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Clinical Laboratories and Pathology Groups

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News, Analysis, Trends, Management Innovations for
Clinical Laboratories and Pathology Groups

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IT Experts Demonstrate How AI and Computer Microphones Can Be Used to Figure Out Passwords and Break into Customer Accounts

Clinical laboratories and pathology groups should be on the alert to this new digital threat; telehealth sessions and video conferencing calls particularly vulnerable to acoustic AI attacks

Banks may be the first to get hit by a new form of hacking because of all the money they hold in deposit accounts, but experts say healthcare providers—including medical laboratories—are comparably lucrative targets because of the value of patient data. The point of this hacking spear is artificial intelligence (AI) with increased capabilities to penetrate digital defenses.

AI is developing rapidly. Are healthcare organizations keeping up? The hackers sure are. An article from GoBankingRates titled, “How Hackers Are Using AI to Steal Your Bank Account Password,” reveals startling new AI capabilities that could enable bad actors to compromise information technology (IT) security and steal from customers’ accounts.

Though the article covers how the AI could conduct cyberattacks on bank information, similar techniques can be employed to gain access to patients’ protected health information (PHI) and clinical laboratory databases as well, putting all healthcare consumers at risk.

The new AI cyberattack employs an acoustic Side Channel Attack (SCA). An SCA is an attack enabled by leakage of information from a physical computer system. The “acoustic” SCA listens to keystrokes through a computer’s microphone to guess a password with 95% accuracy.

That’s according to a UK study published in IEEE Xplore, a journal of the IEEE European Symposium on Security and Privacy Workshops, titled, “A Practical Deep Learning-Based Acoustic Side Channel Attack on Keyboards.”

“With recent developments in deep learning, the ubiquity of microphones and the rise in online services via personal devices, acoustic side channel attacks present a greater threat to keyboards than ever,” wrote UK study authors Joshua Harrison, MEng, Durham University; Ehsan Toreini, University of Surrey; and Maryam Mehrnezhad, PhD, University of London.

Hackers could be recording keystrokes during video conferencing calls as well, where an accuracy of 93% is achievable, the authors added.

This nefarious technological advance could spell trouble for healthcare security. Using acoustic SCA attacks, busy healthcare facilities, clinical laboratories, and telehealth appointments could all be potentially compromised.

“The ubiquity of keyboard acoustic emanations makes them not only a readily available attack vector, but also prompts victims to underestimate (and therefore not try to hide) their output,” wrote Joshua Harrison, MEng (above), and his team in their IEEE Xplore paper. “For example, when typing a password, people will regularly hide their screen but will do little to obfuscate their keyboard’s sound.” Since computer keyboards and microphones in healthcare settings like hospitals and clinical laboratories are completely ubiquitous, the risk that this AI technology will be used to invade and steal patients’ protected health information is high. (Photo copyright: CNBC.)

Why Do Hackers Target Healthcare?

Ransomware attacks in healthcare are costly and dangerous. According to InstaMed, a healthcare payments and billing company owned by J.P. Morgan, healthcare data breaches increased to 29.5% in 2021 costing over $9 million. And beyond the financial implications, these attacks put sensitive patient data at risk.

Healthcare can be seen as one of the most desirable markets for hackers seeking sensitive information. As InstaMed points out, credit card hacks are usually quickly figured out and stopped. However, “medical records can contain multiple pieces of personally identifiable information. Additionally, breaches that expose this type of data typically take longer to uncover and are harder for an organization to determine in magnitude.”

With AI advancing at such a high rate, healthcare organizations may be unable to adapt older network systems quickly—leaving them vulnerable.

“Legacy devices have been an issue for a while now,” Alexandra Murdoch, medical data analyst at GlobalData PLC, told Medical Device Network, “Usually big medical devices, such as imaging equipment or MRI machines are really expensive and so hospitals do not replace them often. So as a result, we have in the network these old devices that can’t really be updated, and because they can’t be updated, they can’t be protected.”

