WASE-COVID Study also found that use of artificial intelligence technology minimized variability among echocardiogram scan results
Many physicians—including anatomic pathologists—are watching the development of artificial intelligence (AI)-powered diagnostic tools that are intended to analyze images and analyze the data with accuracy comparable to trained doctors. Now comes news of a recent study that demonstrated the ability of an AI tool to analyze echocardiograph images and deliver analyses equal to or better than trained physicians.
Conducted by researchers from the World Alliance Societies of Echocardiography and presented at the latest annual sessions of the American College of Cardiology (ACC), the WASE-COVID Study involved assessing the ability of the AI platform to analyze digital echocardiograph images with the goal of predicting mortality in patients with severe cases of COVID-19.
To complete their research, the WASE-COVID Study scientists examined 870 patients with acute COVID-19 infection from 13 medical centers in nine countries throughout Asia, Europe, United States, and Latin America.
Human versus Artificial Intelligence Analysis
Echocardiograms were analyzed with automated, machine learning-derived algorithms to calculate various data points and identify echocardiographic parameters that would be prognostic of clinical outcomes in hospitalized patients. The results were then compared to human analysis.
All patients in the study had previously tested positive for COVID-19 infection using a polymerase chain reaction (PCR) or rapid antigen test (RAT) and received a clinically-indicated echocardiogram upon admission. For those patients ultimately discharged from the hospital, a follow-up echocardiogram was performed after three months.
“What we learned was that the manual tracings were not able to predict mortality,” Federico Asch, MD, FACC, FASE, Director of the Echocardiography Core Lab at MedStar Health Research Institute in Washington, DC, told US Cardiology Review in a video interview describing the WASE-COVID Study findings.
Asch is also Associate Professor of Medicine (Cardiology) at Georgetown University. He added, “But on the same echoes, if the analysis was done by machine—Ultromics EchoGo Core, a software that is commercially available—when we used the measurements obtained through this platform, we were able to predict in-hospital and out-of-hospital mortality both with ejection fraction and left ventricular longitudinal strain.”
“When compared to the manual reads, the AI algorithms had a much higher predictive value for mortality,” Federico Asch, MD (above), told US Cardiology Review. “Indeed, they were predictive where the manual ones were not.” These findings may have implications in the development and adoption of artificial intelligence driven clinical laboratory diagnostics and for predicting risk of COVID-19 deaths in hospitalized heart patients. Click here to review the entire video interview. (Photo copyright: US Cardiology Review.)
Nearly half of the 870 hospitalized patients were admitted to intensive care units, 27% were placed on ventilators, 188 patients died in the hospital, and 50 additional patients died within three to six months after being released from the hospital.
10 of 13 medical centers performed limited cardiac exams as their primary COVID in-patient practice and three out of the 13 centers performed comprehensive exams.
In-hospital mortality rates ranged from 11% in Asia, 19% in Europe, 26% in the US, to 27% in Latin America.
Left ventricular longitudinal strain (LVLS), right ventricle free wall strain (RVFWS), as well as a patient’s age, lactic dehydrogenase levels and history of lung disease, were independently associated with mortality. Left ventricle ejection fraction (LVEF) was not.
Fully automated quantification of LVEF and LVLS using AI minimized variability.
AI-based left ventricular analyses, but not manual, were significant predictors of in-hospital and follow-up mortality.
The WASE-COVID Study also revealed the varying international use of cardiac ultrasound (echocardiography) on COVID-19 patients.
“By using machines, we reduce variability. By reducing variability, we have a better capacity to compare our results with other outcomes, whether that outcome in this case is mortality or it could be changes over time,” Asch stated in the US Cardiology Review video. “What this really means is that we may be able to show associations and comparisons by using AI that we cannot do with manual [readings] because manual has more variation and is less reliable.”
He said the next steps will be to see if the findings hold true when AI is used in other populations of cardiac patients.
COVID-19 Pandemic Increased Need for Swift Analyses
An earlier WASE Study in 2016 set out to answer whether normal left ventricular heart chamber quantifications vary across countries, geographical regions, and cultures. However, the data produced by that study took years to review. Asch said the COVID-19 pandemic created a need for such analysis to be done more quickly.
