Bostel Technologies
Bostel Technologies is a clinical-stage healthcare company headquartered in Atlanta that has developed Melody, a patented Dual Deep Learning technology platform designed to improve the diagnosis of skin cancer by dermoscopy. The technology uniquely combines traditional vision-based machine learning with sonification, converting image data into sound waves to enhance diagnostic accuracy. Melody addresses the significant unmet medical need in skin cancer detection, which currently requires many biopsies to identify melanoma. The platform is clinically validated with proven high sensitivity and specificity, empowering healthcare professionals in hospitals, clinics, pharmacies, and point-of-care locations to efficiently diagnose melanoma and other skin cancers. Bostel's technology is supported by multiple peer-reviewed publications and a U.S. patent, positioning the company as an innovator in AI-driven dermatological diagnostics with a focus on improving patient outcomes and reducing diagnostic burdens.
Bostel Technologies
Bostel Technologies is a clinical-stage healthcare company headquartered in Atlanta that has developed Melody, a patented Dual Deep Learning technology platform designed to improve the diagnosis of skin cancer by dermoscopy. The technology uniquely combines traditional vision-based machine learning with sonification, converting image data into sound waves to enhance diagnostic accuracy. Melody addresses the significant unmet medical need in skin cancer detection, which currently requires many biopsies to identify melanoma. The platform is clinically validated with proven high sensitivity and specificity, empowering healthcare professionals in hospitals, clinics, pharmacies, and point-of-care locations to efficiently diagnose melanoma and other skin cancers. Bostel's technology is supported by multiple peer-reviewed publications and a U.S. patent, positioning the company as an innovator in AI-driven dermatological diagnostics with a focus on improving patient outcomes and reducing diagnostic burdens.