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Clinical Laboratories and Pathology Groups

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Clinical Laboratories and Pathology Groups

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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

Scientists at St. Jude Children’s Research Hospital Create 3D Map of Mouse Genome to Study How Genes Respond to Disease

The scientist also employed machine learning “to gauge how easily accessible genes are for transcription” in research that could lead to new clinical laboratory diagnostic tests

Anatomic pathologists and clinical laboratories are of course familiar with the biological science of genomics, which, among other things, has been used to map the human genome. But did you know that a three-dimensional (3D) map of a genome has been created and that it is helping scientists understand how DNA regulates its organization—and why?

The achievement took place at St. Jude Children’s Research Hospital (St. Jude) in Memphis, Tenn. Scientists there created “the first 3D map of a mouse genome” to study “the way cells organize their genomes during development,” a St. Jude news release noted.

Some experts predict that this new approach to understanding how changes happen in a genome could eventually provide new insights that anatomic pathologists and clinical laboratory scientists could find useful when working with physicians to diagnose patients and using the test results to identify the most appropriate therapy for those patients.

The St. Jude researchers published their findings in the journal Neuron in a paper titled “Nucleome Dynamics during Retinal Development.” 

Machine Learning Provides Useful Genomic Data

In addition to 3D modeling, the researchers applied machine learning to data from multiple sources to see how the organization of the genome changed at different times during development. “The changes are not random, but part of the developmental program of cells,” Dyer said in the news release.

The St. Jude study focused on the rod cells in a mouse retina. That may seem like a narrow scope, but there are more than 8,000 genes involved in retinal development in mice, during which those genes are either turned on or off.

To see what was happening among the cells, the researchers used HI-C analysis, an aspect of ultra-deep chromosome conformation capture, in situ. They found that the loops in the DNA bring together regions of the genome, allowing them to interact in specific ways.

Until this study, how those interactions took place was a mystery.

“Understanding the way cells organize their genomes during development will help us to understand their ability to respond to stress, injury and disease,” Michael Dyer, PhD (above), Chair of St. Jude’s Developmental Neurobiology Department, co-leader of the Developmental Biology and Solid Tumor Program, and Investigator at Howard Hughes Medical Institute (HHMI), said in the news release. (Photo copyright: St. Jude Children’s Research Hospital.)

The scientists also discovered there were DNA promoters, which encourage gene expression, and also DNA enhancers that increase the likelihood gene expression will occur.

“The research also included the first report of a powerful regulator of gene expression, a super enhancer, that worked in a specific cell at a specific stage of development,” the news release states. “The finding is important because the super enhancers can be hijacked in developmental cancers of the brain and other organs.”

St. Jude goes on to state, “In this study, the scientists determined that when a core regulatory circuit super-enhancer for the VSX2 gene was deleted, an entire class of neurons (bipolar neurons) was eliminated. No other defects were identified. Deletion of the VSX2 gene causes many more defects in retinal development, so the super-enhancer is highly specific to bipolar neurons.”

The St. Jude researchers developed a genetic mouse model of the defect that scientists are using to study neural circuits in the retina, the news release states.

Research Technologist Victoria Honnell (left); Developmental Neurobiologist Jackie Norrie, PhD (center); and Postdoctoral Researcher Marybeth Lupo, PhD (right), work in the St. Jude clinical laboratory of Michael Dyer, PhD, using 3D genomic mapping to study gene regulation during development and disease. (Photo copyright: St. Jude Children’s Research Hospital.)

DNA Loops May Matter to Pathology Sooner Rather than Later

Previous researcher studies primarily used genomic sequencing technology to locate and investigate alterations in genes that lead to disease. In the St. Jude study, the researchers examined how DNA is packaged. If the DNA of a single cell could be stretched out, it would be more than six feet long. To fit into the nucleus of a cell, DNA is looped and bundled into a microscopic package. The St. Jude scientists determined that how these loops are organized regulates how the cell functions and develops.

Scientists around the world will continue studying how the loops in DNA impact gene regulation and how that affects the gene’s response to disease. At St. Jude Children’s Research Hospital, Dyer and his colleagues “used the same approach to create a 3D genomic map of the mouse cerebellum, a brain structure where medulloblastoma can develop. Medulloblastoma is the most common malignant pediatric brain tumor,” noted the St. Jude’s news release.

In addition to providing an understanding of how genes function, these 3D studies are providing valuable insight into how some diseases develop and mature. While nascent research such as this may not impact pathologists and clinical laboratories at the moment, it’s not a stretch to think that this work may lead to greater understanding of the pathology of diseases in the near future.

—Dava Stewart

Related Information:

Researchers Move Beyond Sequencing and Create a 3D Genome

Nucleome Dynamics During Retinal Development

Whole Genome Sequencing

HiPiler: Visual Exploration of Large Genome Interaction Matrices with Interactive Small Multiples

Reorganization of 3D Genome Structure May Contribute to Gene Regulatory Evolution in Primates

An Overview of Methods for Reconstructing 3D Chromosome and Genome Structures from Hi-C Data

Dental Plaque Could Lead to Clinical Laboratory Testing for Biomarkers to Identify Health Risks

Researchers have found that isolating a particular gene within the oral microbiome can reveal a huge amount of useful diagnostic information about a person’s health

Most people don’t think of dental plaque when they think about clinical laboratories. For the vast majority of people, the only diseases that dental plaque bring to mind are those of the mouth:

periodontitis; and,
dental caries.

Samples that are sent to medical labs and pathology laboratories are more often blood or tissue. However, that could be changing, thanks in part to the work being done at the Oral Microbiome and Metagenomics Research Lab (OMMR) at the University of Toronto. (more…)