AutoAspira: Automating Breast Cancer Biopsies for Enhanced Diagnosis

Program:

Biomedical Engineering

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.

Team Members

  • Youran Li (Co-lead)

  • Neha Chellu (Co-lead)

  • Sangmita Singh

  • Moonhyung Lee

  • Ishir Sharma

  • Shreya Tiwari

  • Derek Minn

  • Hassan Farah

Project Mentors, Sponsors, and Partners

Course Faculty

Project Links

Additional Project Information

Project Photo

Project Photo Caption:

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.

Project Video

https://jh.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=5f4a42f2-d6c3-4623-8f01-b0d0015c9592