Introduction
As developers of healthcare technology, it's crucial to question the scientific grounding of new tools. Here, we discuss the robust clinical validation and peer-review process our technology has undergone, reflecting our commitment to evidence-based solutions.
The publications listed here do not include all of our work. Nor do they include evidence submitted to certifying bodies as part of our certification process as a medical device.
Short answer
Yes, Legit.Health has been clinically validated in various healthcare settings by leading specialists in their fields. Our technology has demonstrated its effectiveness in enhancing diagnosis and follow-up, with specific studies focused on certain pathologies to assess sensitivity and precision.
Several of these studies are published in prestigious journals within dermatology, with others in varying stages of publication. Furthermore, we also provide clinical evidence during the process of certification as a medical device, some of which has not been made publicly available.
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Detailed answer
The technology behind Legit.Health is complex and multifaceted. Behind a seemingly simple process for the user, several algorithms interact with each other. Indeed, our technology integrates multiple algorithms to not only diagnose and assess disease severity but also optimize referral accuracy, ensure image quality, and gauge treatment efficacy.
Atopic Dermatitis
Our ASCORAD (Automatic SCORing of Atopic Dermatitis) study, a collaboration with Dr. Ramon Grimalt, was published in the Journal of Investigative Dermatology (JID) Innovations. This study details our approach to automating severity assessment of atopic dermatitis and eczema.
This work is further acknowledged in recent scientific literature, highlighting its potential to revolutionize AD severity assessment.
(...) very promising is the attempt to arrive at an automatic definition of AD severity by using CNNs (...) to achieve a scoring accuracy of erythema, papulation, excoriation, and lichenification severity comparable to that of dermatologists (...). Computational applicative advances in this direction have led to the more recent design of Automatic SCORing Atopic Dermatitis (ASCORAD).
Discover more about ASCORAD from its authors in this webinar (in Spanish).
Hidradenitis Suppurativa
The AIHS4, our novel system for scoring Hidradenitis Suppurativa, is detailed in Skin Research and Technology. This study exemplifies our commitment to developing practical AI solutions for complex dermatological conditions.
The AIHS4 has been echoed in recent scientific publications, such as the following article by the National Research Council of Italy, and the Universities of Palermo and Messina:
(...) to overcome the IHS4, which is time-consuming and subject to variability, the AIHS4 is introduced, using a DL model, Legit.Health-IHS4net, for lesion detection (...). This evidence highlights the utility of AI in evidence-based dermatology, offering a potential tool to empower dermatologists in daily practice and clinical trials.
We also have presented the IHS4 in several congresses. For example, the following image shows our poster at the 2022 Spanish national dermatology congress (AEDV).
Urticaria (Hives)
The Automatic Urticaria Activity Score (AUAS) system has been published in the Journal of Investigative Dermatology (JID) Innovations, showcasing our deep learning-based approach for urticaria severity assessment.
Our work on the AUAS was presented at congresses such as the AEDV annual meeting in the year 2021. The following video shows a short explanation of the AUAS in the congress mentioned above:
Image Quality
We also publish our research regarding the non-diagnostic technology. Such is the case of the Dermatology Image Quality Assessment (DIQA) technology, which ensures the clinical utility of images for remote consultations and clinical trials. This was published in the Journal of the American Academy of Dermatology.
In this video (in Spanish), Taig Mac Carthy, co-author of the publication, shares how the referral artificial intelligence works, including the Image Quality Assurance, at the annual congress of the Spanish Dermatology Academy.