Vulnerabilities of Telehealth

In “Penn Medicine Study Shows Telemedicine Can Cut Employer Healthcare Costs by 25%,” Dark Daily reported a study conducted by the Perelman School of Medicine at the University of Pennsylvania (Penn Medicine) which suggested there could be significant financial advantages for hospitals that conduct telehealth visits. This, we projected, would be a boon to clinical laboratories that perform medical testing for telemedicine providers.

But telehealth, according to the UK researchers, may also be one way hackers get past safeguards and into critical hospital systems.

“When trained on keystrokes recorded using the video-conferencing software Zoom, an accuracy of 93% was achieved, a new best for the medium. Our results prove the practicality of these side channel attacks via off-the-shelf equipment and algorithms,” the UK researchers wrote in IEEE Xplore.

“[AI] has worrying implications for the medical industry, as more and more appointments go virtual, the implications of deepfakes is a bit concerning if you only interact with a doctor over a Teams or a Zoom call,” David Higgins, Senior Director at information security company CyberArk, told Medical Device Network.

Higgins elaborated on why healthcare is a highly targeted industry for hackers.

“For a credit card record, you are looking at a cost of one to two dollars, but for a medical record, you are talking much more information because the gain for the purposes of social engineering becomes very lucrative. It’s so much easier to launch a ransomware attack, you don’t even need to be a coder, you can just buy ransomware off of the dark web and use it.”

Steps Healthcare Organizations Should Take to Prevent Cyberattacks

Hackers will do whatever they can to get their hands on medical records because stealing them is so lucrative. And this may only be the beginning, Higgins noted.

“I don’t think we are going to see a slowdown in attacks. What we are starting to see is that techniques to make that initial intrusion are becoming more sophisticated and more targeted,” he told Medical Device Network. “Now with things like AI coming into the mix, it’s going to become much harder for the day-to-day individual to spot a malicious email. Generative AI is going to fuel more of that ransomware and sadly it’s going to make it easier for more people to get past that first intrusion stage.”

To combat these attacks patient data needs to be encrypted, devices updated, and medical staff well-trained to spot cyberattacks before they get out of hand. These SCA attacks on bank accounts could be easily transferable to attacks on healthcare organizations’ patient records.

Clinical laboratories, anatomic pathology groups, and other healthcare facilities would be wise to invest in cybersecurity, training for workers, and updated technology. The hackers are going to stay on top of the technology, healthcare leaders need to be one step ahead of them.

—Ashley Croce

Related Information:

How Hackers Are Using AI to Steal Your Bank Account Password

A Practical Deep Learning-Based Acoustic Side Channel Attack on Keyboards

AI Can Steal Passwords with 95% Accuracy by ‘Listening’ to Keystrokes, Alarming Study Finds

New ‘Deep Learning Attack’ Deciphers Laptop Keystrokes with 95% Accuracy

Can A.I. Steal Your Password? Study Finds 95% Accuracy by Listening to Keyboard Typing

Ransomware in Healthcare: What You Need to Know

Hospital 2040: How Healthcare Cybercrime is Predicted to Escalate

30 Crucial Cybersecurity Statistics (2023): Data, Trends and More

Penn Medicine Study Shows Telemedicine Can Cut Employer Healthcare Costs by 25%

Google DeepMind Says Its New Artificial Intelligence Tool Can Predict Which Genetic Variants Are Likely to Cause Disease

Genetic engineers at the lab used the new tool to generate a catalog of 71 million possible missense variants, classifying 89% as either benign or pathogenic

Genetic engineers continue to use artificial intelligence (AI) and deep learning to develop research tools that have implications for clinical laboratories. The latest development involves Google’s DeepMind artificial intelligence lab which has created an AI tool that, they say, can predict whether a single-letter substitution in DNA—known as a missense variant (aka, missense mutation)—is likely to cause disease.

The Google engineers used their new model—dubbed AlphaMissense—to generate a catalog of 71 million possible missense variants. They were able to classify 89% as likely to be either benign or pathogenic mutations. That compares with just 0.1% that have been classified using conventional methods, according to the DeepMind engineers.

