The paper "Machine Learning Applications in Optical Fiber Sensing: A Research Agenda" by Erick Reyes-Vera et al. explores the integration of machine learning (ML) techniques with optical fiber sensors to enhance their performance and applications. The authors conduct a bibliometric analysis using the PRISMA 2020 set to identify research trends in this field. The study evaluates the quantity and quality of publications from Scopus and Web of Science databases, focusing on key concepts, trends, and advances over time. Key findings include: - Deep learning techniques and fiber Bragg gratings (FBGs) have been extensively researched for infrastructure monitoring, particularly in structural health monitoring (SHM). - The use of novel materials like graphite for designing fiber optic sensors is a significant limitation, presenting opportunities for future research. - The historical evolution of scientific literature shows a growing interest in the topic, with a notable increase in publications since 2018. - The most cited articles and authors highlight advancements in sensor classification, vehicle classification, and earthquake detection using deep learning models. - Journals such as IEEE Sensors Journal, Sensors, and Journal of Lightwave Technology are prominent in publishing research on fiber optic sensors and ML. - Countries like the United States and China are leading in academic contributions, with a focus on leak detection, structural health monitoring, and nanoparticle characterization. - Thematic evolution reveals a shift from initial applications in water leak detection to more specialized areas like structural health monitoring and nanoparticle characterization. - Emerging keywords such as gold nanoparticles and SHM indicate growing interest in biomedicine and nanotechnology. - The research agenda suggests future directions in areas like pattern recognition, wireless sensor networks, and fault detection, highlighting the potential for interdisciplinary collaboration and innovation. The study provides a comprehensive overview of the current state and future directions of machine learning applications in optical fiber sensing, emphasizing the need for continued research and development in this field.The paper "Machine Learning Applications in Optical Fiber Sensing: A Research Agenda" by Erick Reyes-Vera et al. explores the integration of machine learning (ML) techniques with optical fiber sensors to enhance their performance and applications. The authors conduct a bibliometric analysis using the PRISMA 2020 set to identify research trends in this field. The study evaluates the quantity and quality of publications from Scopus and Web of Science databases, focusing on key concepts, trends, and advances over time. Key findings include: - Deep learning techniques and fiber Bragg gratings (FBGs) have been extensively researched for infrastructure monitoring, particularly in structural health monitoring (SHM). - The use of novel materials like graphite for designing fiber optic sensors is a significant limitation, presenting opportunities for future research. - The historical evolution of scientific literature shows a growing interest in the topic, with a notable increase in publications since 2018. - The most cited articles and authors highlight advancements in sensor classification, vehicle classification, and earthquake detection using deep learning models. - Journals such as IEEE Sensors Journal, Sensors, and Journal of Lightwave Technology are prominent in publishing research on fiber optic sensors and ML. - Countries like the United States and China are leading in academic contributions, with a focus on leak detection, structural health monitoring, and nanoparticle characterization. - Thematic evolution reveals a shift from initial applications in water leak detection to more specialized areas like structural health monitoring and nanoparticle characterization. - Emerging keywords such as gold nanoparticles and SHM indicate growing interest in biomedicine and nanotechnology. - The research agenda suggests future directions in areas like pattern recognition, wireless sensor networks, and fault detection, highlighting the potential for interdisciplinary collaboration and innovation. The study provides a comprehensive overview of the current state and future directions of machine learning applications in optical fiber sensing, emphasizing the need for continued research and development in this field.