According to an EADV press release, the AI software demonstrated a “100% (59/59 cases identified) sensitivity for detecting melanoma—the most serious form of skin cancer.” The AI software also “correctly detected 99.5% (189/190) of all skin cancers and 92.5% (541/585) of pre-cancerous lesions.”
“Of the basal cell carcinoma cases, a single case was missed out of 190, which was later identified at a second read by a dermatologist ‘safety net.’ This further demonstrates the need to have appropriate clinical oversight of the AI,” the press release noted.
AI is being utilized more frequently within the healthcare industry to diagnose and treat a plethora of illnesses. This recent study performed by scientists in the United Kingdom demonstrates that new AI models can be used to accurately diagnose some skin cancers, but that “AI should not be used as a standalone detection tool without the support of a consultant dermatologist,” the press release noted.
“The role of AI in dermatology and the most appropriate pathway are debated,” said Kashini Andrew, MBBS, MSc (above), Specialist Registrar at University Hospitals Birmingham NHS Foundation Trust. “Further research with appropriate clinical oversight may allow the deployment of AI as a triage tool. However, any pathway must demonstrate cost-effectiveness, and AI is currently not a stand-alone tool in dermatology. Our data shows the great promise of AI in future provision of healthcare.” Clinical laboratories and dermatopathologists in the United States will want to watch the further development of this AI application. (Photo copyright: LinkedIn.)
How the NHS Scientists Conducted Their Study
Researchers tested their algorithm for almost three years to determine its ability to detect cancerous and pre-cancerous growths. A group of dermatologists and medical photographers entered patient information into their algorithm and trained it how to detect abnormalities. The collected data came from 22,356 patients with suspected skin cancers and included photos of known cancers.
The scientists then repeatedly recalibrated the software to ensure it could distinguish between non-cancerous lesions and potential cancers or malignancies. Dermatologists then reviewed the final data from the algorithm and compared it to diagnoses from health professionals.
“This study has demonstrated how AI is rapidly improving and learning, with the high accuracy directly attributable to improvements in AI training techniques and the quality of data used to train the AI,” said Kashini Andrew, MBBS, MSc, Specialist Registrar at University Hospitals Birmingham NHS Foundation Trust, and co-author of the study, in EADV press release.
Freeing Up Physician Time
The EADV Congress where the NHS researchers presented their findings took place in October in Berlin. The first model of their AI software was tested in 2021 and that version was able to detect:
85.9% (195 out of 227) of melanoma cases,
83.8% (903 out of 1078) of all skin cancers, and
54.1% (496 out of 917) of pre-cancerous lesions.
After fine-tuning, the latest version of the algorithm was even more promising, with results that included the detection of:
100% (59 out of 59) cases of melanoma,
99.5% (189 out of 190) of all skin cancers, and
92.5% (541 out of 585) pre-cancerous lesions.
“The latest version of the software has saved over 1,000 face-to-face consultations in the secondary care setting between April 2022 and January 2023, freeing up more time for patients that need urgent attention,” Andrew said in the press release.
Still, the researchers admit that AI should not be used as the only detection method for skin cancers.
“We would like to stress that AI should not be used as a standalone tool in skin cancer detection and that AI is not a substitute for consultant dermatologists,” stated Irshad Zaki, B Med Sci (Hons), Consultant Dermatologist at University Hospitals Birmingham NHS Foundation Trust and one of the authors of the study, in the press release.
“The role of AI in dermatology and the most appropriate pathway are debated. Further research with appropriate clinical oversight may allow the deployment of AI as a triage tool,” said Andrew in the press release. “However, any pathway must demonstrate cost-effectiveness, and AI is currently not a stand-alone tool in dermatology. Our data shows the great promise of AI in future provision of healthcare.”
Two People in the US Die of Skin Cancer Every Hour
According to the Skin Cancer Foundation, skin cancer is the most common cancer in the United States as well as the rest of the world. More people in the US are diagnosed with skin cancer every year than all other cancers combined.
When detected early, the five-year survival rate for melanoma is 99%, but more than two people in the US die of skin cancer every hour. At least one in five Americans will develop skin cancer by the age of 70 and more than 9,500 people are diagnosed with the disease every day in the US.
