Exploring the Landscape of Machine Learning Applications in Neurosurgery: A Bibliometric Analysis and Narrative Review of Trends and Future Directions

Machine learning applications in craniosynostosis diagnosis and treatment prediction: a systematic review

Craniosynostosis refers to the premature fusion of one or more of the fibrous cranial sutures connecting the bones of the skull. Machine learning (ML) is an emerging technology and its application to craniosynostosis detection and management is underexplored. This systematic review aims to evaluate the application of ML techniques in the diagnosis, severity assessment, and predictive modeling of craniosynostosis. A comprehensive search was conducted on the PubMed and Google Scholar databases using predefined keywords related to craniosynostosis and ML. Inclusion criteria encompassed peer-reviewed studies in English that investigated ML algorithms in craniosynostosis diagnosis, severity assessment, or treatment outcome prediction. Three independent reviewers screened the search results, performed full-text assessments, and extracted data from selected studies using a standardized form. Thirteen studies met the inclusion criteria and were included in the review. Of the thirteen papers examined on the application of ML to the identification and treatment of craniosynostosis, two papers were dedicated to sagittal craniosynostosis, five papers utilized several different types of craniosynostosis in the training and testing of their ML models, and six papers were dedicated to metopic craniosynostosis. ML models demonstrated high accuracy in identifying different types of craniosynostosis and objectively quantifying severity using innovative metrics such as metopic severity score and cranial morphology deviation. The findings highlight the significant strides made in utilizing ML techniques for craniosynostosis diagnosis, severity assessment, and predictive modeling. Predictive modeling of treatment outcomes following surgical interventions showed promising results, aiding in personalized treatment strategies. Despite methodological diversities among studies, the collective evidence underscores ML's transformative potential in revolutionizing craniosynostosis management.

A bibliometric analysis and visualization of normal pressure hydrocephalus

Background

Normal pressure hydrocephalus (NPH) has drawn an increasing amount of attention over the last 20 years. At present, there is a shortage of intuitive analysis on the trends in development, key contributors, and research hotspots topics in the NPH field. This study aims to analyze the evolution of NPH research, evaluate publications both qualitatively and quantitatively, and summarize the current research hotspots.

Methods

A bibliometric analysis was conducted on data retrieved from the Web of Science Core Collection (WoSCC) database between 2003 and 2023. Quantitative assessments were conducted using bibliometric analysis tools such as VOSviewer and CiteSpace software.

Results

A total of 2,248 articles published between 2003 and 2023 were retrieved. During this period, the number of publications steadily increased. The United States was the largest contributor. The University of Gothenburg led among institutions conducting relevant research. Eide P. K. was the most prolific author. The Journal of Neurosurgery is the leading journal on NPH. According to the analysis of the co-occurrence of keywords and co-cited references, the primary research directions identified were pathophysiology, precise diagnosis, and individualized treatment. Recent research hotspots have mainly focused on epidemiology, the glymphatic system, and CSF biomarkers.

Conclusion

The comprehensive bibliometric analysis of NPH offers insights into the main research directions, highlights key countries, contributors, and journals, and identifies significant research hotspots. This information serves as a valuable reference for scholars to further study NPH.

A critical moment in machine learning in medicine: on reproducible and interpretable learning

Over the past two decades, advances in computational power and data availability combined with increased accessibility to pre-trained models have led to an exponential rise in machine learning (ML) publications. While ML may have the potential to transform healthcare, this sharp increase in ML research output without focus on methodological rigor and standard reporting guidelines has fueled a reproducibility crisis. In addition, the rapidly growing complexity of these models compromises their interpretability, which currently impedes their successful and widespread clinical adoption. In medicine, where failure of such models may have severe implications for patients' health, the high requirements for accuracy, robustness, and interpretability confront ML researchers with a unique set of challenges. In this review, we discuss the semantics of reproducibility and interpretability, as well as related issues and challenges, and outline possible solutions to counteracting the "black box". To foster reproducibility, standard reporting guidelines need to be further developed and data or code sharing encouraged. Editors and reviewers may equally play a critical role by establishing high methodological standards and thus preventing the dissemination of low-quality ML publications. To foster interpretable learning, the use of simpler models more suitable for medical data can inform the clinician how results are generated based on input data. Model-agnostic explanation tools, sensitivity analysis, and hidden layer representations constitute further promising approaches to increase interpretability. Balancing model performance and interpretability are important to ensure clinical applicability. We have now reached a critical moment for ML in medicine, where addressing these issues and implementing appropriate solutions will be vital for the future evolution of the field.