This is yet another example of how Google is investing to develop solutions for healthcare and medical care. In this case, DeepMind might find genetic sequences that are associated with disease or health conditions. In turn, these genetic sequences could eventually become biomarkers that clinical laboratories could use to help physicians make earlier, more accurate diagnoses and allow faster interventions that improve patient care.

The Google engineers published their findings in the journal Science titled, “Accurate Proteome-wide Missense Variant Effect Prediction with AlphaMissense.” They also released the catalog of predictions online for use by other researchers.

Jun Cheng, PhD (left), and Žiga Avsec, PhD (right)

“AI tools that can accurately predict the effect of variants have the power to accelerate research across fields from molecular biology to clinical and statistical genetics,” wrote Google DeepMind engineers Jun Cheng, PhD (left), and Žiga Avsec, PhD (right), in a blog post describing the new tool. Clinical laboratories benefit from the diagnostic biomarkers generated by this type of research. (Photo copyrights: LinkedIn.)

AI’s Effect on Genetic Research

Genetic experiments to identify which mutations cause disease are both costly and time-consuming, Google DeepMind engineers Jun Cheng, PhD, and Žiga Avsec, PhD, wrote in a blog post. However, artificial intelligence sped up that process considerably.

“By using AI predictions, researchers can get a preview of results for thousands of proteins at a time, which can help to prioritize resources and accelerate more complex studies,” they noted.

Of all possible 71 million variants, approximately 6%, or four million, have already been seen in humans, they wrote, noting that the average person carries more than 9,000. Most are benign, “but others are pathogenic and can severely disrupt protein function,” causing diseases such as cystic fibrosis, sickle-cell anemia, and cancer.

“A missense variant is a single letter substitution in DNA that results in a different amino acid within a protein,” Cheng and Avsec wrote in the blog post. “If you think of DNA as a language, switching one letter can change a word and alter the meaning of a sentence altogether. In this case, a substitution changes which amino acid is translated, which can affect the function of a protein.”

In the Google DeepMind study, AlphaMissense predicted that 57% of the 71 million variants are “likely benign,” 32% are “likely pathogenic,” and 11% are “uncertain.”

The AlphaMissense model is adapted from an earlier model called AlphaFold which uses amino acid genetic sequences to predict the structure of proteins.

“AlphaMissense was fed data on DNA from humans and closely related primates to learn which missense mutations are common, and therefore probably benign, and which are rare and potentially harmful,” The Guardian reported. “At the same time, the program familiarized itself with the ‘language’ of proteins by studying millions of protein sequences and learning what a ‘healthy’ protein looks like.”

The model assigned each variant a score between 0 and 1 to rate the likelihood of pathogenicity [the potential for a pathogen to cause disease]. “The continuous score allows users to choose a threshold for classifying variants as pathogenic or benign that matches their accuracy requirements,” Avsec and Cheng wrote in their blog post.

However, they also acknowledged that it doesn’t indicate exactly how the variation causes disease.

The engineers cautioned that the predictions in the catalog are not intended for clinical use. Instead, they “should be interpreted with other sources of evidence.” However, “this work has the potential to improve the diagnosis of rare genetic disorders, and help discover new disease-causing genes,” they noted.

Genomics England Sees a Helpful Tool

BBC noted that AlphaMissense has been tested by Genomics England, which works with the UK’s National Health Service. “The new tool is really bringing a new perspective to the data,” Ellen Thomas, PhD, Genomics England’s Deputy Chief Medical Officer, told the BBC. “It will help clinical scientists make sense of genetic data so that it is useful for patients and for their clinical teams.”

AlphaMissense is “a big step forward,” Ewan Birney, PhD, Deputy Director General of the European Molecular Biology Laboratory (EMBL) told the BBC. “It will help clinical researchers prioritize where to look to find areas that could cause disease.”

Other experts, however, who spoke with MIT Technology Review were less enthusiastic.