The annual cost of treating skin cancers in the United States is estimated at $8.1 billion annually, with approximately $3.3 billion of that amount being for melanoma and the remaining $4.8 billion for non-melanoma skin cancers.
More research is needed before University Hospitals Birmingham’s new AI model can be used clinically in the diagnoses of skin cancers. However, its level of accuracy is unprecedented in AI diagnostics. This is a noteworthy step forward in the field of AI for diagnostic purposes that can be used by clinical laboratories and dermatopathologists.
Hello primary diagnosis of digital pathology images via artificial intelligence! Goodbye light microscopes!
Digital pathology is poised to take a great leap forward. Within as few as 12 months, image analysis algorithms may gain regulatory clearance in the United States for use in primary diagnosis of whole-slide images (WSIs) for certain types of cancer. Such a development will be a true revolution in surgical pathology and would signal the beginning of the end of the light microscope era.
A harbinger of this new age of digital pathology and automated image analysis is a press release issued last week by Ibex Medical Analytics of Tel Aviv, Israel. The company announced that its Galen artificial intelligence (AI)-powered platform for use in the primary diagnosis of specific cancers will undergo an accelerated review by the Food and Drug Administration (FDA).
FDA’s ‘Breakthrough Device Designation’ for Pathology AI Platform
Ibex stated that “The FDA’s Breakthrough Device Designation is granted to technologies that have the potential to provide more effective treatment or diagnosis of life-threatening diseases, such as cancer. The designation enables close collaboration with, and expedited review by, the FDA, and provides formal acknowledgement of the Galen platform’s utility and potential benefit as well as the robustness of Ibex’s clinical program.”
“All surgical pathologists should recognize that, once the FDA begins to review and clear algorithms capable of using digital pathology images to make an accurate primary diagnosis of cancer, their daily work routines will be forever changed,” stated Robert L. Michel, Editor-in-Chief of Dark Daily and its sister publication The Dark Report. “Essentially, as FDA clearance is for use in clinical care, pathology image analysis algorithms powered by AI will put anatomic pathology on the road to total automation.
“Clinical laboratories have seen the same dynamic, with CBCs (complete blood counts) being a prime example. Through the 1970s, clinical laboratories employed substantial numbers of hematechnologists [hematechs],” he continued. “Hematechs used a light microscope to look at a smear of whole blood that was on a glass slide with a grid. The hematechs would manually count and record the number of red and white blood cells.
“That changed when in vitro diagnostics (IVD) manufacturers used the Coulter Principle and the Coulter Counter to automate counting the red and white blood cells in a sample, along with automatically calculating the differentials,” Michel explained. “Today, only clinical lab old-timers remember hematechs. Yet, the automation of CBCs eventually created more employment for medical technologists (MTs). That’s because the automated instruments needed to be operated by someone trained to understand the science and medicine involved in performing the assay.”
Primary Diagnosis of Cancer with an AI-Powered Algorithm
Surgical pathology is poised to go down a similar path. Use of a light microscope to conduct a manual review of glass slides will be supplanted by use of digital pathology images and the coming next generation of image analysis algorithms. Whether these algorithms are called machine learning, computational pathology, or artificial intelligence, the outcome is the same—eventually these algorithms will make an accurate primary diagnosis from a digital image, with comparable quality to a trained anatomic pathologist.
How much of a threat is automated analysis of digital pathology images? Computer scientist/engineer Ajit Singh, PhD, a partner at Artiman Ventures and an authority on digital pathology, believes that artificial intelligence is at the stage where it can be used for primary diagnosis for two types of common cancer: One is prostate cancer, and the other is dermatology.
On June 17, Ajit Singh, PhD (above), Partner at Artiman Ventures, will lead a special webinar and roundtable discussion for all surgical pathologists and their practice administrators on the coming arrival of artificial intelligence-powered algorithms to aid in the primary diagnosis of certain cancers. Regulatory approval for such solutions may happen by the end of this year. Such a development would accelerate the transition from light microscopes to a fully digital pathology workflow. Singh is shown above addressing the 2018 Executive War College. (Photo copyright: The Dark Report.)
“It is now possible to do a secondary read, and even a first read, in prostate cancer with an AI system alone. In cases where there may be uncertainty, a pathologist can review the images. Now, this is specifically for prostate cancer, and I think this is a tremendous positive development for diagnostic pathways,” he added.
