several factors combined to create the COVID-19 pandemic. But SARS-CoV-2 was a novel coronavirus, meaning it was a new pathogen of a known virus. This meant every person on the planet was a potential host.
The situation now is different. Thanks to natural immunity, vaccines, and treatments that shorten the infection, the SARS-CoV-2 coronavirus has less chance to replicate.
“The pressure is there, but the opportunity is not. The virus has to replicate in order to mutate, but each virus doesn’t get many lottery tickets in a vaccinated person who’s infected,” Jeremy Kamil, PhD, Associate Professor of Microbiology and Immunology at LSU Health in Shreveport, La., told STAT.
Tracking Variants of Interest and Variants of Concern
The World Health Organization (WHO) has been monitoring the viral evolution of SARS-CoV-2 since the beginning of the pandemic. In late 2020, the WHO created categories for tracking variants:
VOIs are “A variant with specific genetic markers that have been associated with changes to receptor binding, reduced neutralization by antibodies generated against previous infection or vaccination, reduced efficacy of treatments, potential diagnostic impact, or predicted increase in transmissibility or disease severity.”
Current VOIs include:
Eta (Lineage B.1.525), detected in multiple countries, designated a VOI in March 2021.
Iota (Lineage B.1.526), US, first detected in November 2020, designated a VOI in March 2021.
Lambda (lineage C.37), Peru, first detected in December 2020, designated a VOI in June 2021.
VOCs, on the other hand, demonstrate all the characteristics of VOIs and also demonstrate “an increase in transmissibility, more severe disease (e.g., increased hospitalizations or deaths), significant reduction in neutralization by antibodies generated during previous infection or vaccination, reduced effectiveness of treatments or vaccines, or diagnostic detection failures.”
Current VOCs include:
Alpha (lineage B.1.1.7), first detected in the UK, September 2020.
Delta (lineage B.1.617.2), first detected in India, October 2020.
Will Vaccines Stop Working?
With each new variant, there tends to be a flurry of media attention and fearmongering. That a variant could emerge which would render our current vaccines ineffective has the scientific community’s attention.
“There is intense interest in whether mutations in the spike glycoprotein mediate escape from host antibodies and could potentially compromise vaccine effectiveness, since spike is the major viral antigen in the current vaccines,” wrote Adam S. Lauring, MD, PhD, and Emma B. Hodcroft, PhD, in “Genetic Variants of SARS-CoV-2—What Do They Mean?” published in the Journal of the American Medical Association (JAMA).
“Because current vaccines provoke an immune response to the entire spike protein, it is hoped that effective protection may still occur despite a few changes at antigenic sites in SARS-CoV-2 variants,” they added.
Future events may justify the optimism that the ongoing effectiveness of vaccines will help with many COVID-19 variants. But pathologists and clinical laboratory leaders may want to be vigilant, because as infection rates increase, so do workloads and demands on critical resources in their medical laboratories.
These new findings may affect how microbiology labs and physicians diagnose and treat several gastrointestinal conditions
Once again, a research effort has teased out new insights into the role the human microbiome plays in our digestive processes. Microbiologist and medical laboratory managers will be interested to learn that, according to the study team, specific microbes have a role in regulating how fast food moves through the digestive tract.
Researchers at the Dey Laboratory in Seattle recently examined the function of microbial bile acid metabolism in gut motility. They determined that “metabolites generated by the gut microbiome regulate gut transit,” according to a new paper published by the Fred Hutchinson Cancer Research Center (Fred Hutch).
“These findings have potential implications for the treatment of gastrointestinal conditions,” noted a Fred Hutch news release. This may mean new clinical laboratory tests to identify these strains of bacteria, along with new therapies for treating patients.
Gut motility (aka, Peristalsis) is the term used to describe the movement of food from the time it enters via the mouth until it leaves the body. This movement, the researchers found, is regulated by interactions between diet, the enteric nervous system (ENS) and the gut microbiota via processes that include bile acid metabolism.