“When the pandemic began, we knew that the clinical urgency to learn as much as possible about the cardiovascular connection to COVID-19 was incredibly high, and that we had to find a better way of securely and consistently reviewing all of this information in a timely manner,” he said in the Ultromics new release.
Coronary artery disease (CAD) is the most common form of heart disease and affects more than 16.5 million people over the age of 20. By 2035, the economic burden of CAD will reach an estimated $749 billion in the US alone, according to the Ultromics website.
“COVID-19 has placed an even greater pressure on cardiac care and looks likely to have lasting implications in terms of its impact on the heart,” said Ross Upton, PhD, Founder and CEO of Oxford, UK-based Ultromics, in a news release announcing the US Food and Drug Administration’s 510(k) clearance for the EchoGo Pro, which supports clinicians’ diagnosing of CAD. “The healthcare industry needs to quickly pivot towards AI-powered automation to reduce the time to diagnosis and improve patient care.”
Use of AI to analyze digital pathology images is expected to be a fast-growing element in the anatomic pathology profession, particularly in the diagnosis of cancer. As Dark Daily outlined in this free white Paper, “Anatomic Pathology at the Tipping Point? The Economic Case for Adopting Digital Technology and AI Applications Now,” anatomic pathology laboratories can expect adoption of AI and digital technology to gain in popularity among pathologists in coming years.
Determining how dogs do this may lead to biomarkers for new clinical laboratory diagnostics tests
Development of new diagnostic olfactory tools for prostate and other cancers is expected to result from research now being conducted by a consortium of researchers at different universities and institutes. To identify new biomarkers, these scientists are studying how dogs can detect the presence of prostate cancer by sniffing urine specimens.
Funded by a grant from the Prostate Cancer Foundation, the pilot study demonstrated that dogs could identify prostate samples containing cancer and discern between cancer positive and cancer negative samples.
Canine Olfactory Combined with Artificial Intelligence Analysis Approach
The part of a canine brain that controls smell is 40 million times greater than that of humans. Some dog breeds have 300 to 350 million sensory receptors, compared to about five million in humans. With their keen sense of smell, dogs are proving to be vital resources in the detection of some diseases.
The pilot study examined how dogs could be trained to detect prostate cancer in human urine samples.
Claire Guest, CEO and Chief Scientific Officer of UK-based Medical Detection Dogs and one of the study authors, is shown above with one of her cancer detecting dogs. In a Prostate Cancer Foundation article, she said, “Prostate cancer is not going to turn out to be a single note. What dogs are really good at discovering is a tune. Think of Beethoven’s Fifth Symphony, those first few notes. We suspect the cancer signature is something like that. It’s a pattern; the dogs are really good at recognizing the pattern. Machines that recognize the notes but can’t read the pattern are not reliable biomarkers,” she noted. The researchers believe the best solution for developing a clinical laboratory diagnostic that detects prostate cancer may be a combined approach using canine olfaction and AI neural networks. (Photo copyright: Janine Warwick/NPR.)
To perform the study, the researchers trained two dogs to sniff urine samples from men with high-grade prostate cancer and from men without the cancer. The two dogs used in the study were a four-year-old female Labrador Retriever named Florin, and a seven-year-old female wirehaired Hungarian Vizsla named Midas. The dogs were trained to respond to cancer-related chemicals, known as volatile organic compounds, or VOCs, the researchers added to the urine samples, and to not respond to the samples without the VOCs.
Both dogs performed well in their cancer detection roles, and both successfully identified five of seven urine samples from men with prostate cancer, correlating to a 71.4% accuracy rate. In addition, Florin correctly identified 16 of 21 non-aggressive or no cancer samples for an accuracy rate of 76.2% and Midas did the same with a 66.7% accuracy rate.
“We wondered if having the dogs detect the chemicals, combined with analysis by GC-MS, bacterial profiling, and an artificial intelligence (AI) neural network trained to emulate the canine cancer detection ability, could significantly improve the diagnosis of high-grade prostate cancer,” said Alan Partin, MD, PhD, Professor of Urology, Pathology and Oncology, Johns Hopkins University School of Medicine and one of the authors of the study, told Futurity.