Use of Digital Pathology with AI-Algorithms Changes Diagnostics
Pathologists who are wedded to their light microscopes will want to pay attention to the impending arrival of a fully digital pathology system, where glass slides are converted to whole-slide images and then digitized. From that point, the surgical pathologist becomes the coach and quarterback of an individual patient’s case. The pathologist guides the AI-powered image analysis algorithms. Based on the results, the pathologist then orders supplementary tests appropriate to developing a robust diagnosis and guiding therapeutic decisions for that patient’s cancer.
In his interview with The Dark Report, Singh explained that the first effective AI-powered algorithms in digital pathology will be developed for prostate cancer and skin cancer. Both types of cancer are much less complex than, say, breast cancer. Moreover, the AI developers have decades of prostate cancer and melanoma cases where the biopsies, diagnoses, and downstream patient outcomes create a rich data base from which the algorithms can be trained and tuned.
This webinar is organized as a roundtable discussion so participants can interact with the expert panelists. The Chair and Moderator is Ajit Singh, PhD, Adjunct Professor at the Stanford School of Medicine and Partner at Artiman Ventures.
The panelists (above) represent academic pathology, community hospital pathology, and the commercial sector. They are:
Because the arrival of automated analysis of digital pathology images will transform the daily routine of every surgical pathologist, it would be beneficial for all pathology groups to have one or more of their pathologists register and participate in this critical webinar.
The roundtable discussion will help them understand how quickly AI-powered image analysis is expected be cleared for use by the FDA in such diseases as prostate cancer and melanomas. Both types of cancers generate high volumes of case referrals to the nation’s pathologists, so potential for disruption to long-standing client relationships, and the possible loss of revenue for pathology groups that delay their adoption of digital pathology, can be significant.
On the flip side, community pathology groups that jump on the digital pathology bandwagon early and with the right preparation will be positioned to build stronger client relationships, increase subspecialty case referrals, and generate additional streams of revenue that boost partner compensation within their group.
Also, because so many pathologists are working remotely, Dark Daily has arranged special group rates for pathology practices that would like their surgical pathologists to participate in this important webinar and roundtable discussion on AI-powered primary diagnosis of pathology images. Inquire at info@darkreport.com or call 512-264-7103.
New study conducted by an international team of researchers suggests that artificial intelligence (AI) may be better than highly-trained humans at detecting certain skin cancers
Artificial intelligence (AI) has been working its way into health technology for several years and, so far, AI tools have been a boon to physicians and health networks. Until now, though, the general view was that it was a supplemental tool for diagnosticians, not a replacement for them. But what if the AI was better at detecting disease than humans, including anatomic pathologists?
Researchers in the Department of Dermatology at Heidelberg University in Germany have concluded that AI can be more accurate at identifying certain cancers. The challenge they designed for their study involved skin biopsies and dermatologists.
They pitted a deep-learning convolutional neural network (CNN) against 58 dermatologists from 17 countries to determine which was more accurate at detecting malignant melanomas—humans or AI. A CNN is an artificial network based on the biological processes that occur when neurons in the brain are connected to each other and respond to what the eye sees.
The CNN won.
“For the first time we compared a CNN’s diagnostic performance with a large international group of 58 dermatologists, including 30 experts. Most dermatologists were outperformed by the CNN. Irrespective of any physicians’ experience, they may benefit from assistance by a CNN’s image classification,” the report noted.
“I expected only a performance on an even level with the physicians. The outperformance even of the average experienced and trained dermatologists was a major surprise,” Holger Haenssle, PhD, Professor of Dermatology at Heidelberg University and one of the authors of the study, told Healthline. Anatomic pathologists will want to follow the further development of this research and its associated diagnostic technologies. (Photo copyright: University of Heidelberg.)
Does AI Tech Have Superior Visual Acuity Compared to Human Eyes?
The dermatologists who participated in the study had varying degrees of experience in dermoscopy, also known as dermatoscopy. Thirty of the doctors had more than five-year’s experience and were considered to be expert level. Eleven of the dermatologists were considered “skilled” with two- to five-year’s experience. The remaining 17 doctors were termed beginners with less than two-year’s experience.