“DeepMind is being DeepMind,” Insilico Medicine founder/CEO Alex Zhavoronkov, PhD, told the MIT publication. “Amazing on PR and good work on AI.”

Heidi Rehm, PhD, co-director of the Program in Medical and Population Genetics at the Broad Institute, suggested that the DeepMind engineers overstated the certainty of the model’s predictions. She told the publication that she was “disappointed” that they labeled the variants as benign or pathogenic.

“The models are improving, but none are perfect, and they still don’t get you to pathogenic or not,” she said.

“Typically, experts don’t declare a mutation pathogenic until they have real-world data from patients, evidence of inheritance patterns in families, and lab tests—information that’s shared through public websites of variants such as ClinVar,” the MIT article noted.

Is AlphaMissense a Biosecurity Risk?

Although DeepMind has released its catalog of variations, MIT Technology Review notes that the lab isn’t releasing the entire AI model due to what it describes as a “biosecurity risk.”

The concern is that “bad actors” could try using it on non-human species, DeepMind said. But one anonymous expert described the restrictions “as a transparent effort to stop others from quickly deploying the model for their own uses,” the MIT article noted.

And so, genetics research takes a huge step forward thanks to Google DeepMind, artificial intelligence, and deep learning. Clinical laboratories and pathologists may soon have useful new tools that help healthcare provider diagnose diseases. Time will tell. But the developments are certain worth watching.

—Stephen Beale

Related Information:

AlphaFold Is Accelerating Research in Nearly Every Field of Biology

A Catalogue of Genetic Mutations to Help Pinpoint the Cause of Diseases

Accurate Proteome-wide Missense Variant Effect Prediction with AlphaMissense

Google DeepMind AI Speeds Up Search for Disease Genes

DeepMind Is Using AI to Pinpoint the Causes of Genetic Disease

DeepMind’s New AI Can Predict Genetic Diseases

Clarapath Acquires Crosscope, Bridging Histology Automation with Digital Pathology

Clarapath is working to automate manual processes in histology while also capturing data to better inform clinical laboratories

Looking to provide an end-to-end digital pathology solution, medical robotics maker Clarapath has acquired Crosscope, a medical artificial intelligence (AI) software company that develops AI-powered telepathology for medical image information extraction and precision medicine diagnostics.

The deal will enable Crosscope’s digital pathology platform to layer around Clarapath’s histology automation hardware, a combination that could improve quality and efficiencies in diagnostic services for future customers, according to a Clarapath press release.

Clarapath’s goal with its products is to automate certain manual processes in histology laboratories, while at the same time reducing variability in how specimens are processed and produced into glass slides. In an exclusive interview with Dark Daily, Eric Feinstein, CEO and President at Clarapath said he believes the resulting data about these activities can drive further changes.

“A histotechnologist turns a microtome wheel and makes decisions about a piece of tissue in real time,” noted Feinstein, who will speak at the Executive War College on Diagnostics, Clinical Laboratory, and Pathology Management on April 25-26 in New Orleans. “All of that real-time data isn’t captured. Imagine if we could take all of that data from thousands of histotechnologists who are cutting every day and aggregate it. Then you could start drawing definitive conclusions about best practices.”

Eric Feinstein

“Clarapath’s foundation is about creating consistency and standardizing steps in histology—and uncovering the data that you need in order to accomplish those goals as a whole system,” Eric Feinstein (above), CEO and President at Clarapath told Dark Daily. “A histology lab’s workflow—from when the tissue comes in to when the glass slide is produced—should all be connected.” Many processes in histology and anatomic pathology continue to be manual. Automated solutions can contribute to improved productivity and reducing variability in how individual specimens are processed. (Photo copyright: Clarapath.)

Details Behind Clarapath’s Deal to Acquire Crosscope

As part of its acquisition, Clarapath of Hawthorne, New York, has retained all of Crosscope’s employees, who are located in Mountain View, California, and Bombay, India. Financial terms of the deal were not disclosed.

