Computer aided diagnosis in mammograms for breast cancer screening

Diagnóstico asistido por computadora en mamografías para detección del cáncer de mama

Abstract

Securing access to quality healthcare services, particularly for essential screening tests like mammograms, presents a significant challenge in developing nations. Women often encounter extensive waiting periods, sometimes extending for several months, to undergo a mammogram. This crucial test plays a pivotal role in the early detection of breast cancer, where timely diagnosis is crucial for effective treatment and enhanced survival prospects. Delays in obtaining a diagnosis can significantly impact the health of patients and their well-being, underscoring the importance of early detection. Compounding these challenges is the scarcity of resources and healthcare professionals, which hinders swift and efficient access to preventive care. Such constraints underscore the pressing need for improvements in the interpretation of radiological studies and a reduction in the workload of imaging specialists. These improvements would not only optimize interdisciplinary collaboration but also enhance patient care, particularly for critical screenings like mammograms (3).

Technological advancements, particularly in artificial intelligence (AI), have significantly influenced numerous sectors, including healthcare (4). Yet, in areas such as Latin America, where the healthcare infrastructure and resources may not fully support the integration of advanced diagnostic techniques based on informatic tools, the potential of these innovations seems to be underutilized. A promising application of technology is in Computer-Aided Diagnosis (CAD) for breast cancer screening with mammograms. CAD leverages vast datasets and pattern recognition algorithms to detect anomalies within mammograms, potentially facilitating the early identification of lesions. Given that breast cancer ranks among the most prevalent diseases affecting women globally, early detection is vital for enhancing patient survival rates and quality of life. Given these challenges, our study aims to examine the latest developments in artificial intelligence technology as a supplementary tool in CAD strategies for mammograms. Our goal is to foster better interdisciplinary collaboration among clinicians, radiologists, and pathologists through a state of the art (SOTA) review of the topic. By doing so, we anticipate streamlining the diagnostic workflow and elevating the efficiency of breast cancer detection and treatment processes.