“Dr.J”: An Artificial Intelligence Powered Ultrasonography Breast Cancer Preliminary Screening Solution

Breast cancer ranks top incidence rate among all malignant tumors for women, globally. Early detection through regular preliminary screening is critical to decreasing the breast cancer’s fatality rate. However, the promotion of preliminary screening faces major limitations of human diagnosis capacity, cost, and technical reliability in China and most of the world. To meet these challenges, we developed a solution featuring an innovative division of labor model by incorporating artificial intelligence (AI) with ultrasonography and cloud computing. The objective of this research was to develop a solution named “Dr.J”, which applies AI to process real-time video live feed from ultrasonography, which is physically safe and more suitable for Asian women. It can automatically detect and highlight the suspected breast cancer lesions and provide BI-RADS (Breast Imaging-Reporting and Data System) ratings to assist human diagnosis. “Dr.J” does not require its frontline operators to have prior medical or IT background and thus significantly lowers manpower threshold for preliminary screening promotion. Furthermore, its cloud computing platform can store detailed breast cancer data such as images and BI-RADS ratings for further essential needs in medical treatment, research and health management, etc. as well as establishing a hierarchy medical service network for this disease. Therefore, “Dr.J” significantly enhances the availability and accessibility of preliminary screening service for breast cancer at grassroots.

@article{Zhou2020,
  title     = {“Dr.J”: An Artificial Intelligence Powered Ultrasonography Breast Cancer Preliminary Screening Solution},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {7},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Zhenzhong Zhou and Xueqin Xie and Alex L. Zhou and Zongjin Yang and Muhammad Nabeel and Yongjie Deng and Zhongxiong Feng and Xiaoling Zheng and Zhiwen Fang},
  doi       = {10.14569/IJACSA.2020.0110702},
  url       = {https://doi.org/10.14569/IJACSA.2020.0110702}
}