A Comprehensive Bibliometric Analysis of Disability Research in Saudi Arabia: Trends, Gaps, and Future Directions

Deleted Journal, Journal Year: 2025, Volume and Issue: 4(2)

Published: Jan. 1, 2025

This study employed bibliometric analysis using the Scopus database to evaluate Saudi disability research (SDR). From an initial dataset of 17,102 documents (0.54% global output), scope was refined 13,246 data-driven publications for detailed examination. Trends, themes, and collaborations were analyzed R packages VOSviewer. Metrics such as citations, total link strength (TLS), thematic mapping used identify key contributors, emerging topics, international partnerships. authors demonstrated strong collaboration, with 59.53% involving co-authorships, particularly United States, Egypt, India. Prolific contributors include Alkuraya, F.S. leading institutions King Saud University. Key motor themes “quality life” “Alzheimer’s disease,” while “deep learning” “molecular docking” reflect a shift toward advanced technologies. Machine learning is trending topic applied in early diagnosis, drug discovery, rehabilitation conditions Alzheimer’s disease, autism, epilepsy. These findings underscore evolving priorities relevance SDR.

Language: Английский

A new approach combining CNN, RNN, and an improved Otsu threshold method for detecting hand gestures in people with thumb finger size problems and hand tremors

AIP Advances, Journal Year: 2025, Volume and Issue: 15(3)

Published: March 1, 2025

The recognition of hand gestures involves the application mathematical algorithms to detect human movements, with diverse applications in communication for hearing impaired, human–computer interaction, autonomous driving, and virtual environments. This research presents a comprehensive approach identifying dynamic gestures, which is particularly beneficial individuals finger disabilities. In addition, those tremors may encounter challenges when using interaction devices. proposed technique enhances sensitivity these devices through an advanced Otsu segmentation method. It begins by isolating from complex backgrounds this sophisticated algorithm incorporates motion data derived RGB video sequences. are then transformed into texture contour characteristics, subsequently input hybrid architecture that combines convolutional neural network (CNN) recurrent (RNN). Our findings demonstrate method achieves superior results existing alternatives can joint interactions high sensitivity. When comparing traditional our method, indicate improvement 6.3% accuracy CNN RNN classifiers. performance novel has been evaluated compared metrics, yielding significant results.

Language: Английский

Deep learning in neurosurgery: a systematic literature review with a structured analysis of applications across subspecialties

Frontiers in Neurology, Journal Year: 2025, Volume and Issue: 16

Published: April 16, 2025

This study systematically reviewed deep learning (DL) applications in neurosurgical practice to provide a comprehensive understanding of DL neurosurgery. The review process included systematic overview recent developments technologies, an examination the existing literature on their neurosurgery, and insights into future also summarized most widely used algorithms, specific practice, limitations, directions. An advanced search using medical subject heading terms was conducted Medline (via PubMed), Scopus, Embase databases restricted articles published English. Two independent neurosurgically experienced reviewers screened selected articles. A total 456 were initially retrieved. After screening, 162 found eligible study. Reference lists all checked, 19 additional 181 divided 6 categories according subspecialties: general neurosurgery (n = 64), neuro-oncology 49), functional 32), vascular 17), neurotrauma 9), spine peripheral nerve 10). leading procedures which algorithms commonly brain stimulation subthalamic thalamic nuclei localization 24) group; segmentation, identification, classification, diagnosis tumors 29) neuronavigation image-guided 13) group. Apart from various video image datasets, computed tomography, magnetic resonance imaging, ultrasonography frequently datasets train groups overall 79). Although there few studies involving 2016, research interest began increase 2019 has continued grow 2020s. can enhance by improving surgical workflows, real-time monitoring, diagnostic accuracy, outcome prediction, volumetric assessment, education. However, integration involves challenges limitations. Future should focus refining models with wide variety developing effective implementation techniques, assessing affect time cost efficiency.

Language: Английский