Segmentation and Quantification of the Left Ventricle to Assess the Ventricular Remodeling post Myocardial Infarction; Accessibility of Robotic Assisted Gynecologic Surgery

Thakore, Meenoti, School of Engineering and Applied Science, University of Virginia
Hossack, John, EN-Biomed Engr Dept, University of Virginia
Wayland, Kent, Engineering and Society, University of Virginia
Huang, Yi, EN-Biomed Engr Dept Engineering Graduate, University of Virginia
Xie, Yanjun, EN-Biomed Engr Dept Engineering Graduate, University of Virginia

An important safety problem in healthcare is diagnostic error, with major errors being found in 10%-20% of autopsies. This suggests that an average of 60,000 patients die annually in the United States from error during diagnosis. Medical diagnosis is a critical task that must be performed efficiently and accurately to ensure adequate patient treatment. Factors including lack of communication, doctor inexperience, and lack of time with patients contribute to these errors. With the complexity and rise of data in healthcare, machine learning (ML) technology has the potential to transform many aspects of patient care including diagnosing diseases. There are studies suggesting that artificial intelligence (AI) can perform as well as or better than humans at key healthcare tasks such as identifying a malignant tumor. Google’s ML applications in healthcare detected breast cancer with 89% accuracy, which is an 11% increase compared to currently used diagnostic methods. ML is a common form of AI that ‘learns’ by training models with data and can be used for tasks like personalized treatment, medical imaging, and robotic surgery. In 2021, about 90% of hospitals in the United States had some sort of AI strategy in place, with 75% of healthcare executives believing that AI initiatives are crucial for hospital success. However, out of that 90%, only 33% of hospitals had systems past the pilot stage in place due to lack of infrastructure or financial means. Lack of access to certain AI technologies, such as robotic surgical systems, may be detrimental to hospitals and patients seeking treatment. Automated medical imaging, a type of AI technology, is becoming more common since it is cost-effective for hospitals. Thus, the focus of this project is to develop a ML model for automated imaging of the heart. However, access to robotic surgery, another type of AI technology, varies greatly between hospitals. Therefore, the disparity in access to robotic surgery technology between demographic groups in the United States will be analyzed to discuss the need for stronger robotic surgery initiatives in hospitals.

BS (Bachelor of Science)
Image Segmentation, Robotic Surgery, Deep Learning

School of Engineering and Applied Science
Bachelor of Science in Biomedical Engineering
Technical Advisor: John Hossack, Yi Huang, Yanjun Xie
STS Advisor: Kent Wayland

All rights reserved (no additional license for public reuse)
Issued Date: