AutoAspira: Automating Breast Cancer Biopsies for Enhanced Diagnosis
- Program: Biomedical Engineering
- Course: EN.580.X12 BME Design Team
Project Description:
Fine Needle Aspiration (FNA) is a biopsy procedure commonly used to diagnose breast cancer in low- and middle- income countries (LMICs) due to its affordability and low invasiveness. Despite its benefits, however, FNA has a high false negative rate of 26.5% in Uganda. This is mainly a result of inadequate sample cellularity, where not enough cells are collected to enable a diagnosis that accurately reflects the lesion pathology. False negative results can be detrimental to patients as they delay treatment initiation, necessitate repeat biopsies, and result in unmanaged disease progression. Therefore, to enhance the accuracy of FNA, this project developed a novel technology that improves the cellularity of FNA samples for use in Uganda and other LMICs. While having the potential to enable more accurate diagnoses, this technology is also minimally invasive, cost-effective, simple to operate, and can be easily incorporated into the current clinical workflow of FNA.
Project Photo:
Cross-sectional view of AutoAspira’s handheld device in use, during breast Fine Needle Aspiration (FNA). Our solution improves mass and cellularity of samples compared to manual FNA, by optimizing combinations of automated motion modalities.
Student Team Members
- Youran (Peggy) Li
- Neha Chellu
- Moonhyung (Bruce) Lee
- Ishir Sharma
- Sangmita Singh
- Hassan Farah
- Derek Minn
- Shreya Tiwari
Project Mentors, Sponsors, and Partners
- Emily Ambinder, MD, MS
- Youseph Yazdi, PhD
- Dr. Yekosani Mitala
- Dr. William Wasswa
- Dr. Robert Lukande
- Dr. Robert Ssekitoleko
- Dr. Amy Ly
- Teja Sathi
- Kim Hwang Yeo
- Aditi Sriram