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Clinical Laboratories and Pathology Groups

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Chinese Researchers Develop Non-Invasive Clinical Laboratory Skin Test for Measuring Cholesterol

Study also may have found relationship between atherosclerosis and cholesterol

Chinese scientists have developed a cutting-edge method for non-invasively monitoring blood cholesterol levels in humans. The innovative technology utilizes images of skin on hands and may eliminate the need for both invasive venipunctures and fasting for testing cholesterol. Given the large volumes of blood cholesterol tests currently performed by clinical laboratories, this new technology could have significant impact on cholesterol testing if further studies confirm its capabilities.

Notably, the Chinese researchers have apparently already developed a lab analyzer to perform the procedure and it is being used in clinical care. However, in the United States and other countries, this technology will require additional clinical studies and regulatory review before clinical laboratories would be able to use it in daily patient care.

The cholesterol sensing system consists of a detection reagent associated with a fluorescent group that binds to skin cholesterol, and a detection device. Cholesterol levels are easily obtained from the skin, according to the researchers, by analyzing the manner in which the skin absorbs and scatters light via a scanner.

Should this technology be validated for clinical care, it could replace other invasive clinical laboratory tests for cholesterol measurement.

The scientists published their findings in the journal Lipids in Health and Disease, titled, “Non-invasive Skin Cholesterol Testing: A Potential Proxy for LDL-C and ApoB Serum Measurements.”

Demonstration of how non-invasive cholesterol test is performed

The series of images above, taken from the researchers’ Lipids in Health and Disease published study, demonstrates how their non-invasive clinical laboratory test for total blood cholesterol is performed. Non-invasive clinical laboratory tests for monitoring biomarkers in the blood are always preferred by patients over veinous punctures and fasting. (Photo copyright: Hefei Institutes of Physical Science, Chinese Academy of Sciences.)

First Evidence of Relationship between Cholesterol and Atherosclerosis

“Just put your hands on, and the system will tell you the cholesterol data,” Yikun Wang, PhD, Professor, Department of Physical Sciences, Hefei Institutes of Physical Science, Chinese Academy of Sciences, and leader of the research team, told Diagnostics World. “Cholesterol is one of several types of fats (lipids) that play an important role in human body, we can track your fats in this simple way.”

To perform the testing, clinicians first clean the test site located on the fleshy edge of the palm of the hand with an alcohol swab. A patient’s non-dominant hand is used for the test as the skin on that hand is typically less abrasive and contains fewer melanocytes, which allows for more stable results. A plastic-coated annulus is then applied to the test site and the examined portion is positioned on the measuring hole of the detection system to measure the background light spectrum of the skin.

Once the background signal is ascertained, the detection reagent is added to the annulus until it is full. After 60 seconds, any excess detection reagent is removed from the annulus. A cleaning reagent is then added to the annulus for 30 seconds and removed with a sterile cotton swab. The treated portion of the skin is then placed over the measuring hole of the detection system and two spectrums of light are compared to measure the skin cholesterol, which accurately correlates to the cholesterol in the bloodstream.

“Compared to in-situ detection used in the previous clinical research, our device may offer more accurate results for we can avoid the influence of pressure and skin background differences [person to person],” Wang said. “Study results offer the first evidence of a relationship between skin cholesterol and atherosclerotic disease in a Chinese population, which may be of great significance to researchers around the world.”

Initially, 154 patients diagnosed with acute coronary syndrome (ACS) between January 2020 and April 2021 were involved in the study. However, only 121 of those patients were included in the final study with the remaining being excluded due to at least one of the following criteria:

  • History of statin drug use,
  • Inability to tolerate statins,
  • Severe hepatic (liver) or renal (kidney) insufficiency, and
  • Obesity.

Clinician Use Can Affect Accuracy of Test

Developed by researchers from the Hefei Institutes of Physical Science Chinese Academy of Sciences, and the University of Science and Technology of China, the researchers noted that how clinicians operate the device can have an impact on the accuracy of the test results.

“A critical step in the [testing] process that is subject to operator variability is blotting, which requires the operator to remove an unbound detector from the palm before adding the indicator,” Wang told Diagnostics World. “Excess residual indicator solution can result in falsely increased skin cholesterol levels. Considering this, we are planning to develop a simplified and standardized blotting procedure.”

Millions of people in the US live with illness that requires regular monitoring of blood cholesterol. Normal total cholesterol should be less than 200 milligrams per deciliter (mg/dL). According to the federal Centers for Disease Control and Prevention (CDC), nearly 94 million US adults over the age of 20 have total cholesterol levels higher than 200 mg/dL and 28 million adults have total cholesterol levels higher than 240 mg/dL. In addition, 7% of children and adolescents between the ages of six and 19 have high cholesterol. For these reasons, cholesterol testing represents a substantial portion of the clinical laboratory tests performed daily in this country.  

This new non-invasive technology for monitoring total blood cholesterol in humans could greatly benefit patients, especially if it eliminates the need for venipunctures and fasting prior to testing. Clinical laboratory managers and pathologists may want to follow the progress of this new cholesterol testing technology as it demonstrates its value in China and is submitted for regulatory review in this country.

JP Schlingman

Related Information:

Non-invasive Scanning Tech Reads Blood Cholesterol Levels via the Skin

Non-invasive Skin Cholesterol Testing: A Potential Proxy for LDL-C and ApoB Serum Measurements

Researchers Develop Novel System for Rapid and Non-invasive Detection of Skin Cholesterol

Noninvasive Detection System to Prevent Cardiovascular Diseases

Skin Cholesterol Testing Could Play Role in Lipid Screening and Management

CDC: High Cholesterol Facts

Dermatopathologists May Soon Have Useful New Tool That Uses AI Algorithm to Detect Melanoma in Wide-field Images of Skin Lesions Taken with Smartphones

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

Scientists at the Massachusetts Institute of Technology (MIT) and other Boston-area research institutions have developed an artificial intelligence (AI) algorithm that detects melanoma in wide-field images of skin lesions taken on smartphones. And its use could affect how dermatologists and dermatopathologists diagnose cancer.

The study, published in Science Translational Medicine, titled, “Using Deep Learning for Dermatologist-Level Detection of Suspicious Pigmented Skin Lesions from Wide-Field Images,” demonstrates that even a common device like a smartphone can be a valuable resource in the detection of disease.

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.  

Luis Soenksen, PhD

“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.

Smartphone image of pigmented skin lesions

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.

JP Schlingman

Related Information:

Software Spots Suspicious Skin Lesions on Smartphone Photos

An Artificial Intelligence Tool That Can Help Detect Melanoma

Using Deep Learning for Dermatologist-level Detection of Suspicious Pigmented Skin Lesions from Wide-field Images