Kaposi Sarcoma (KS) is the most common cancer and a leading cause of cancer-related death among men in Malawi, Mozambique, and Uganda, and is the second leading cause of cancer-related death among men in Kenya. Cancer systems are overburdened in East Africa. Generally, wait times are quite long due to the enormous disease burden and lack of healthcare personnel. Innovative and affordable technologies and research are much needed to reduce wait times and improve care.
Current treatment monitoring for KS is imprecise, as clinical assessment of lesion area and height, using a ruler, are subject to human variation. Manual measurements can also take a long time and the measurement performs poorly on persons with dark skin, leading to racial inequities in adverse effects.
Despite declines in the incidence of KS in the U.S., incidence among Blacks/African Americans has remained stable or increased. Worse, Black/African American people with KS have significantly higher mortality than other groups
To address these challenges, we propose to test, refine, and validate a new technology, SkinScan3D (SS3D) as pictured above. SS3D is a simple, low-cost (67,000 KSH), and user-friendly device that combines liquid lens technology and artificial intelligence, providing high-resolution 3D images of KS skin lesions that incorporate lesion height and volume.
This device will be piloted with 100 participants in Kenya and Uganda, across a variety of clinical settings (Uganda Cancer Institute, Jaramogi Oginga Odinga Teaching & Referral Hospital, and Chulaimbo Level IV Hospital).
PRIME-KS has three main components: 1) Devise design and protocol refinement. Because this device has not yet been tested in the East African context, the team will engage patients and clinical staff via focus groups, surveys, and design workshops to ensure it is feasible and acceptable for clinical use; 2) Reproducibility and accuracy of SS3D vs. standard of care. The team will test how the device works as compared to the regular standard of care (ruler-based); 3) Clinical validation. Collect data regarding the time and cost of using the device vs. not using the device in a clinical setting.