Clarapath’s flagship histology automation product is SectionStar, a tissue sectioning and transfer system designed to automate inefficient and manual activities in slide processing. The device offers faster and more efficient sample processing while reducing human involvement. Clarapath expects SectionStar be on the market in 2023. The company is currently taking pre-orders. 

Meanwhile, Crosscope developed Crosscope Dx, a turnkey digital pathology solution that provides workflow tools and slide management as well as AI and machine learning to assist pathologists with their medical decision-making and diagnoses.

Adoption of Digital Pathology and Automation Can Be Challenging

Digital pathology has experienced growing popularity in the post-COVID-19 pandemic world. This is not only because remote pathology case reviews have become increasingly acceptable to physicians but also because of the ongoing shortages in clinical laboratory staffing.

“A pain point today for clinicians and laboratories is labor. That’s across the board,” Feinstein said. “We can help solve that with SectionStar.”

In “Recent Separate Business Transactions by Fujifilm and GE Healthcare Suggest Bullish Outlook for Faster Adoption of Digital Pathology,” Dark Daily reported that vendors have their eyes open for deals and partnerships in digital pathology.

Feinstein does not believe adoption of digital pathology and histology automation is proceeding slowly, but he does acknowledge barriers to healthcare organizations installing the technologies.

“There are lots of little things that—from a workflow perspective—people have outsized expectations about,” he explained. “Clinicians and administrators are not used to innovating in a product sense. They may be innovating on how they deliver care or treatment pathways, but they’re not used to developing an engineering product and going through alpha and beta stages. That makes adopting new technology challenging.”

Medical laboratory managers and pathologists interested in pursuing histology automation and digital pathology should first determine what processes are sub-optimal or would benefit from the standardization hardware and software can offer. Being able to articulate those gains can help build the case for a return on investment to decision-makers.

Another resource to consider: Feinstein will speak about innovations for remote histology laboratory workers at the upcoming Executive War College for Clinical Laboratory, Diagnostics, and Pathology Management on April 25-26 in New Orleans. His session is titled, “Re-engineering the Classic Histology Laboratory: Enabling the Remote Histotechnologist with New Tools That Improve Productivity, Automate Processes, and Protect Quality.”

Scott Wallask

Related Information:

Clarapath Acquires Crosscope and Combines Tissue Processing Robotics with AI Powered Digital Pathology for Building the Lab of the Future

Histopathology is Ripe for Automation

UCLA’s Virtual Histology Could Eliminate Need for Invasive Biopsies for Some Skin Conditions and Cancers

2023 Executive War College on Diagnostics, Clinical Laboratory, and Pathology Management Announced for April 25-26

Cedars-Sinai Researchers Determine Smartphone App Can Assess Stool Form as Well as Gastroenterologists and Better than IBS Patients

Artificial intelligence performs BSS assessments with higher sensitivity and specificity than human diagnosticians

In a recent study conducted by scientists at Cedars-Sinai Medical Center in Los Angeles, researchers evaluated a smartphone application (app) that uses artificial intelligence (AI) to assess and characterize digital images of stool samples. The app, it turns out, matched the accuracy of participating gastroenterologists and exceeded the accuracy of study patients’ self-reports of stool specimens, according to a news release.

Though smartphone apps are technically not clinical laboratory tools, anatomic pathologists and medical laboratory scientists (MLSs) may be interested to learn how health information technology (HIT), machine learning, and smartphone apps are being used to assess different aspects of individuals’ health, independent of trained healthcare professionals.

The issue that the Cedars Sinai researchers were investigating is the accuracy of patient self-reporting. Because poop can be more complicated than meets the eye, when asked to describe their bowel movements patients often find it difficult to be specific. Thus, use of a smartphone app that enables patients to accurately assess their stools in cases where watching the function of their digestive tract is relevant to their diagnoses and treatment would be a boon to precision medicine treatments of gastroenterology diseases.

The scientists published their findings in the American Journal of Gastroenterology, titled, “A Smartphone Application Using Artificial Intelligence Is Superior to Subject Self-Reporting when Assessing Stool Form.”

