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New AI-based Digital Pathology Platform Scheduled to Roll Out across Europe Promises Faster Time to Diagnosis, Increased Accuracy, while Improving Pathologists’ Work Lives

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.

Christian Rebhan, MD, PhD

“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.

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.

—JP Schlingman

Related Information

Unilabs Signs Deal with Ibex to Deploy AI-powered Cancer Diagnostics

Industry Voices—the Shortage of Invisible Doctors

Part 1: Doing More with Less—Changing the Face of Pathology

Critical Shortage of Pathologists in Africa Triggers Calls for More Training Programs and Incentives to Increase the Number of Skilled Histopathologists

Severe Shortage of Pathologists Threatens Israel’s Health System—Especially Cancer Testing

Shortage of Histopathologists in the United Kingdom Now Contributing to Record-Long Cancer-Treatment Waiting Times in England

Shortage of Registered Pathologists in India Continues to Put Patients at Risk in Illegal Labs That Defy Bombay Court Orders

China Struggling to Keep Up with Demand for Anatomic Pathologists

JAMA Study: 17% Fewer Pathologists Since 2007

UPMC Researchers Develop Artificial Intelligence Algorithm That Detects Prostate Cancer with ‘Near Perfect Accuracy’ in Effort to Improve How Pathologists Diagnose Cancer

Working from tissue slides similar to those used by surgical pathologists, the algorithm accurately detects prostate cancer with an impressive 98% sensitivity

It could be that a new milestone has been reached on the road to using artificial intelligence (AI) to help anatomic pathologists diagnose cancer and other diseases. A research collaboration between a major American university and an Israeli company recently published a study about the ability of an AI algorithm to correctly diagnose prostate cancer.

The collaboration involved researchers at the University of Pittsburgh Medical Center (UPMC) and at Ibex Medical Analytics of Israel. The research team created an AI algorithm dubbed the Galen Prostate (part of the Galen Platform). In the study, the Galen Prostate AI accurately detected prostate cancer with 98% sensitivity and 97% specificity.

Researchers noted that this level of diagnostic sensitivity and specificity was significantly higher, compared to previously tested cancer-detecting algorithms that utilized tissue slides. The UPMC scientists published their findings in The Lancet Digital Health, titled, “An Artificial Intelligence Algorithm for Prostate Cancer Diagnosis in Whole Slide Images of Core Needle Biopsies: A Blinded Clinical Validation and Deployment Study.”

AI Show and Tell in Anatomic Pathology

The scientists trained the Galen Prostate AI to recognize prostate cancer by having it examine images from over a million parts of stained tissue slides taken from patient biopsies. Expert pathologists labeled each image to teach the algorithm how to distinguish between healthy and abnormal tissue. The AI was then tested on 1,600 different tissue slide images that had been collected from 100 patients seen at UPMC who were suspected of having prostate cancer.  

“Humans are good at recognizing anomalies, but they have their own biases or past experience,” said Rajiv Dhir, MD, Chief Pathologist and Vice Chair of Pathology at UPMC Shadyside Hospital, Professor of Biomedical Informatics at University of Pittsburgh, and senior author of the study, in a UPMC news release. “Machines are detached from the whole story. There’s definitely an element of standardizing care.”

Ibex Galen Prostate AI solution
The image above is “of prostate cancer (represented by the heatmap) detected by the Ibex Galen Prostate [AI] solution on a biopsy that was previously diagnosed as benign by the pathologist,” stated an Ibex news release announcing the UPMC study. (Photo copyright: Ibex.)

UPMC Algorithm Goes Beyond Cancer Detection, Exceeds Human Pathologists

The researchers also noted that this is the first algorithm to extend beyond cancer detection. It reported high performance for tumor grading, sizing, and invasion of surrounding nerves—clinically important features of pathology reports.  

“Algorithms like this are especially useful in lesions that are atypical,” Dhir said. “A nonspecialized person may not be able to make the correct assessment. That’s a major advantage of this kind of system.”