Sex, Diet, and Lifestyle All Affect Treatment for Gastrointestinal Diseases
The Dey Laboratory researchers also discovered that sex was a significant variable in determining transit times with males having larger pro-motility effects.
“Our results suggest that strategies for treating or preventing gastrointestinal diseases may need to be tailored to sex and to biogeography of the gut,” they wrote. “While targeting the microbiome and the ENS is justified, our observation of significant transcriptional responses to defined interventions in a highly controlled gnotobiotic setting also highlights challenges to clinical translation.”
The researchers concluded that:
Gut microbiome-generated bile acids regulate colonic transit via TGR5 protein.
Lithocholic acid (LCA) had the largest colonic pro-motility effect.
Bile acids exert sex-biased effects on gut transit times.
Enteric nervous system (ENS) transcriptional responses are regional- and microbiome-specific.
“The human experience—which reflects the aggregate effects of the innumerable dietary ingredients that we consume daily, the hugely diverse metabolically dynamic microbes that inhabit our guts, our own digestive processes, and the interactions of all of the above that result in thousands of gut metabolites—entails significantly more complex and variable transcriptional responses to environmental cues,” the Dey Laboratory scientists concluded.
To perform their research, the scientists developed both high and low BSH (bile salt hydrolase) bacterial communities for germ-free mice, which are known to exhibit slower gut motility and less complex bile acid profiles than colonized animals. (See graphic above taken from the Dey Laboratory published paper.)
The spice turmeric and dyes were added to the diets of the mice to track gut motility. The mice that were given the BSH-high microbiota had higher fecal concentrations of unconjugated bile acids than those given the BSH-low form of the microbiota. The mice given the BSH-high version also experienced faster transit times, according to the researchers’ iScience paper.
The researchers also concluded that the BSH-high group had greater fecal concentrations of lithocholic acid (LCA) which indicates variations in bile acid metabolism might affect gut transit.
When the scientists infused bile acids directly into mouse colons, variable acids reacted differently with LCA having the fastest transit times. The researchers hypothesized that LCA might signal through a bile receptor known as TGR5 which blocked the effects of LCA on colonic transit times. TGR5, also called G protein-coupled bile acid receptor, functions as a cell surface receptor for bile acids.
The Dey Laboratory team developed a method to measure expression changes in ENS genes and found that neither BSH activity nor gut transit phenotypes were major drivers of gene expression changes. They found that the location of the gut segment, or biogeography, was the leading contributor to ENS signature variance between samples.
“We expected to see shared host transcriptional responses in mice harboring communities with similar metabolic profiles. However, we did not see this for the most part,” explained gastroenterologist Neelendu Dey, MD (above), a physician/scientist and Assistant Professor, Clinical Research Division, at Fred Hutchinson Cancer Research Center, in the press release. “If anything, shared responses were regional, and these signatures did not cluster by BSH/motility phenotypes.” (Photo copyright: Seattle Cancer Care Alliance.)
The scientists “identified consortium-specific transcriptional changes in genes involved in ENS signaling, development, maintenance, and bile acid metabolism, and these differed across regions of the GI tract. Together these findings indicate that ENS transcriptional responses are regional and microbiome-specific,” according to the Fred Hutch press release.
“This remains a confusing part of the story for us—how is it that we can see predictable host motility responses when colonizing the guts of gnotobiotic mice with phenotypically defined communities, but the middle-man (the host enteric nervous system) appears to have such varied responses?” the Dey Laboratory researchers noted in the press release.
“It suggests that gut motility phenotypes that appear similar may in fact represent (when we look under the hood) diverse host physiologic phenotypes that we are just beginning to understand,” they added.
The results of this study could have potential implications for the precision medicine diagnosis and treatment of gastrointestinal illnesses.
Blue Poop Challenge
Earlier this year, people were encouraged to participate in the “blue poop challenge” conducted by research company ZOE Global Limited (ZOE) to determine how long it takes food to travel through the body.
For the Blue Poop Challenge, individuals are asked to eat blue muffins and then report on the company’s website as to how long it took for the blue dye to appear in their stools.
