Emerging trends and research foci of deep learning in spine: bibliometric and visualization study

Abstract

As the cognition of spine develops, deep learning (DL) presents a tremendous potential and function as an advantageous tool in this eld. In this study, bibliometrics and visual methods were adopted with a retrieval of Web of Science to provide a comprehensive overview of DL-spine research. VOSviewer and Citespace were primarily used for literature measurement and knowledge graph analysis. A total of 273 studies was retrieved focusing on DL in spine with a sum of 2407 citations, and the global total number of articles published showed a continuous increasing trend. China was the country with the largest number of publications, while USA was the country with the most citation. The top 2 journals were "European spine journal" and "Medical image analysis", and the most involved research area was Radiology Nuclear Medicine Medical Imaging. VOSviewer visually presented three clusters into "segmentation", "area", and "neural network". And CiteSpace indicated the keywords with the longest use were "magnetic resonance image" and "lumbar", while "agreement" and "automated detection" were the most popular keywords. The stage of DL-spine research is still in its infancy and its future is bright. Intercontinental cooperation, extensive application and more interpretable algorithms will exert more vitality in this eld.

Revolutionizing Spinal Care: Current Applications and Future Directions of Artificial Intelligence and Machine Learning

“…The advent of AI has irrevocably changed numerous fields, and healthcare is no exception [ 1 , 2 , 3 , 4 , 5 , 6 ]. AI—broadly defined as the ability of a computer or computer-controlled robot to perform tasks commonly associated with intelligent beings—has introduced a level of sophistication in data analysis and decision making that was previously unattainable.…”

“…The roots of AI in healthcare are intertwined with the history of AI itself ( Table 1 ). The initial AI wave in the mid-20th century saw promising applications in healthcare, but many of these early attempts were hampered by the limited computational power and data availability of the time [ 1 , 2 , 3 , 4 , 5 , 6 , 8 ]. However, the situation has drastically changed.…”

“…The management of spinal diseases is on the cusp of a transformative shift precipitated by the emergence and integration of artificial intelligence (AI) and machine learning (ML) into the realm of standard medical care [ 1 , 2 , 3 , 4 , 5 , 6 ]. Rather than being a vision of the distant future, this shift towards an intelligence-based spinal care model is well underway, promising a host of potential applications, including diagnosis, treatment, and the anticipation of adverse events [ 1 , 2 , 3 , 4 , 5 , 6 ].…”

“…The advent of AI and ML in healthcare is not an isolated phenomenon but rather the logical outcome of decades of accumulated scientific and technological progress within computational and healthcare disciplines. AI and ML have transcended mere theoretical promise; they are already delivering tangible results in the present day [ 1 , 2 , 3 , 4 , 5 , 6 ]. One of the most striking examples of their efficacy lies in the realm of spinal imaging [ 7 ].…”

A Hybrid Method of 1D-CNN and Machine Learning Algorithms for Breast Cancer Detection

“…DL is a type of ML that utilizes artificial neural networks to model complicated relationships between inputs and outputs. Large datasets may be utilized to train DL algorithms, which makes it possible to automatically identify and classify breast cancers this to increases identification accuracy and reduces the need for manual evaluation 3 .…”