Bibliometric Analysis Of Prostate Cancer And Deep Learning

Аннотация

One of the most common tumors in men around the world, prostate cancer presents considerable obstacles to early detection, precise diagnosis, and efficient treatment planning. Innovative approaches to better managing prostate cancer have been made possible by recent developments in artificial intelligence (AI), especially deep learning. Deep learning algorithms provide previously unheard-of precision in pattern recognition, predictive modeling, and decision support systems due to their capacity to evaluate complicated datasets like clinical records, genetic profiles, and medical imaging. To thoroughly assess the corpus of literature on the relationship between deep learning and prostate cancer, this study performs a bibliometric analysis. Through an analysis of publishing patterns, highly referenced publications, well-known authors, top journals, and new fields of study, this report offers a thorough summary of the state of deep learning applications in prostate cancer.

The results of the study demonstrate the advancements made in applying deep learning to problems including survival analysis, tumor detection, segmentation, and treatment response prediction. These developments could improve patient outcomes, optimize treatment plans, and increase the accuracy of diagnoses. Additionally, this study highlights important research gaps and avenues for further investigation, promoting multidisciplinary cooperation among radiologists, oncologists, and AI researchers. This work intends to direct future research efforts and expedite the incorporation of AI-driven solutions into clinical practice by illuminating the revolutionary potential of deep learning in the management of prostate cancer.