Publications by authors named "M Arrivillaga"
Cervical cancer is predominantly caused by human papillomavirus (HPV), with oncogenic strains HPV 16 and 18 accounting for most cases worldwide. Prompt and precise identification of these high-risk HPV types is essential for enhancing patient outcomes as it enables timely intervention and management. However, the existing HPV detection techniques are time-consuming, expensive, and require highly skilled personnel.
View Article and Find Full Text PDFArticle Synopsis
- * The CITOBOT-COL project focuses on creating CITOBOT, a portable cervical cancer screening device, through a human-centered design approach and the use of AI, with four design iterations leading to CITOBOT v4.
- * The device's prototypes were tested by experts and validated through focus groups, emphasizing high-quality image capture, AI algorithms for image classification, and risk assessment to enhance cervical cancer screening in resource-limited communities.
Background: In the Americas, the Pan American Health Organization (PAHO) has promoted initiatives that aim at the elimination of mother-to-child transmitted diseases for over two decades. Although Guatemala has assumed the commitment to improve access and coverage of reproductive and perinatal services, the goals have not yet been reached. Often, the implementation of these efforts is hampered by complexities rooted in social, cultural, and environmental intersections.
View Article and Find Full Text PDFArticle Synopsis
- - The integration of artificial intelligence (AI) in cancer research is growing, particularly in analyzing diagnostic images for cervical cancer, with a review of literature conducted under systematic guidelines.
- - A comprehensive search identified 32 studies from 2009 to 2022, highlighting various AI algorithms like support vector machines, deep learning methods, and their performance in diagnosing cervical cancer from sources such as digital colposcopy and cervicography.
- - Results indicated that deep learning techniques, especially convolutional neural networks, achieved over 97% accuracy in diagnosis, suggesting promising advancements, yet emphasizing the need for more research to confirm these outcomes.