The purpose of this ongoing study is to reveal pertinent information about an individual’s gut health and microbiome.
Since 2010, Dark Daily has reported on dozens of research studies and innovative developments involving human microbiome and gut bacteria and their critical importance in the development of clinical laboratory testing, drug therapies, and precision medicine.
These studies’ findings could lead to improved immune system therapeutics and associated clinical laboratory tests.
“All of this suggests the potential in the future for clinical laboratories and microbiologists to do microbiome testing in support of clinical care,” said Robert Michel, Editor-in-Chief of Dark Daily and its sister publication The Dark Report.
More research is needed in these areas. But gut bacteria and the human microbiome are an integral part of our health and wellbeing. It is worth keeping an eye on new developments in those fields of study.
As the worldwide demand for histopathology services increases faster than the increase in the number of anatomic pathologist and histopathologists, a DP platform that suggests courses of treatments may be a boon to cancer diagnostics
Europe may become Ground Zero for the widespread adoption of whole-slide imaging (WSI), digital pathology (DP) workflow, and the use of image-analysis algorithms to make primary diagnoses of cancer. Several forward-looking histopathology laboratories in different European countries are moving swiftly to adopt these innovative technologies.
Clinical laboratories and anatomic pathology groups worldwide have watched digital pathology tools evolve into powerful diagnostic aids. And though not yet employed for primary diagnoses, thanks to artificial intelligence (AI) and machine learning many DP platforms are moving closer to daily clinical use and new collaborations with pathologists who utilize the technology to confirm cancer and other chronic diseases.
Now, Swiss company Unilabs, one of the largest laboratory, imaging, and pathology diagnostic developers in Europe, and Israel-based Ibex Medical Analytics, developer of AI-based digital pathology and cancer diagnostics, have teamed together to deploy “Ibex’s multi-tissue AI-powered Galen platform” across 16 European nations, according to a Unilabs press release.
Though not cleared by the federal Food and Drug Administration (FDA) for clinical use in the US, the FDA recently granted Breakthrough Device Designation to Ibex’s Galen platform. This designation is part of the FDA’s Breakthrough Device Program which was created to help expedite the development, assessment, and review of certain medical devices and products that promise to provide for more effective treatment or diagnosis of life-threatening or irreversibly debilitating diseases or conditions.
Benefits of AI-Digital Pathology to Pathologists, Clinical Labs, and Patients
According to Ibex’s website, the Galen DP platform uses AI algorithms to analyze images from breast and prostate tissue biopsies and provide insights that help pathologists and physicians determine the best treatment options for cancer patients.
This will, Ibex says, give pathologists “More time to dedicate to complex cases and research,” and will make reading biopsies “Less tedious, tiring, and stressful.”
Patients, according to Ibex, benefit from “Increased diagnostic accuracy” and “More objective results.”
And pathology laboratories benefit from “Increased efficiency, decreased turnaround time, and improved quality of service,” Ibex claims.
According to the press release, AI-generated insights can include “case prioritization worklists, cancer heatmaps, tumor grading and measurements, streamlined reporting tools and more.”
This more collaborative approach between pathologists and AI is a somewhat different use of digital pathology, which primarily has been used to confirm pathologists’ diagnoses, rather than helping to identify cancer and suggest courses of treatment to pathologists.
“This cutting-edge AI technology will help our teams quickly prioritize urgent cases, speed up diagnosis, and improve quality by adding an extra set of digital eyes,” said Christian Rebhan, MD, PhD (above), Chief Medical and Operations Officer at Unilabs, in the press release. “When it comes to cancer, the earlier you catch it, the better the prognosis—so getting us critical results faster will help save lives.” (Photo copyright: Unilabs.)
AI-based First and Second Reads
The utilization of the Galen platform will first be rolled out nationally in Sweden and then deployed in sixteen other countries. The AI-based DP platform is CE marked in the European Union for breast and prostate cancer detection in multiple workflows.