To perform the study, the researchers first compiled a set of 100 dermoscopic images that showed melanomas and benign moles called Nevi. Dermoscopes (or dermatoscopes) create images using a magnifying glass and light source pressed against the skin. The resulting magnified, high-resolution images allow for easier, more accurate diagnoses than inspection with the naked eye.
During the first stage of the research, the dermatologists were asked to diagnose whether a lesion was melanoma or benign by looking at the images with their naked eyes. They also were asked to render their opinions for any needed action, such as surgery and follow-up care based on their diagnoses.
After this part of the study, the dermatologists on average identified 86.6% of the melanomas and 71.3% of the benign moles. More experienced doctors identified the melanomas at 89%, which was slightly higher than the average of the group.
The researchers also showed 300 images of malignant and benign skin lesions to the CNN. The AI accurately identified 95% of the melanomas by analyzing the images.
“The CNN missed fewer melanomas, meaning it had a higher sensitivity than the dermatologists, and it misdiagnosed fewer benign moles as malignant melanoma, which means it had a higher specificity. This would result in less unnecessary surgery,” Haenssle told CBS News.
In a later part of the research, the dermatologists were shown the images a second time and provided clinical information about the patients, including age, gender, and location of the lesion. They were again instructed to make diagnoses and projected care decisions. With the additional information, the doctors’ average detection of melanomas increased to 88.9% and their recognition of benign moles increased to 75.7%. Still below the results of the CNN.
These findings suggest that the visual pattern recognition of AI technology could be a meaningful tool to help physicians and researchers diagnose certain cancers.
“In the future, I think AI will be integrated into practice as a diagnostic aide, particularly in primary care, to support the decision to excise a lesion, refer, or otherwise to reassure that it is benign,” Victoria Mar, PhD, an Adjunct Senior Lecturer in the Department of Public Health and Preventative Medicine at Australia’s Monash University, told Healthline.
“There is the potential for AI technology to be integrated with 2D or 3D skin imaging systems, which means that the majority of benign lesions would be already filtered by the machine, so that we can spend more time concentrating on the difficult or more concerning lesions,” she said. “To me, this means a more productive interaction with the patient, where we can focus on appropriate management and provide more streamlined care.”
AI Performs Well in Other Studies Involving Skin Biopsies
This study is not the only research that suggests entities besides humans may be utilized in diagnosing some cancers from images. Last year, computer scientists at Stanford University performed similar research and found comparable results. For that study, the researchers created and trained an algorithm to visually diagnose potential skin cancers by looking at a database of skin images. They then showed photos of skin lesions to 21 dermatologists and asked for their diagnoses based on the images. They found the accuracy of their AI matched the performance of the doctors when diagnosing skin cancer from viewed images.
While many dermatologists read patient biopsies on their own, they also refer high volumes of skin biopsies to anatomic pathologists. A technology that can accurately diagnose skin cancers could potentially impact the workload received by clinical laboratories and anatomic pathology groups.
Pathologists will soon have multiple low-cost devices that allow their smartphones and notebook computers to function as microscopes
Microscopy is going mobile and becoming accessible to people beyond pathologists. Researchers and entrepreneurs have invented lenses to transform smartphones and tablets into flat microscopes.
Researchers at the Australian National University in Canberra, Australia, (ANU) have developed an optical lens that can be combined with a smartphone camera to create a microscope for diagnosing skin cancer, reported Physics World in a story published this spring.
Other mobile microscope products have gone to market over the past year. They vary in purpose, magnification strength, and cost. But they have one thing in common: they all fit in a pocket or a flat case and are relatively inexpensive. (more…)
Team of bioengineers succeeds in putting three different imaging technologies into a handheld probe that could be used by physicians to assess skin lesions in their offices
Dermatopathologists and pathology practice administrators will be keenly interested in a new, hand-held diagnostic device that is designed to reduce the need for skin biopsies. Because of high volume of skin biopsies referred to pathologists, any significant reduction in the number of such case referrals would have negative revenue impact on medical laboratories that process and diagnose these specimens.
This innovative work was done at the University of Texas at Austin’s Cockrell School of Engineering. The research team developed a probe that uses three different light modalities to detect melanoma and other skin cancer lesions in real-time, according to a news release. (more…)