The researchers determined that canine olfaction was able to distinguish between positive and negative prostate cancer in the samples, and the VOC and microbiota profiling analyses showed a qualitative difference between the two groups. The multisystem approach demonstrated a more sensitive and specific way of detecting the presence of prostate cancer than any of the methods used by themselves.
In their paper, the researchers concluded that “this study demonstrated feasibility and identified the challenges of a multiparametric approach as a first step towards creating a more effective, non-invasive early urine diagnostic method for the highly aggressive histology of prostate cancer.”
Can Man’s Best Friend be Trained to Detect Cancer and Save Lives?
Prostate cancer is the second leading cause of cancer deaths among men in the developed world. And, according to data from the National Cancer Institute, standard clinical laboratory blood tests, such as the prostate-specific antigen (PSA) test for early detection, sometimes miss the presence of cancer.
Establishing an accurate, non-invasive method of sensing the disease could help detect the disease sooner when it is more treatable and save lives.
The American Cancer Society estimates that there will be about 248,530 new cases of prostate cancer diagnosed in 2021 and that there will be approximately 34,130 deaths resulting from the disease during the same year.
Of course, more testing will be needed before Man’s best friend can be put to work detecting cancer in medical environments. But if canines can be trained to detect the disease early, and in a non-invasive way, more timely diagnosis and treatment could result in higher survival rates.
Meanwhile, as researchers identify the elements dogs use to detect cancer and other diseases, this knowledge can result in the creation of new biomarkers than can be used in clinical laboratory tests.
Former CEO also testified that she believed company’s proprietary blood-testing technology could perform ‘any’ clinical laboratory blood test
One relevant question in the federal fraud trial of ex-Theranos CEO Elizabeth Holmes was whether she would testify on her own behalf. That question was answered shortly after the government rested its criminal fraud case against the former Silicon Valley clinical laboratory testing company founder. Holmes took the stand in her own defense, a risk her defense team hopes will pay off in her favor.
During her first three days of testimony leading up to the Thanksgiving holiday break, Holmes—who faces 11 counts of fraud and conspiracy related to her tenure as founder and CEO of Theranos—made headlines by admitting she did personally put the logos of pharmaceutical giants Pfizer and Schering-Plough on reports she sent to Theranos investors and executives at Walgreens and Safeway. She expressed regret for doing so to the jury, but claimed her intent was not to deceive but to give credit to others.
“This work was done in partnership with these companies, and I was trying to convey that,” she testified, according to a trial coverage from Ars Technica.
When asked if she realized that others would assume the pharmaceutical companies—not Theranos—were the authors of the report, Holmes replied, “I’ve heard that testimony in this case, and I wish I’d done it differently.”
If found guilty, Holmes—who once claimed Theranos’ Edison proprietary blood-testing technology would to be able to complete as many as 200 clinical laboratory tests using a single finger-stick of blood—could face maximum penalties of 20 years in prison, a $2.75 million fine, and possible restitution.
The illustration above depicts ex-Theranos CEO Elizabeth Holmes testifying on her own behalf. Former Santa Clara County prosecutor Steven Clark, JD, told The Mercury News, “(T)he best person to say what Elizabeth Holmes’ intent was is Elizabeth Holmes, and that’s why I think she’s taking the stand. … I think the jury will like her.” That remains to be seen. But there’s no doubt that Clinical Laboratory Directors should take a strong interest in the outcome of this trail. (Graphic copyright: Vicki Behringer/Reuters.)
Holmes Testifies She Believed the Edison Device Could Perform “Any” Blood Test
In its trail coverage, NPR described Holmes’ first three days of testimony “as having involved deflecting responsibility, pointing to the expertise of the Theranos board of directors, lab staff, and other company employees whom Holmes has suggested were close to how [Theranos’] blood analyzers worked.”
