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Melanoma, which accounts for over 70 percent of all skin cancers, occurs when pigment producing cells called melanocytes multiply uncontrollably. This cancer is typically diagnosed through visual inspection of Suspicious Pigmented Lesions (SPLs), and such early detection of lesions in a physician's office are often life-saving. However, there are several disadvantages with this approach, including the high volume of potential lesions one has to biopsy and test before confirming a diagnosis.

To overcome these issues, researchers from MIT and a few other institutions around Boston, have developed a new deep learning tool to more easily identify harmful lesions from photographs taken with a smartphone.

A wide-field image of lesions on a patient are classified with new deep learning techniques to identify suspicious lesions. (MIT)

The paper, published in Science Translational Medicine, describes the development of the tool using a branch of artificial intelligence called deep convolutional neural networks (DCNNs). The researchers trained their tool using over 20,000 images, taken from 133 patients and from publicly available databases. Importantly, the pictures were taken using different personal cameras, to ensure that it would work with real-life examples.

Once the tool was trained using known examples, it demonstrated over 90.3% sensitivity and 89.9% specificity in distinguishing SPLs from nonsuspicious lesions, skin, and complex backgrounds.

One interesting aspect that distinguishes this tool from others is based on identifying lesions using the 'ugly duckling' criteria. This method, currently used by dermatologists, assumes that most moles on an individual appear similar to each other and are typically non-suspicious, with different-looking moles classified as 'ugly ducklings' for further investigation.

Criteria for classifying lesions as suspicious or non-suspicious include their circularity, convexity, inertia, intensity, and size.

By training the system on different features of moles such as circularity, size, and intensity, the accuracy of prediction was greatly improved: the algorithm matched the consensus of seasoned dermatologists 88 percent of the time, and matched individual dermatologists' opinion 86 percent of the time. If the technology is confirmed, it could lead to significant savings in terms of clinical time and cost involved in the imaging and analysis of individual lesions.

"Our research suggests that systems leveraging computer vision and deep neural networks, quantifying such common signs, can achieve comparable accuracy to expert dermatologists", said Soenksen, the first author on the paper, 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".

Paper in Science Translational Medicine: Using deep learning for dermatologist-level detection of suspicious pigmented skin lesions from wide-field images

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Software Spots Suspicious Skin Lesions on Smartphone Photos

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Melanoma, which accounts for over 70 percent of all skin cancers, occurs when pigment producing cells called melanocytes multiply uncontrollably. This cancer is typically diagnosed through visual inspection of Suspicious Pigmented Lesions (SPLs), and such early detection of lesions in a physician's office are often life-saving.

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Source: Medgadget.com

Article Author: Rukmani Sridharan

Author Affiliations: Royal College of Surgeons, Ireland

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Skin damage often goes unnoticed to the naked eye.  Yes, we may see moles and freckles show up at times, but most of the damage lies beneath the surface.  I know that sun damage prevention is key, but honestly a lot of the damage can happen during child and adolescence; a time when vanity takes over.  As teenagers and young adults, we want to be bronze and often that happens in tanning beds.  Unfortunately, many people pay a lot of money to develop skin cancer.

I believe this new technology can be lifesaving.  I think in today’s world, telemedicine is starting to become more popular and practical.  This app would allow for monitoring and early detection of melanoma which can save lives.  I think this app and use of AI technology will open the door for future use of home screening.

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Sridharan, R. (2021, April 20). Medgadget. Retrieved September 24, 2021, from https://www.medgadget.com/2021/04/software-spots-suspicious-skin-lesions-on-smartphone-photos.html.

Soenksen LR, Kassis T, Conover ST, Marti-Fuster B, Birkenfeld JS, Tucker-Schwartz J, Naseem A, Stavert     RR, Kim CC, Senna MM, Avilés-Izquierdo J, Collins JJ, Barzilay R, Gray ML. Using deep learning for        dermatologist-level detection of suspicious pigmented skin lesions from wide-field images. Sci Transl Med. 2021 Feb 17;13(581) doi: 10.1126/scitranslmed.abb3652.

Tronier, D., & Harrison, J. (2019). Promoting the Use of Health Apps to Monitor Signs of Skin                 Cancer. Dermatological Nursing, 18(4), 42–45.

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https://www.medgadget.com/2021/04/software-spots-suspicious-skin-lesions-on-smartphone-photos.html

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4/20/2021

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Melanoma is a form of skin cancer that causes the majority of skin cancer related deaths (Soenksen et al., 2021).  Phone apps for dermatological use have been very helpful in melanoma detection.  Taking serial photographs can aid in showing how the skin develops and changes over time and allow for comparison of lesions (Tronier & Harrison, 2019).  Photographs of larger areas also allow the skin to be mapped especially for patients who have 50 or more moles, as this is an increased risk factor for melanoma (Tronier & Harrison, 2019).  Having such a large number of moles can be hard to keep track of and photographs over time allow for more accurate monitoring.

Scientists have recently developed a phone app that uses deep convolutional neural networks (DCNN), a form of artificial intelligence, to interpret data from a picture to screen for melanoma (Soenksen et al., 2021).  The app allows patients to take a picture of a suspicious pigmented lesion (SLP) (Soenksen et al., 2021).  The photographs are analyzed to determine if the SLP is benign or needs further evaluation with a biopsy to detect for melanoma (Soenksen et al., 2021).  They basically allow lesions on the skin to be compared and determine which ones could be suspicious for cancer.  The DCNN analyzes, compares and determines which lesions need to be further tested.  This technology can not only save lives, but also reduce healthcare costs by determining which moles need to be biopsied and capture melanomas at earlier stages.

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Pros:
-earlier detection and possibly life saving
-potentially cost effective-earlier detection can decrease the healthcare cost
-potentially user friendly-depends on the patients tech savy skills
-this app can be used in primary care office as a way to triage the need for dermatology referral which allows for more patients to be screened
-this opens the door for more future in patient hand technology for detection of health problems

Cons:
-how reliable is the app-are they are lot of bugs?
-limited technology-on the part of the patient and equipment; does the patient have a smart phone?
-how many dermatologists actually use this technology?
-cost-physician and patient upfront cost and is there a monthly fee for the app?
-patient and physician buy in-could take time and possibly a lot of resources.

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My top 5 strengths: deliberative, responsibility, consistency, relator and context.

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