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.
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.
In “JAMA Study: 17% Fewer Pathologists Since 2007,” Dark Daily’s sister publication The Dark Report covered research published in the Journal of the American Medical Association (JAMA) which showed that between 2007 and 2017 the number of pathologists in the US decreased by 18% and that the workload per pathologist rose by almost 42% during the same decade.
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.
In “Critical Shortage of Pathologists in Africa Triggers Calls for More Training Programs and Incentives to Increase the Number of Skilled Histopathologists,” we noted that a critical shortage of pathologists in southern Africa is hindering the ability of medical laboratories in the region to properly diagnose and classify diseases.
In “Severe Shortage of Pathologists Threatens Israel’s Health System—Especially Cancer Testing,” Dark Daily reported that inadequate numbers of pathologists would soon threaten the quality and integrity of clinical pathology laboratory testing in the nation of Israel.
And in “Shortage of Histopathologists in the United Kingdom Now Contributing to Record-Long Cancer-Treatment Waiting Times in England,” we reported how a chronic shortage of histopathologists in the UK is being blamed for cancer treatment waiting times that now reach the worst-ever levels, as National Health Service (NHS) training initiatives and other steps fail to keep pace with growing demand for diagnostic services.
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.