HUVY™
Huvy has developed an AI-based skin cancer screening application that analyzes dermoscopic images of pigmented lesions in under 15 seconds, categorizing them based on risk levels. This technology addresses the challenge of limited access to dermatological care by providing initial diagnostic support, enabling healthcare professionals to prioritize patients who may require urgent attention.
- Artificial Intelligence
- Digital Health
- Healthcare Technology
- Software Only
Funding
Raised to date
$780KRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Access to dermatological care is limited due to a shortage of specialists and increasing wait times, leading to delayed diagnoses and increased risks for patients with skin lesions. General practitioners and other healthcare providers often lack the specialized training and tools needed for accurate early detection of melanoma and other skin cancers.
Solution
Huvy provides an AI-powered platform that analyzes dermoscopic images of skin lesions to assist healthcare professionals in the early detection of melanoma. The platform uses a deep-learning algorithm trained on a large dataset of clinical images to classify lesions based on their risk level in under 15 seconds. By providing a rapid, objective assessment of suspicious moles, Huvy enables primary care physicians, nurses, and pharmacists to prioritize patients for specialist referral, facilitating earlier intervention and improved patient outcomes. The Huvy platform integrates a 3D skin mapping feature and patient-specific alerts to optimize follow-up care.
Target Audience
The primary target audience includes general practitioners, nurses, pharmacists, and other healthcare professionals who provide primary care and need a tool to assist in the early detection of melanoma.
Features
- AI-driven analysis of dermoscopic images, providing results in under 15 seconds
- Risk stratification of lesions into three categories: benign, monitor, and consult a dermatologist
- 94% accuracy in clinical trials (96% sensitivity, 72% specificity)
- 3D skin mapping for optimized patient follow-up
- Patient-specific alerts for regular monitoring
- Hosted on secure servers