Producción Científica

Over the past decade, Deep Learning (DL) techniques have demonstrated remarkable advancements across various domains, driving their widespread adoption. Particularly in medical image analysis, DL received greater attention for tasks like image segmentation, object detection, and classification. This paper provides an overview of DL-based object recognition in medical images, exploring recent methods and emphasizing different imaging techniques and anatomical applications. Utilizing a meticulous quantitative and qualitative analysis following PRISMA guidelines, we examined publications based on citation rates to explore into the utilization of DL-based object detectors across imaging modalities and anatomical domains. Our findings reveal a consistent rise in the utilization of DL-based object detection models, indicating unexploited potential in medical image analysis. Predominantly within Medicine and Computer Science domains, research in this area is most active in the US, China, and Japan. Notably, DL-based object detection methods have gotten significant interest across diverse medical imaging modalities and anatomical domains. These methods have been applied to a range of techniques including CR scans, pathology images, and endoscopic imaging, showcasing their adaptability. Moreover, diverse anatomical applications, particularly in digital pathology and microscopy, have been explored. The analysis underscores the presence of varied datasets, often with significant discrepancies in size, with a notable percentage being labeled as private or internal, and with prospective studies in this field remaining scarce. Our review of existing trends in DL-based object detection in medical images offers insights for future research directions. The continuous evolution of DL algorithms highlighted in the literature underscores the dynamic nature of this field, emphasizing the need for ongoing research and fitted optimization for specific applications.

In the face of global competition, it is imperative to conduct research on organizational identification in order to maximize employee commitment and organizational loyalty. Understanding how employees identify with their organizations can lead to improved productivity and retention rates. Organizational identification (OI) refers to an individual’s cognitive and affective connection to an organization. It exerts a significant impact on employee attitudes, behaviors, and job performance. It indicates an employee’s emotional bond, connectivity, and recognition with their company. This study aims to examine the impact of OI using bibliometric analysis and a literature review. Despite the topic’s wide use and coverage, the literature lacks quantitative data for bibliographic element analysis, making this study unique. This research fills that knowledge gap. We selected 227 research publications from the ABDC’s 2022 Quality Journals list, with A*, A, and B category journals for 2013 and 2022. The research methodologies employed encompassed trend analysis, scientific mapping, bibliographic coupling, co-occurrence analysis, and so on. Also, this study provides in-depth thematic analysis, content analysis, and a theoretical framework that contributes to the originality of the research. Our research findings offer bibliographic insights that enhance the study of literature. The study found that top journals are more interested in this topic. “Journal of Business Ethics” is very productive, whereas “Academy of Management Journal” holds the most influence. The study also highlights research gaps through quantitative and qualitative research. The study concluded by proposing further investigation and examining the consequences of its findings.