Mark Pimentel, MD

“This app takes out the guesswork by using AI—not patient input—to process the images (of bowel movements) taken by the smartphone,” said gastroenterologist Mark Pimentel, MD (above), Executive Director of Cedars-Sinai’s Medically Associated Science and Technology (MAST) program and principal investigator of the study, in a news release. “The mobile app produced more accurate and complete descriptions of constipation, diarrhea, and normal stools than a patient could, and was comparable to specimen evaluations by well-trained gastroenterologists in the study.” (Photo copyright: Cedars-Sinai.)

Pros and Cons of Bristol Stool Scale

In their paper, the scientists discussed the Bristol Stool Scale (BSS), a traditional diagnostic tool for identifying stool forms into seven categories. The seven types of stool are:

  • Type 1: Separate hard lumps, like nuts (difficult to pass).
  • Type 2: Sausage-shaped, but lumpy.
  • Type 3: Like a sausage, but with cracks on its surface.
  • Type 4: Like a sausage or snake, smooth and soft (average stool).
  • Type 5: Soft blobs with clear cut edges.
  • Type 6: Fluffy pieces with ragged edges, a mushy stool (diarrhea).
  • Type 7: Watery, no solid pieces, entirely liquid (diarrhea). 

In an industry guidance report on irritable bowel syndrome (IBS)and associated drugs for treatment, the US Food and Drug Administration (FDA) said the BSS is “an appropriate instrument for capturing stool consistency in IBS.”

But even with the BSS, things can get murky for patients. Inaccurate self-reporting of stool forms by people with IBS and diarrhea can make proper diagnoses difficult.

“The problem is that whenever you have a patient reporting an outcome measure, it becomes subjective rather than objective. This can impact the placebo effect,” gastroenterologist Mark Pimentel, MD, Executive Director of Cedars-Sinai’s Medically Associated Science and Technology (MAST) program and principal investigator of the study, told Healio.

Thus, according to the researchers, AI algorithms can help with diagnosis by systematically doing the assessments for the patients, News Medical reported.

30,000 Stool Images Train New App

To conduct their study, the Cedars-Sinai researchers tested an AI smartphone app developed by Dieta Health. According to Health IT Analytics, employing AI trained on 30,000 annotated stool images, the app characterizes digital images of bowel movements using five parameters:

  • BSS,
  • Consistency,
  • Edge fuzziness,
  • Fragmentation, and
  • Volume.

“The app used AI to train the software to detect the consistency of the stool in the toilet based on the five parameters of stool form, We then compared that with doctors who know what they are looking at,” Pimentel told Healio.

AI Assessments Comparable to Doctors, Better than Patients

According to Health IT Analytics, the researchers found that:

  • AI assessed the stool comparable to gastroenterologists’ assessments on BSS, consistency, fragmentation, and edge fuzziness scores.
  • AI and gastroenterologists had moderate-to-good agreement on volume.
  • AI outperformed study participant self-reports based on the BSS with 95% accuracy, compared to patients’ 89% accuracy.

Additionally, the AI outperformed humans in specificity and sensitivity as well:

  • Specificity (ability to correctly report a negative result) was 27% higher.
  • Sensitivity (ability to correctly report a positive result) was 23% higher.

“A novel smartphone application can determine BSS and other visual stool characteristics with high accuracy compared with the two expert gastroenterologists. Moreover, trained AI was superior to subject self-reporting of BSS. AI assessments could provide more objective outcome measures for stool characterization in gastroenterology,” the Cedars-Sinai researchers wrote in their paper.

“In addition to improving a physician’s ability to assess their patients’ digestive health, this app could be advantageous for clinical trials by reducing the variability of stool outcome measures,” said gastroenterologist Ali Rezaie, MD, study co-author and Medical Director of Cedars-Sinai’s GI Motility Program in the news release.

The researchers plan to seek FDA review of the mobile app.

