In the realm of technology, Artificial Intelligence (AI) has emerged as a novel domain that industries are increasingly allocating their resources towards.
Healthcare institutions are increasingly integrating artificial intelligence (AI) into various domains of healthcare delivery, including the realm of breast cancer screenings.
Dr. Shruthi Ram, a medical doctor, currently holds the position of Section Chief within the Division of Breast Imaging at Rochester Regional Health. In her professional capacity, she elucidates the manner in which artificial intelligence (AI) is facilitating radiologists and imaging providers in enhancing the quality of care provided to their patients.
Integrating AI
Artificial intelligence (AI) employs a methodology of training machines by providing them with extensive quantities of data. The utilization of artificial intelligence (AI) in breast cancer imaging enables the rapid analysis of images and the detection of cancers at a significantly accelerated rate compared to human learning capabilities. Throughout the duration of a radiologist’s professional trajectory, it is not uncommon for them to assess a substantial volume of mammograms, typically ranging from 30,000 to 40,000. Artificial intelligence (AI) possesses the capability to efficiently analyze vast quantities of images and conduct comparative assessments at a significantly accelerated pace compared to human counterparts.
Rochester Regional Health has established a collaboration with Volpara Analytics in order to implement AI-integrated software at their breast imaging centers. The software provides a means to enhance the evaluation of breast density and analyze the efficacy of each mammogram.
Approximately 50% of individuals undergoing mammography exhibit dense breast tissue, thereby posing a challenge in distinguishing between dense tissue and cancerous tissue.
According to Dr. Ram, mammogram readings are subject to variability in interpretation when assessed by different radiologists, as they are based on the subjective opinion of the radiologist. The AI software developed by Volpara Analytics aids in mitigating the variability to a degree that empirical research indicates is equivalent to human performance.
The breast imaging centers affiliated with Rochester Regional Health have also implemented the utilization of artificial intelligence software to evaluate the caliber of each study. Mammograms may not yield optimal breast imaging results due to:
- Insufficient tissue is observed in the provided image.
- Insufficient compression during the imaging process results in the manifestation of motion artifacts, which are caused by excessive movement.
The utilization of artificial intelligence enables radiology technologists to compare their images with a vast collection of mammograms, obtain personalized feedback for each examination, and ultimately enhance the quality of mammography provided at each breast imaging facility.
According to Dr. Ram, the objective is to detect malignancies, provide assistance to the patient, and identify the cancer at its earliest and most manageable stage. It is imperative to maintain a focus on this objective. The utilization of artificial intelligence (AI) as a supplementary tool for reviewing the work of radiologists has the potential to be highly advantageous in the detection of breast cancer.
Looking to the future
Recent studies have demonstrated that artificial intelligence (AI) is making notable advancements in the field of mammogram and breast imaging interpretation, resulting in a reduction of false positive findings in certain investigations.
Although research is still being conducted, the utilization of AI-only mammography reading is currently in the investigational stage and has not yet been implemented for widespread application in patient care.
According to Dr. Ram, healthcare providers should prioritize the patients and their access to optimal care and treatment.
Dr. Ram emphasized the importance of annual screening mammograms commencing at the age of 40, in light of the remarkable advancements in technology employed in mammography screenings. This recommendation is based on empirical evidence demonstrating its efficacy in preserving human lives.