“The partnership with Ibex underlines Unilabs’ pioneering role in Digital Pathology and represents yet another step in our ambition to become the most digitally-enabled provider of diagnostic services in Europe,” Rebhan stated.
The Ibex website explains that the Galen platform is divided into two parts—First Read and Second Read:
The First Read “is an AI-based diagnostics application that aims to help pathologists significantly reduce turnaround time and improve diagnostic accuracy. The application uses a highly accurate AI algorithm to analyze slides prior to the pathologist and provides decision support tools that enable focusing on cancerous slides and areas of interest, streamline reporting, improve lab efficiency, and increase diagnostic confidence.”
The Second Read “is an AI-based diagnostics and quality control application that helps pathologists enhance diagnostic accuracy with no impact on routine workflow. The application analyzes slides in parallel with the pathologist and alerts in case of discrepancies with high clinical significance (e.g., a missed cancer), thereby providing a safety net that reduces error rates and enables a more efficient workflow.”
“Ibex is transforming cancer diagnosis with innovative AI solutions across the diagnostic pathway,” said Joseph Mossel, Chief Executive Officer and co-founder of Ibex, in the press release. “We are excited to partner with Unilabs to deploy our AI solutions and empower their pathologists with faster turnaround times and quality diagnosis. This cooperation follows a thorough evaluation of our technology at Unilabs and demonstrates the robustness and utility of our platform for everyday clinical practice.”
Use of AI in Pathology Increases as Number of Actual Pathologists Declines
Developers like Unilabs and Ibex believe that DP platforms driven by AI image analysis algorithms can help pathologists be more productive and can shorten the time it takes for physicians to make diagnoses and issue reports to patients.
This may be coming at a critical time. As nations around the globe face increasing shortages of pathologists and histopathologists, the use of AI in digital pathology could become more critical for disease diagnosis and treatment.
A 2019 Medscape survey stated that “One-third of active pathologists are burned out,” and that many pathologists are on the road to retirement.
And in the same year, Fierce Healthcare noted that in a 2013 study, “researchers found that more than 40% of pathologists were 55 or older. They predicted that retirements would reach their apex in 2021. Consequently, by the end of next decade, the United States will be short more than 5,700 pathologists.”
Dark Daily previously reported on the growing global shortage of pathologists going back to 2011.
Even China is struggling to keep up with demand for anatomic pathologists. In 2017, Dark Daily wrote, “China is currently facing a severe shortage of anatomic pathologists, which blocks patients’ access to quality care. The relatively small number of pathologists are often overworked, even as more patients want access to specialty care for illnesses. Some hospitals in China do not even have pathologists on staff. Thus, they rely on understaffed anatomic pathology departments at other facilities, or they use imaging only for diagnoses.”
Thus, it may be time for an AI-driven digital platform to arrive that can speed up and increase the accuracy of the cancer diagnostics process for pathologists, clinical laboratories, and patients alike.
There are multiple companies rapidly developing AI, machine learning, and image analysis products for diagnosing diseases. Pathologists should expect progress in this field to be ongoing and new capabilities regularly introduced into the market.
Unlike most other CRISPR/Cas-9 therapies that are ex vivo treatments in which cells are modified outside the body, this study was successful with an in vivo treatment
Use of CRISPR-Cas9 gene editing technology for therapeutic purposes can be a boon for clinical laboratories. Not only is this application a step forward in the march toward precision medicine, but it can give clinical labs the essential role of sequencing a patient’s DNA to help the referring physician identify how CRISPR-Cas9 can be used to edit the patient’s DNA to treat specific health conditions.
Most pathologists and medical lab managers know that CRISPR-Cas9 gene editing technology has been touted as one of the most significant advances in the development of therapies for inherited genetic diseases and other conditions. Now, a pair of biotech companies have announced a milestone for CRISPR-Cas9 with early clinical data involving a treatment delivered intravenously (in vivo).
As with other therapies, determining which patients are suitable candidates for specific treatments is key to the therapy’s success. Therefore, clinical laboratories will play a critical role in identifying those patients who would most likely benefit from a CRISPR-delivered therapy.