Opportunity for Clinical Laboratories

Anatomic pathologists and clinical laboratory leaders may want to reach out to referring gastroenterologists to find out how they can help to better serve gastro patients. As the Cedars-Sinai study suggests, AI smartphone apps can perform BSS assessments as good as or better than humans and may be useful tools in the pursuit of precision medicine treatments for patient suffering from painful gastrointestinal disorders.

—Donna Marie Pocius

Related Information:

Smartphone Application Using Artificial Intelligence is Superior to Subject Self-Reporting When Assessing Stool Form

Study: App More Accurate than Patient Evaluation of Stool Samples

Industry Guidance Report: Irritable Bowel Syndrome—Clinical Evaluation of Drugs

Artificial Intelligence-based Smartphone App for Characterizing Stool Form

AI Mobile App Improves on “Subjective” Patient-Reported Stool Assessment in IBS

Artificial Intelligence App Outperforms Patient-Reported Stool Assessments

UCLA’s Virtual Histology Could Eliminate Need for Invasive Biopsies for Some Skin Conditions and Cancers

Though the new technology could speed diagnoses of cancers and other skin diseases, it would also greatly reduce dermatopathology biopsy referrals and revenue

What effect would elimination of tissue biopsies have on dermatopathology and clinical laboratory revenue? Quite a lot. Dermatologists alone account for a significant portion of skin biopsies sent to dermatopathologists. Thus, any new technology that can “eliminate the need for invasive skin biopsies” would greatly reduce the number of histopathological referrals and reduce revenue to those practices.

Nevertheless, one such new technology may have been created by Ozcan Research Group in a proof-of-concept study they conducted at the University of California, Los Angeles (UCLA).

Called Virtual Histology, the technology applies artificial intelligence (AI) deep learning methods to reflectance confocal microscopy (RCM) images “to rapidly perform virtual histology of in vivo, label-free RCM images of normal skin structure, basal cell carcinoma, and melanocytic nevi with pigmented melanocytes, demonstrating similar histological features to traditional histology from the same excised tissue,” the UCLA scientists wrote in their study, published in the Nature peer-reviewed journal Light: Science and Applications.

Aydogan Ozcan, PhD

“What if we could entirely bypass the biopsy process and perform histology-quality staining without taking tissue and processing tissue in a noninvasive way? Can we create images that diagnosticians can benefit from?” asked Aydogan Ozcan, PhD (above), Chancellor’s Professor of Electrical and Computer Engineering at UCLA’s Samueli School of Engineering, one of the scientists who developed UCLA’s new virtual histology method, during an interview with Medical Device + Diagnostic Industry (MD+DI). (Photo copyright: Nature.)

Could Skin Biopsies be Eliminated?

The UCLA researchers believe their innovative deep learning-enabled imaging framework could possibly circumvent the need for skin biopsies to diagnose skin conditions.

“Here, we present a deep learning-based framework that uses a convolutional neural network to rapidly transform in vivo RCM images of unstained skin into virtually-stained hematoxylin and eosin-like images with microscopic resolution, enabling visualization of the epidermis, dermal-epidermal junction, and superficial dermis layers.

“This application of deep learning-based virtual staining to noninvasive imaging technologies may permit more rapid diagnoses of malignant skin neoplasms and reduce invasive skin biopsies,” the researchers added in their published study.

“This process bypasses several standard steps typically used for diagnosis, including skin biopsy, tissue fixation, processing, sectioning, and histochemical staining,” Aydogan Ozcan, PhD, Chancellor’s Professor of Electrical and Computer Engineering at UCLA’s Samueli School of Engineering, told Optics.org.

AI and Deep Learning in Dermatopathology

According to the published study, the UCLA team trained their neural network under an adversarial machine learning scheme to transform grayscale RCM images into virtually stained 3D microscopic images of normal skin, basal cell carcinoma, and pigmented melanocytic nevi. The new images displayed similar morphological features to those shown with the widely used hematoxylin and eosin (H&E) staining method.

