|
|
# 2024 Dermopsy |
|
Traditional methods for diagnosing skin disorders, including cancer and fungal infections, are often slow, costly, and invasive. This solution utilises a reflectance confocal microscope combined with advanced generative AI and deep learning techniques to provide rapid, non-invasive diagnosis. This system performs virtual staining and segments infection samples without the need for traditional histological stains, reducing diagnostic time from days to minutes and lowering costs. Integrated on a cloud platform, it enables dermatopathologists to make swift, accurate decisions, significantly improving patient outcomes
I am a passionate entrepreneur with a background in Computer Engineering, dedicated to solving global challenges through innovative solutions. My work at Dermopsy leverages AI and deep learning to revolutionize the diagnosis of skin diseases, reducing costs and improving outcomes through early detection. I am also the co-founder of ClassNotes, an edtech startup where I’ve helped over 30 million students get access to quality education. With a focus on impactful problem-solving, I continue to push the boundaries of technology for social good.more
Abdullah Arshad, a Computer Engineering graduate from NUST, Pakistan, is a Software Engineer at Osol. Specializing in AI and backend development, he contributed to project Dermopsy, advancing skin disease diagnosis in collaboration with NIDI Skin and BIOMISA.more
Nanyang Technological University
Targeted antimicrobial treatment for resistant infections
