J&J, SciBase to pilot AI-driven skin monitoring for infants

Nevisense Go, a portable, non-invasive tool that uses electrical impedance spectroscopy measurements for skin barrier assessment, will be studied in clinical practice at hospitals in Switzerland.


With 20% of children developing atopic dermatitis, SciBasedeveloper of the AI technology,says the ability to predict those that are at high risk for this type of eczema could significantly expand treatments before it develops – possibly preventing the disease and down-the-line conditions that can persist through adulthood.

The Swedish company, which develops augmented intelligence-based solutions for skin disorders, announced a two-year collaboration with Johnson & Johnson. The study is a validation test of how SciBase’s portable, non-invasive tool can help predict atopic dermatitis in infants.

“We believe we can prevent them from getting on this lifetime of diseases that often occur,” said Simon Grant, CEO of SciBase, in a video released with the announcement.

A dysfunctional skin barrier has been demonstrated in allergic and autoimmune conditions and diseases, and studies have shown increases in allergic and autoimmune diseases worldwide, according to the SciBase website. 

“I work every day with the management of different allergic diseases in children, and it is a growing problem,” said Dr. Caroline Roduit, the principal study investigator, in the announcement.

“Allergic diseases have a natural progression with atopic dermatitis being the first to manifest, often already in infancy, followed by other allergic diseases, such as food allergy and allergic asthma,” she said. “The ability to identify these children early will help to develop preventive strategies for allergic diseases,” she explained.

Skin barrier assessment is a newer application for SciBase’s EIS technology, which is approved in the U.S. and European Union for use in melanoma and non-melanoma skin cancer detection. 

In 2020, SciBase announced Nevisense would be used to measure skin properties including barrier function in a study by Mt. Sinai Department of Pediatric Allergy study looking at how the type of birth affects the risk of developing allergies.

Grant said that once developed, the solution could be used in post-natal care by clinicians and for at-home monitoring by parents.


The use of AI to predict patient conditions is a growing area of collaboration and development.

Research published last year about a data study involving more than 20 hospitals worldwide indicated the ability of an algorithm to predict COVID-19 patients’ oxygen levels with a specificity of more than 88%.

Combining AI with precision medicine could also lead to personalized diagnoses, an area where big healthcare technology investments are being made. 

At Sioux Falls, South Dakota-based Sanford Health, leveraging machine learning to analyze data and identify patients that could benefit from proactive treatment is a priority, said Matt Hocks, COO.

“Precision medicine will allow us to concentrate our efforts on prevention and early screening, diagnosis and care that will help keep our patients healthy and thriving for generations to come,” he told Healthcare IT News while discussing health IT investments.


“The promise of the test is that it is non-invasive and can be used widely – in this study the test will be performed using Nevisense Go in the home of the infant,” Grant said in the announcement. “We see the collaboration as an important step in shaping a future where medical technology is accessible, non-invasive and personalized.”

Andrea Fox is senior editor of Healthcare IT News.
Email: [email protected]

Healthcare IT News is a HIMSS publication.

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