The algorithm also flagged six slides as potentially containing abnormal tissue that were not flagged by human pathologists. However, the researchers pointed out that this difference does not mean the AI is better than humans at detecting prostate cancer. It is probable, for example, that the pathologists simply saw enough evidence of malignancy elsewhere in the patients’ samples to recommend treatment.

Other Studies Where AI Detected Prostate Cancer

The UPMC researchers are not the first to use AI to detect prostate cancer. In February, The Lancet Oncology published a study from researchers at Radboud University Medical Center (RUMC) in the Netherlands who developed a deep learning AI system that could determine the aggressiveness of prostate cancer in certain patients. 

For that research, the RUMC scientists collected 6,000 biopsies from more than 1,200 men. They then showed the biopsy images along with the original pathology reports to their AI system. Using deep learning, the AI was able to detect and grade prostate cancer according to the Gleason Grading System (aka, Gleason Score), which is used to rate prostate cancer and choose appropriate treatment options. The Gleason Score ranges from one to five and most cancers obtain a score of three or higher. 

“Systems such as ours can be used in different ways. First, it can be used to screen biopsies and to filter out the easy (benign) cases. This could reduce the workload for pathologists,” said Wouter Bulten, a PhD candidate at Radboud who worked on the study, in an interview with HemOnc Today. “Second, the system can be used as a second opinion after the pathologist’s initial read. The system can flag a case if its opinion differs from that of the pathologist. It also can give feedback during the first read, showing the pathologist where to look. In this case, the pathologist needs only to confirm the opinion of the AI system.” 

Can Today’s AI Outperform Human Pathologists?

In their research, the Radboud team discovered that their AI system was able to achieve pathologist-level performance and, in some cases, even performed better than human pathologists. However, they do not foresee AI replacing the need for pathologists, but rather emerging as another method to use in cancer detection and treatment.  

“We see our system as an additional tool that the pathologist can use. Although our system performs very well, it still makes mistakes,” stated Bulten. “These mistakes are often different from those a human would make. We believe that when you merge the expertise of the pathologist with the second opinion of an AI system, you get the best of both worlds.” 

According to the American Cancer Society, prostate cancer is the second most common cancer among men in the US, after skin cancer. The organization estimates there will be approximately 191,930 new cases of prostate cancer diagnosed and about 33,330 deaths from the disease in the US in 2020. 

Though the UPMC study focused only on prostate cancer, the scientists believe their algorithm can be trained to detect other types of cancer as well. AI in clinical diagnostics is clearly progressing, however more studies will be required. Nevertheless, if AI can truly become a useful tool for anatomic pathologists to detect cancer earlier, we may see a welcomed reduction in cancer deaths.   

—JP Schlingman

Related Information:

Newly Developed AI Capable of Identifying Prostate Cancer with “Near-perfect Accuracy”

An Artificial Intelligence Algorithm for Prostate Cancer Diagnosis in Whole Slide Images of Core Needle Biopsies: A Blinded Clinical Validation and Deployment Study

Artificial Intelligence Identifies Prostate Cancer

The Lancet Reports Outstanding Performance of Ibex Medical Analytics’ AI-based Algorithm in a Study at UPMC

Prostate Cancer Can Now be Diagnosed Better Using Artificial Intelligence

AI System Outperforms Pathologists in Identifying Prostate Cancer Aggressiveness

Automated Deep-learning System for Gleason Grading of Prostate Cancer using Biopsies: A Diagnostic Study

New AI Technology Helps Pathologists Spot Cancer

Hospitals Worldwide Are Deploying Artificial Intelligence and Predictive Analytics Systems for Early Detection of Sepsis in a Trend That Could Help Clinical Laboratories, Microbiologists

CMS Considers Using Artificial Intelligence to Battle Fraud; Medical Laboratories Must Ensure Billing Practices Comply with New Federal Affiliation Regulations

Mobile Device Software Companies Are Developing Smartphone Apps That Use Artificial Intelligence to Test for COVID-19, Potentially Bypassing the Clinical Laboratory Altogether