Artificial Intelligence in Neurosurgery: A Bibliometric Analysis

El-Hajj, Victor Gabriel; Gharios, Maria; Edström, Erik; Elmi-Terander, Adrian

doi:10.1016/j.wneu.2022.12.087

Cited by 21 publications

(9 citation statements)

References 29 publications

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“…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.…”

Section: Historical Context and Evolutionmentioning

confidence: 99%

“…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.…”

Section: Historical Context and Evolutionmentioning

confidence: 99%

“…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 ].…”

Section: Introductionmentioning

confidence: 99%

“…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 ].…”

Section: Introductionmentioning

confidence: 99%

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

Yagi

Yamanouchi

Fujita

et al. 2023

JCM

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Artificial intelligence (AI) and machine learning (ML) are rapidly becoming integral components of modern healthcare, offering new avenues for diagnosis, treatment, and outcome prediction. This review explores their current applications and potential future in the field of spinal care. From enhancing imaging techniques to predicting patient outcomes, AI and ML are revolutionizing the way we approach spinal diseases. AI and ML have significantly improved spinal imaging by augmenting detection and classification capabilities, thereby boosting diagnostic accuracy. Predictive models have also been developed to guide treatment plans and foresee patient outcomes, driving a shift towards more personalized care. Looking towards the future, we envision AI and ML further ingraining themselves in spinal care with the development of algorithms capable of deciphering complex spinal pathologies to aid decision making. Despite the promise these technologies hold, their integration into clinical practice is not without challenges. Data quality, integration hurdles, data security, and ethical considerations are some of the key areas that need to be addressed for their successful and responsible implementation. In conclusion, AI and ML represent potent tools for transforming spinal care. Thoughtful and balanced integration of these technologies, guided by ethical considerations, can lead to significant advancements, ushering in an era of more personalized, effective, and efficient healthcare.

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Section: Historical Context and Evolutionmentioning

confidence: 99%

Section: Historical Context and Evolutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Yagi

Yamanouchi

Fujita

et al. 2023

JCM

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“…In recent years, the integration of machine learning (ML) into spine surgery has shown promise in enabling personalized risk predictions [1,2]. These advancements could improve patient outcomes, streamline surgical decision-making, reduce costs, and optimize medical management [3].…”

Section: Introductionmentioning

confidence: 99%

Limitations in Evaluating Machine Learning Models for Imbalanced Binary Outcome Classification in Spine Surgery: A Systematic Review

Ghanem,

Ghaith,

El-Hajj

et al. 2023

Brain Sciences

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Clinical prediction models for spine surgery applications are on the rise, with an increasing reliance on machine learning (ML) and deep learning (DL). Many of the predicted outcomes are uncommon; therefore, to ensure the models’ effectiveness in clinical practice it is crucial to properly evaluate them. This systematic review aims to identify and evaluate current research-based ML and DL models applied for spine surgery, specifically those predicting binary outcomes with a focus on their evaluation metrics. Overall, 60 papers were included, and the findings were reported according to the PRISMA guidelines. A total of 13 papers focused on lengths of stay (LOS), 12 on readmissions, 12 on non-home discharge, 6 on mortality, and 5 on reoperations. The target outcomes exhibited data imbalances ranging from 0.44% to 42.4%. A total of 59 papers reported the model’s area under the receiver operating characteristic (AUROC), 28 mentioned accuracies, 33 provided sensitivity, 29 discussed specificity, 28 addressed positive predictive value (PPV), 24 included the negative predictive value (NPV), 25 indicated the Brier score with 10 providing a null model Brier, and 8 detailed the F1 score. Additionally, data visualization varied among the included papers. This review discusses the use of appropriate evaluation schemes in ML and identifies several common errors and potential bias sources in the literature. Embracing these recommendations as the field advances may facilitate the integration of reliable and effective ML models in clinical settings.

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Extended reality in cranial and spinal neurosurgery – a bibliometric analysis

Buwaider,

El-Hajj,

Mahdi

et al. 2024

Acta Neurochir

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Purpose This bibliometric analysis of the top 100 cited articles on extended reality (XR) in neurosurgery aimed to reveal trends in this research field. Gender differences in authorship and global distribution of the most-cited articles were also addressed. Methods A Web of Science electronic database search was conducted. The top 100 most-cited articles related to the scope of this review were retrieved and analyzed for trends in publications, journal characteristics, authorship, global distribution, study design, and focus areas. After a brief description of the top 100 publications, a comparative analysis between spinal and cranial publications was performed. Results From 2005, there was a significant increase in spinal neurosurgery publications with a focus on pedicle screw placement. Most articles were original research studies, with an emphasis on augmented reality (AR). In cranial neurosurgery, there was no notable increase in publications. There was an increase in studies assessing both AR and virtual reality (VR) research, with a notable emphasis on VR compared to AR. Education, surgical skills assessment, and surgical planning were more common themes in cranial studies compared to spinal studies. Female authorship was notably low in both groups, with no significant increase over time. The USA and Canada contributed most of the publications in the research field. Conclusions Research regarding the use of XR in neurosurgery increased significantly from 2005. Cranial research focused on VR and resident education while spinal research focused on AR and neuronavigation. Female authorship was underrepresented. North America provides most of the high-impact research in this area.

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Artificial Intelligence in Neurosurgery: A Bibliometric Analysis

Cited by 21 publications

References 29 publications

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

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

Limitations in Evaluating Machine Learning Models for Imbalanced Binary Outcome Classification in Spine Surgery: A Systematic Review

Extended reality in cranial and spinal neurosurgery – a bibliometric analysis

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