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.
By rethinking how their medical labs relate to health insurers, physicians, and patients, a handful of progressive lab companies are enjoying increased revenue while also lifting patient and payer satisfaction
There is widespread agreement across the clinical laboratory industry that it is becoming ever more difficult to have health plans reimburse claims for common tests, molecular assays, and genetic tests in a reliable and consistent manner. Many lab companies report that they are experiencing high rates of denied claims. Moreover—even for claims reimbursed by payers—the amount paid will vary on claims for the same type of lab test.
“Essentially, on this point, the consistent theme we hear from many lab companies—particularly those labs with a menu of proprietary, specialized molecular and genetic tests—is that it is now almost a crap-shoot to submit lab test claims to many payers and see timely and predictable reimbursement for those claims,” stated Robert L. Michel, Editor-In-Chief of Dark Daily’s sister publication, The Dark Report. “One could say that, today, the function of billing patients and payers for clinical lab testing has become financial quicksand for most labs. By following traditional coding, billing, and collection practices, in today’s healthcare market, they find themselves sinking steadily deeper in this financial quicksand.” (more…)
Confusion, unhappiness, and many unresolved issues remain about the way government and private payers are handling claims for molecular diagnostic tests covered by the 114 new CPT codes
Dust is settling from the fiasco triggered by the Medicare program’s failure to be ready on January 1, 2013, to settle molecular diagnostic test claims filed under the 114 new Tier 1 and Tier 2 molecular CPT codes. The damage is not just limited to Medicare test claims, but also involves private health plans that were waiting to let the Medicare program set precedents on coverage and prices for the new molecular test codes.
Many Clinical Laboratories Must Cope With an Unsatisfactory Situation
Although federal Medicare officials and Medicare contractors have scrambled to rectify the situation, even today there is much unhappiness across the clinical laboratory industry about the current state of things. That unhappiness extends to state Medicaid and private payers because many of these payers have been slow to publish coverage guidelines and prices for these new molecular test CPT codes.
Medicare contractors are setting prices that are 40% to 60% lower than they paid medical laboratories last year for these same molecular diagnostic tests
A lack of payment for these claims, have forced some clinical laboratories and pathology groups to stop doing molecular testing and lay off staff. At least one lab company shut its doors, blaming non-payment by its Medicare contractor as the primary reason. (more…)
