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.
MIT’s deep learning artificial intelligence algorithm demonstrates how similar new technologies and smartphones can be combined to give dermatologists and dermatopathologists valuable new ways to diagnose skin cancer from digital images
According to an MIT press release, “The paper describes the development of an SPL [Suspicious Pigmented Lesion] analysis system using DCNNs [Deep Convolutional Neural Networks] to more quickly and efficiently identify skin lesions that require more investigation, screenings that can be done during routine primary care visits, or even by the patients themselves. The system utilized DCNNs to optimize the identification and classification of SPLs in wide-field images.”
The MIT scientists believe their AI analysis system could aid dermatologists, dermatopathologists, and clinical laboratories detect melanoma, a deadly form of skin cancer, in its early stages using smartphones at the point-of-care.
“Our research suggests that systems leveraging computer vision and deep neural networks, quantifying such common signs, can achieve comparable accuracy to expert dermatologists,” said Luis Soenksen, PhD (above), Venture Builder in Artificial Intelligence and Healthcare at MIT and first author of the study in an MIT press release. “We hope our research revitalizes the desire to deliver more efficient dermatological screenings in primary care settings to drive adequate referrals.” The MIT study demonstrates that dermatologists, dermatopathologists, and clinical laboratories can benefit from using common technologies like smartphones in the diagnosis of disease. (Photo copyright: Wyss Institute Harvard University.)
Improving Melanoma Treatment and Patient Outcomes
Melanoma develops when pigment-producing cells called melanocytes start to grow out of control. The cancer has traditionally been diagnosed through visual inspection of SPLs by physicians in medical settings. Early-stage identification of SPLs can drastically improve the prognosis for patients and significantly reduce treatment costs. It is common to biopsy many lesions to ensure that every case of melanoma can be diagnosed as early as possible, thus contributing to better patient outcomes.
“Early detection of SPLs can save lives. However, the current capacity of medical systems to provide comprehensive skin screenings at scale are still lacking,” said Luis Soenksen, PhD, Venture Builder in Artificial Intelligence and Healthcare at MIT and first author of the study in the MIT press release.
The researchers trained their AI system by using 20,388 wide-field images from 133 patients at the Gregorio Marañón General University Hospital in Madrid, as well as publicly available images. The collected photographs were taken with a variety of ordinary smartphone cameras that are easily obtainable by consumers.
They taught the deep learning algorithm to examine various features of skin lesions such as size, circularity, and intensity. Dermatologists working with the researchers also visually classified the lesions for comparison.
When the algorithm is “shown” a wide-field image like that above taken with a smartphone, it uses deep convolutional neural networks to analyze individual pigmented lesions and screen for early-stage melanoma. The algorithm then marks suspicious images as either yellow (meaning further inspection should be considered) or red (indicating that further inspection and/or referral to a dermatologist is required). Using this tool, dermatopathologists may be able to diagnose skin cancer and excise it in-office long before it becomes deadly. (Photo copyright: MIT.)
“Our system achieved more than 90.3% sensitivity (95% confidence interval, 90 to 90.6) and 89.9% specificity (89.6 to 90.2%) in distinguishing SPLs from nonsuspicious lesions, skin, and complex backgrounds, avoiding the need for cumbersome individual lesion imaging,” the MIT researchers noted in their Science Translational Medicine paper.
In addition, the algorithm agreed with the consensus of experienced dermatologists 88% of the time and concurred with the opinions of individual dermatologists 86% of the time, Medgadget reported.
Modern Imaging Technologies Will Advance Diagnosis of Disease
According to the American Cancer Society, about 106,110 new cases of melanoma will be diagnosed in the United States in 2021. Approximately 7,180 people are expected to die of the disease this year. Melanoma is less common than other types of skin cancer but more dangerous as it’s more likely to spread to other parts of the body if not detected and treated early.
More research is needed to substantiate the effectiveness and accuracy of this new tool before it could be used in clinical settings. However, the early research looks promising and smartphone camera technology is constantly improving. Higher resolutions would further advance development of this type of diagnostic tool.
In addition, MIT’s algorithm enables in situ examination and possible diagnosis of cancer. Therefore, a smartphone so equipped could enable a dermatologist to diagnose and excise cancerous tissue in a single visit, without the need for biopsies to be sent to a dermatopathologist.
Currently, dermatologists refer a lot of skin biopsies to dermapathologists and anatomic pathology laboratories. An accurate diagnostic tool that uses modern smartphones to characterize suspicious skin lesions could become quite popular with dermatologists and affect the flow of referrals to medical laboratories.
New imaging technology might change flow of biopsies to dermatopathologists
Dermatopathologists will be interested to learn about new imaging technology that significantly boosts the accuracy of this methodology to analyze images of the skin and diagnose malignant melanomas.
Although still in the research stage, these technology advances demonstrate how advanced imaging solutions, in tandem with computer-aided diagnosis, may allow dermatologists to evaluate patients without the need to harvest a biopsy and send it to the pathology laboratory for diagnosis.
Anatomic pathology firm hopes proposed sale of stock will bring in up to $150 million
Anatomic pathology company Aurora Diagnostics, Inc., of Palm Beach Gardens, Florida, just announced a new credit facility that will give it access to as much as $335 million should all conditions be met. Aurora Diagnostics hopes to raise $150 million from an initial public offering (IPO) of its stock, for which it filed registration documents in April.
Aurora Diagnostics was founded in June 2006, by former Ameripath, Inc., executives James New and Marty Stefanelli and was originally funded by Summit Partners and GSO Capital Partners. Over the past four years, Aurora Diagnostics says it has acquired 17 pathology practices. Its revenue for 2009 totaled $171 million, with EBIDTA (earnings before interest, depreciation, taxes, and amortization) of $28 million and net income of $9 million.
