How to spot skin cancer with your phone
id=»article-body» class=»row» section=»article-body»> Early detection of skin cancer could be the difference between a simple mole removal or several rounds of chemotherapy.
SkinVision While skin care advice most commonly comes about at the brink of summer, your skin can get damaged by UV rays no matter what time of year, no matter what the weather. Skin cancer accounts for more diagnoses each year than all other cancers, but the good news is that early detection could be the difference between a simple mole removal or malignant cancer that spreads to other parts of the body.
A handful of smartphone apps and devices claim to aid early detection and keep you on track with regular self-exams. You can capture photos of suspicious moles or marks and track them yourself, or send them off to a dermatologist for assessment. Either way, these apps can be helpful, but they do have limitations, so it’s important to follow conventional wisdom (like wearing sunscreen) to protect yourself. Here’s what you need to know about using your smartphone to detect skin cancer.
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Know the facts about skin cancer
Every year, doctors diagnose more than 4 million cases of nonmelanoma (including basal and squamous cell) skin cancers in the US, and it’s estimated that nearly 200,000 people will receive a melanoma diagnosis in 2019.
Basal and squamous cell skin cancers develop on the outer layers of the skin and are more common, though less harmful, than melanoma.
Melanoma is the deadliest form of skin cancer. It forms in the cells responsible for skin pigmentation, called melanocytes. It’s an aggressive form of cancer and accounts for nearly 10,000 deaths each year. Even with early detection, it can be fatal.
Symptoms of all types of skin cancers include:
Change in the size or color of a mole or other spot on the skin
A new growth on the skin
Odd skin sensations, such as persistent itchiness or tenderness
Spread of pigmentation outside the border of a mole
Skin cancer may develop due to a variety of factors, including genetics and exposure to toxic chemicals, but the clearest connection is that of skin cancer and UV exposure.
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Telemedicine is a growing field, and skin care is not to be left out: Over the last several years, a handful of skin cancer detection apps popped up allowing you to analyze your skin with your smartphone and artificial intelligence algorithms.
Some send photos to a dermatologist, some provide instant feedback and others offer helpful reminders about self-checking your skin and scheduling a doctor’s appointment.
Here are a few you can download on iOS and Android.
Miiskin uses hi-res digital photography to capture magnified photos of moles on your skin.
Miiskin uses mole mapping to analyze your skin. Dermatologists perform mole maps as part of a clinical full-body skin exam, using digital dermoscopy (magnified digital photography) to catch suspicious lesions they may not catch with their own eyes.
Because they’re so high-definition, dermoscopy photos provide much more information than normal digital photos. The developers behind Miiskin wanted to offer a version of this technology to consumers, so they built an app that takes magnified photos of large areas of your skin, for example, your entire leg. According to the website, anyone with an iPhone ($900 at Amazon) with iOS 10 and newer or a phone running Android 4.4 and newer can use Miiskin.
The app stores your photos separate from your smartphone library and allows you to compare moles over time, which is helpful in detecting changes.
Find it: iOS | Android
This app comes from researchers at the University of Michigan (UM) school of medicine and allows you to complete a full-body skin cancer self-exam, as well as create and track a history of moles, growths and lesions.
The app guides you step-by-step on how to complete the exam with graphics and written instructions. UMSkinCheck also comes with access to informational videos and articles, as well as a melanoma risk calculator.
UMSkinCheck also sends push reminders to encourage people to follow-up on their self-exams and check on the lesions or moles they are tracking. You can decide how often you want to see those reminders in the app.
Find it: iOS | Android
With a clip-on camera, MoleScope uses the ABCD method to complete a risk assessment of your moles.
MoleScope Like Miiskin, MoleScope uses magnified images to help people determine whether they should see a dermatologist to get their skin checked.
A product of MetaOptima (a supplier of clinical dermatology technology) MoleScope is a device that attaches to your smartphone and sends photos to a dermatologist for an online checkup.
Though MoleScope itself won’t analyze or diagnose your moles, you can use the ABCD guide in the app to keep tabs on any suspicious moles: The app helps you document your moles with photos and sends them to a dermatologist, who can assess them using the ABCD method:
Asymmetry: the shape of one half doesn’t match the other
Border: edges are bumpy, ragged or blurred
Color: uneven shades of brown, black and tan; odd colors such as red or blue
Diameter: a change in size greater than 6 mm
Unlike Miiskin, you can only take photos of one mole or small areas with a few moles, rather than large areas like your entire chest or back.
Find it: iOS | Android
SkinVision claims to aid early detection of melanoma. The app uses deep learning to analyze photos of your skin and aid in the early detection of skin cancer. The photos are processed through a machine-learning algorithm that filters image layers based on simple, complex, and more abstract functions and If you have any kind of inquiries regarding where and how to use
SkinVision is backed by a scientific board of dermatologists, but Dr. Daniel Friedmann, a dermatologist at Westlake Dermatology in Austin, Texas, told CNET that even an app with prominent support of scientists has limitations.
«I would not recommend that patients avoid these apps, but I would approach their results with cautious skepticism,» Dr. Friedmann said, «and counsel patients that suspicious lesions are best evaluated in-office.»
Find it: iOS | Android
SkinVision uses a machine-learning algorithm to analyze spots on the skin.
SkinVision Read more: The easiest way to protect your skin from the sun is already on your phone
Research is promising, but accuracy isn’t quite there
Of all the apps discussed here, SkinVision seems to have the most research behind it.
A 2014 study on an older version of SkinVision reported 81% accuracy in detecting melanoma, which at the time researchers said was «insufﬁcient to detect melanoma accurately.»
However, a new 2019 study published in the Journal of the European Academy of Dermatology and Venereology determined that SkinVision can detect 95% of skin cancer cases. It’s encouraging to see the company continue to work on app accuracy, as early detection of skin cancer is the number-one way to achieve successful treatment.
In another study, researchers from the University of Pittsburgh, analyzed four smartphone apps that claim to detect skin cancer. We don’t know the exact apps, as they’re named only as Application 1, 2, 3 and 4. Three of the apps used algorithms to send immediate feedback about the person’s risk of skin cancer, and the fourth app sent the photos to a dermatologist.
Unsurprisingly, the researchers found the fourth app be the most accurate. The other three apps were found to incorrectly categorize a large number of skin lesions, with one missing nearly 30% of melanomas, classifying them as low-risk lesions.
A 2018 Cochrane review of prior research found that AI-based skin cancer detection has «not yet demonstrated sufficient promise in terms of accuracy, and they are associated with a high likelihood of missing melanomas.»
To be fair, much of this research took place a few years ago, and the manufacturers may very well have improved their technology since then. More recently, in 2017, a team of researchers at Stanford University announced that their AI does just as well as an in-person dermatologist in detecting skin cancer — showing that these apps and algorithms do hold promise.