Such is the goal of precision medicine. As methods are refined that can correct unwelcome genetic mutations in a patient, the need to do genetic testing to identify and diagnose whether a patient has a specific gene mutation associated with a specific disease will increase.
Cleveland Clinic describes ATTR amyloidosis as a “protein misfolding disorder” involving transthyretin (TTR), a protein made in the liver. The disease leads to deposits of the protein in the heart, nerves, or other organs.
According to Intellia and Regeneron, NTLA-2001 is designed to inactivate the gene that produces the protein.
The interim clinical trial data indicated that one 0.3 mg per kilogram dose of the therapy reduced serum TTR by an average of 87% at day 28. A smaller dose of 0.1 mg per kilogram reduced TTR by an average of 52%. The researchers reported “few adverse events” in the six study patients, “and those that did occur were mild in grade.”
Current treatments, the companies stated, must be administered regularly and typically reduce TTR by about 80%.
“These are the first ever clinical data suggesting that we can precisely edit target cells within the body to treat genetic disease with a single intravenous infusion of CRISPR,” said Intellia President and CEO John Leonard, MD, in a press release. “The interim results support our belief that NTLA-2001 has the potential to halt and reverse the devastating complications of ATTR amyloidosis with a single dose.”
He added that “solving the challenge of targeted delivery of CRISPR-Cas9 to the liver, as we have with NTLA-2001, also unlocks the door to treating a wide array of other genetic diseases with our modular platform, and we intend to move quickly to advance and expand our pipeline.”
“It’s an important moment for the field,” MIT biomedical engineer Daniel Anderson, PhD (above), told Nature. Anderson is Professor, Chemical Engineering and Institute for Medical Engineering and Science at the Koch Institute for Integrative Cancer Research at MIT. “It’s a whole new era of medicine,” he added. Advances in the use of CRISPR-Cas9 for therapeutic purposes will create the need for clinical laboratories to sequence patients’ DNA to help physicians determine the best uses for a CRISPR-Cas9 treatment protocol. (Photo copyright: Massachusetts Institute of Technology.)
In Part 2 of the Phase 1 trial, Intellia plans to evaluate the new therapy at higher doses. After the trial is complete, “the company plans to move to pivotal studies for both polyneuropathy and cardiomyopathy manifestations of ATTR amyloidosis,” the press release states.
Previous clinical trials reported results for ex vivo treatments in which cells were removed from the body, modified with CRISPR-Cas9 techniques, and then reinfused. “But to be able to edit genes directly in the body would open the door to treating a wider range of diseases,” Nature reported.
How CRISPR-Cas9 Works
On its website, CRISPR Therapeutics, a company co-founded by Emmanuelle Charpentier, PhD, a director at the Max Planck Institute for Infection Biology in Berlin, and inventor of CRISPR-Cas9 gene editing, explained that the technology “edits genes by precisely cutting DNA and then letting natural DNA repair processes take over.” It can remove fragments of DNA responsible for causing diseases, as well as repairing damaged genes or inserting new ones.
The therapies have two components: Cas9, an enzyme that cuts the DNA, and Guide RNA (gRNA), which specifies where the DNA should be cut.
Charpentier and biochemist Jennifer Doudna, PhD, Nobel Laureate, Professor of Chemistry, Professor of Biochemistry and Molecular Biology, and Li Ka Shing Chancellor’s Professor in Biomedical and Health at the University of California Berkeley, received the 2020 Nobel Prize in Chemistry for their work on CRISPR-Cas9, STAT reported.
It is important to pathologists and medical laboratory managers to understand that multiple technologies are being advanced and improved at a remarkable pace. That includes the technologies of next-generation sequencing, use of gene-editing tools like CRISPR-Cas9, and advances in artificial intelligence, machine learning, and neural networks.
At some future point, it can be expected that these technologies will be combined and integrated in a way that allows clinical laboratories to make very early and accurate diagnoses of many health conditions.
