collaborators

6 papers

cs.CL2024

Performance of a large language model-Artificial Intelligence based chatbot for counseling patients with sexually transmitted infections and genital diseases

Nikhil Mehta, Sithira Ambepitiya, Thanveer Ahamad +2

Introduction: Global burden of sexually transmitted infections (STIs) is rising out of proportion to specialists. Current chatbots like ChatGPT are not tailored for handling STI-re…

eess.IV2024

AI enhanced diagnosis of Peyronies disease a novel approach using Computer Vision

Yudara Kularathne, Janitha Prathapa, Prarththanan Sothyrajah +4

This study presents an innovative AI-driven tool for diagnosing Peyronie's Disease (PD), a condition that affects between 0.3% and 13.1% of men worldwide. Our method uses key point…

eess.IV2024

Mpox Screen Lite: AI-Driven On-Device Offline Mpox Screening for Low-Resource African Mpox Emergency Response

Yudara Kularathne, Prathapa Janitha, Sithira Ambepitiya

Background: The 2024 Mpox outbreak, particularly severe in Africa with clade 1b emergence, has highlighted critical gaps in diagnostic capabilities in resource-limited settings. Th…

cs.CV2024

Mpox Detection Advanced: Rapid Epidemic Response Through Synthetic Data

Yudara Kularathne, Prathapa Janitha, Sithira Ambepitiya +3

Rapid development of disease detection models using computer vision is crucial in responding to medical emergencies, such as epidemics or bioterrorism events. Traditional data coll…

eess.IV2024

The Development and Performance of a Machine Learning Based Mobile Platform for Visually Determining the Etiology of Penile Pathology

Lao-Tzu Allan-Blitz, Sithira Ambepitiya, Raghavendra Tirupathi +2

Machine-learning algorithms can facilitate low-cost, user-guided visual diagnostic platforms for addressing disparities in access to sexual health services. We developed a clinical…

cs.CV2024

SynthVision -- Harnessing Minimal Input for Maximal Output in Computer Vision Models using Synthetic Image data

Yudara Kularathne, Prathapa Janitha, Sithira Ambepitiya +3

Rapid development of disease detection computer vision models is vital in response to urgent medical crises like epidemics or events of bioterrorism. However, traditional data gath…