According to Reuters, Holmes’ defense team is arguing that Holmes’ always-rosy forecasts about her company’s technology and finances were based on her belief the proprietary Edison device worked as advertised, which, in turn, was based on feedback from pharmaceutical companies, her own employees, and the military.
During her testimony, Holmes compared a traditional blood-testing device to Theranos’ “3.0” device, which she said would reduce the human-error rate that can occur during blood sampling.
“If we had the ability to automate much of that process, we could reduce the error associated with traditional lab testing,” she told the court.
Reuters reported that Holmes told jurors her confidence in the Theranos device was in part due to how well the unit had performed in studies completed in 2008 and 2009, including those run by drug companies such as Novartis.
The Mercury News described Holmes as speaking with “confidence—and frequently a small smile”—during her opening day of testimony.
Asked by one of her lawyers, “Did you believe that Theranos had developed technology that was capable of performing any blood test?” Holmes responded, “I did.”
Holmes Testifies about Military’s Alleged Use of Edison Device
Prosecutors maintain that Holmes knew Theranos’ proprietary blood-testing technology had serious accuracy issues yet lied about its capabilities and use to lure investors. One of those false claims included allegedly stating the US military was using the Edison device on the battlefield. Earlier in the trial, CNBC reported, prosecution witness Brian Grossman, Chief Investment Officer at PFM Health Sciences, which invested $96 million into Theranos, testified he was told in a 2013 meeting with Holmes and Balwani that Theranos technology was being used in medical-evacuation helicopters.
However, on the witness stand, Holmes described Theranos’ projects with the US military as much more limited in scope than the descriptions outlined by investors testifying for the prosecution.
According to The Wall Street Journal (WSJ), Holmes told jurors a 2010 partnership between Theranos and a US Army Institute of Surgical Research doctor in Texas looked into using the Theranos device to measure blood markers to detect kidney performance. A second project involved the military’s Africa Command, which was determining whether the device could withstand high temperatures. Holmes testified the devices used in Africa “held up well,” though some modifications were needed, and some issues were revealed with the touchscreen.
Should Holmes Have Testified on Her Own Behalf?
Trial experts maintain Holmes’ decision to testify in her own defense could backfire.
“It’s always a risk to put your client on because if they make a mistake they can sink the whole case,” former Santa Clara County prosecutor Steven Clark, JD, told The Mercury News. He added, “what’s at issue here is Elizabeth Holmes’ intent. And the best person to say what Elizabeth Holmes’ intent was is Elizabeth Holmes, and that’s why I think she’s taking the stand. She’s very charismatic. She’s really good on her feet. And I think the jury will like her.
“This is the pitch meeting of her life,” Clark added. “She’s going to be explaining herself to 12 people as to what was in her mind.”
Judge Drops One Count Due to Prosecution Error, Government Rests Its Case
Holmes is now charged with nine counts of wire fraud and two counts of conspiracy to commit wire fraud after the government dropped one count of fraud from the indictment. According to WSJ coverage of the trial, US District Judge Edward Davila blocked a patient named in the indictment as “B.B.” from testifying because of a filing error by the prosecution. The judge’s decision resulted in the government dropping one count.
The government rested its case against Holmes on November 19 following testimony from independent journalist Roger Parloff, who wrote a flattering 2014 Fortune magazine story on Holmes. He later redacted his earlier writing in another Fortune article, titled, “How Theranos Misled Me.”
The government alleged Holmes used media publicity as part of her scheme to defraud investors, patients, and physicians. All totaled, 29 witnesses appeared for the prosecution, the WSJ reported.
Former Theranos Chief Operating Officer Ramesh “Sunny” Balwani—Holmes’ one-time boyfriend—faces similar charges of defrauding patients, investors, and physicians. His trial is expected to begin in January 2022.
Clinical laboratory managers and pathologists who have watched the federal court proceedings with keen interest should expect the trial to wrap up at the conclusion of Holmes’ testimony, just in time for the Balwani fraud trial to begin.