“In our studies, the virtually stained images showed similar color contrast and spatial features found in traditionally stained microscopic images of biopsied tissue,” Ozcan told Photonics Media. “This approach may allow diagnosticians to see the overall histological features of intact skin without invasive skin biopsies or the time-consuming work of chemical processing and labeling of tissue.”

The framework covers different skin layers, including the epidermis, dermal-epidermis, and superficial dermis layers. It images deeper into tissue without being invasive and can be quickly performed.

“The virtual stain technology can be streamlined to be almost semi real time,” Ozcan told Medical Device + Diagnostic Industry (MD+DI). “You can have the virtual staining ready when the patient is wrapping up. Basically, it can be within a couple of minutes after you’re done with the entire imaging.”

Currently, medical professionals rely on invasive skin biopsies and histopathological evaluations to diagnose skin diseases and cancers. These diagnostic techniques can result in unnecessary biopsies, scarring, multiple patient visits and increased medical costs for patients, insurers, and the healthcare system.

Improving Time to Diagnosis through Digital Pathology

Another advantage of this virtual technology, the UCLA researchers claim, is that it can provide better images than traditional staining methods, which could improve the ability to diagnose pathological skin conditions and help alleviate human error.

“The majority of the time, small laboratories have a lot of problems with consistency because they don’t use the best equipment to cut, process, and stain tissue,” dermatopathologist Philip Scumpia, MD, PhD, Assistant Professor of Dermatology and Dermatopathology at UCLA Health and one of the authors of the research paper, told MD+DI.

“What ends up happening is we get tissue on a histology slide that’s basically unevenly stained, unevenly put on the microscope, and it gets distorted,” he added, noting that this makes it very hard to make a diagnosis.  

Scumpia also added that this new technology would allow digital images to be sent directly to the pathologist, which could reduce processing and laboratory times.

“With electronic medical records now and the ability to do digital photography and digital mole mapping, where you can obtain a whole-body imaging of patients, you could imagine you can also use one of these reflectance confocal devices. And you can take that image from there, add it to the EMR with the virtual histology stain, which will make the images more useful,” Scumpia said. “So now, you can track lesions as they develop.

“What’s really exciting too, is that there’s the potential to combine it with other artificial intelligence, other machine learning techniques that can give more information,” Scumpia added. “Using the reflectance confocal microscope, a clinician who might not be as familiar in dermatopathology could take images and send [them] to a practitioner who could give a more expert diagnosis.”

Faster Diagnoses but Reduced Revenue for Dermatopathologists, Clinical Labs

Ozcan noted that there’s still a lot of work to be done in the clinical assessment, validation, and blind testing of their AI-based staining method. But he hopes the technology can be propelled into a useful tool for clinicians.

“I think this is a proof-of-concept work, and we’re very excited to make it move forward with further advances in technology, in the ways that we acquire 3D information [and] train our neural networks for better and faster virtual staining output,” he told MD+DI.

Though this new technology may reduce the need for invasive biopsies and expedite the diagnosis of skin conditions and cancers—thus improving patient outcomes—what affect might it have on dermatopathology practices?

More research and clinical studies are needed before this new technology becomes part of the diagnosis and treatment processes for skin conditions. Nevertheless, should virtual histology become popular and viable, it could greatly impact the amount of skin biopsy referrals to pathologists, dermatopathologists, and clinical laboratories, thus diminishing a great portion of their revenue. 

—JP Schlingman

Related Information:

Virtual Histology Eliminates Need for Invasive Skin Biopsies

UCLA Deep-learning Reduces Need for Invasive Biopsies

AI Imaging Method Provides Biopsy-free Skin Diagnosis

Light People: Professor Aydogan Ozcan

Histology Process Bypasses Need for Biopsies, Enables Diagnoses

Reflection-Mode Virtual Histology Using Photoacoustic Remote Sensing Microscopy

Introduction to Reflectance Confocal Microscopy and Its Use in Clinical Practice

Biopsy-free In Vivo Virtual Histology of Skin Using Deep Learning

Can This New Tech Reduce the Need for Skin Biopsies?

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