Jury also heard testimony about Holmes’ claims that the Edison device was doing clinical laboratory testing for the military in overseas theaters
During the seventh week of ex-Theranos CEO Elizabeth Holmes’ criminal fraud trial, headline-making testimony continued nearly non-stop. A former Theranos product manager took the stand offering damning testimony that tied Holmes to questionable product demonstrations and exaggerated claims about the military’s use of the Edison blood-testing device. And a Pfizer scientist testified to alleged improper use of the Pfizer logo by Theranos in a report that went to Walgreen executives.
Those claims contributed to the federal Securities and Exchange Commission (SEC) charging Holmes in 2018 with fraud and stripping her of control of Theranos, the SEC stated in a news release.
CNN reported that former Senior Product Manager Daniel Edlin, who worked at Theranos from 2011-2016, acknowledged in court that the Edison device had never been used in a war zone or installed on a medivac helicopter. He also noted that Holmes had final say over his communications with the DOD.
According to CNN, “Edlin said he worked directly with Holmes to support the relationships with the military and Defense Department. He said, ‘the end goal’ for these discussions ‘was to start a research program that would compare Theranos’ testing to the testing available to the military at that time.”
Edlin testified that Holmes was ‘highly involved’ with these communications, CNN reported.
“I’d say any substantive communication I had with the military, I either discussed with her ahead of time … or email drafts were reviewed and approved before I sent them back out,” he testified.
Elizabeth Holmes, ex-CEO of now-defunct blood-testing company Theranos, is seen above leaving a San Jose, Calif., courthouse following a hearing. Holmes faces 10 counts of wire fraud and two counts of conspiracy to commit wire fraud for allegedly misleading investors, clinical laboratories, patients, and healthcare providers about Theranos’ proprietary blood-testing Edison technology. Holmes has pleaded not guilty. (Photo copyright: Reuters.)
During cross examination, Edlin walked back some of his damaging testimony. When asked by defense attorney Kevin Downey, JD, of Williams and Connelly, LLP, if he or anyone else at Theranos was intentionally trying to deceive investors or other visitors during the demonstrations, Edlin responded, “Of course not,” according to Palo Alto Online.
To counter the prosecution’s claims that Theranos’ Edison machines were unsuitable for military use because they could not operate in high temperatures, Downey introduced an email from an Army doctor at the US Command in Africa praising the Edison after examining it in high temperatures. The doctor also, according to court documents, proposed the Army provide more funding to test the Edison’s capabilities, Palo Alto Online reported.
Nevertheless, according to The Wall Street Journal (WSJ), the Edison was never sent to a US military laboratory in Afghanistan for study, nor was it used in Africa to run blood tests.
Former Pfizer Scientist Testifies to Misuse of Intellectual Property
In another broadside to the Holmes defense, former Pfizer scientist Shane Weber, PhD, testified Holmes used the Pfizer logo in investor materials without the company’s permission in order to pass off as credible a study aimed at validating the Edison device.
The WSJ reported Weber told jurors that in 2008 he had reviewed a 15-page Theranos study involving cancer patients, but that he had stated in his own internal report to Pfizer at that time that nine conclusions in the study—including a statement that the “Theranos system performed with superior performance”—were “not believable.” Pfizer eventually heeded Weber’s advice to not enter into a partnership with Theranos.
Prosecutors stated that as part of Theranos’ negotiations with Walgreens, which ultimately invested $140 million in the blood-testing company, Holmes had placed a Pfizer logo on the top of each page of the cancer study report before sending it to Walgreens executives, claiming it was an independent due-diligence report on Theranos technology.
Weber told jurors that he had not known about the altered report until he was shown the document by prosecutors. He stated the logo was added without Pfizer’s permission, the WSJ reported.
Unfortunately for Walgreens, the retail pharmacy chain entered into a business agreement with Theranos without extensively examining or testing the Edison device, which Theranos had claimed could quickly and accurately run 200 diagnostic tests using a finger-stick of blood. Instead, the company relied on the opinions of its own staff healthcare experts and outside experts, none of whom fully tested the technology either, the WSJ stated.
Testimony in the Elizabeth Holmes fraud trial is expected to continue through December. Therefore, clinical laboratory managers and pathologists should expect headline-making news to continue as well. Dark Daily will continue its coverage as the trial moves forward.
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.
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.
