The Role of Artificial Intelligence in Diagnostic Neurosurgery: A Systematic Review DOI
William Li, Armand Gumera, Shiv Surya

et al.

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: April 4, 2025

Abstract Background: Artificial intelligence (AI) is increasingly applied in diagnostic neurosurgery, enhancing precision and decision-making neuro-oncology, vascular, functional, spinal subspecialties. Despite its potential, variability outcomes necessitates a systematic review of performance applicability. Methods: A comprehensive search PubMed, Cochrane Library, Embase, CNKI, ClinicalTrials.gov was conducted from January 2020 to 2025. Inclusion criteria comprised studies utilizing AI for reporting quantitative metrics. Studies were excluded if they focused on non-human subjects, lacked clear metrics, or did not directly relate applications neurosurgery. Risk bias assessed using the PROBAST tool. This study registered PROSPERO, number CRD42025631040 26th, Results: Within 186 studies, neural networks (29%) hybrid models (49%) dominated. categorised into neuro-oncology (52.69%), vascular neurosurgery (19.89%), functional (16.67%), (11.83%). Median accuracies exceeded 85% most categories, with achieving high accuracy tumour detection, grading, segmentation. Vascular excelled stroke intracranial haemorrhage median AUC values 97%. Functional showed promising results, though sensitivity specificity underscores need standardised datasets validation. Discussion: The review’s limitations include lack data weighting, absence meta-analysis, limited collection timeframe, quality, risk some studies. Conclusion: AI shows potential improving across neurosurgical domains. Models used stroke, ICH, aneurysm conditions such as Parkinson’s disease epilepsy demonstrate results. However, sensitivity, specificity, further research model refinement ensure clinical viability effectiveness.

Language: Английский

Impact of Spatial Evolution of Cropland Pattern on Cropland Suitability in Black Soil Region of Northeast China, 1990–2020 DOI Creative Commons

Long Kang,

Kening Wu

Agronomy, Journal Year: 2025, Volume and Issue: 15(1), P. 172 - 172

Published: Jan. 12, 2025

Agricultural land resources are essential for food production, and thus it is vital to examine the spatiotemporal changes in these their impacts on suitability optimize resource allocation. In this study, we investigated spatial evolution of cropland through use change analysis by utilizing four periods data from 1990 2020 black soil region northeast China (BSRNC). Employing niche theory, developed a cultivability evaluation model tailored BSRNC, which was used assess impact patterns over past 30 years suitability. Our key findings as follows: (1) Cropland have generally tended expand with an increase 7.16 × 103 km2 cultivated area northeastward shift center 52.94 km, indicating significant configuration land. (2) The region’s cultivable were substantial, covering 694.06 km2, or 55.78% total area, notable variability, influenced regional climate topography. (3) has slightly improved, shown 0.10 index, but declining trend observed after 2000. provide valuable insights help accurately productivity BSRNC facilitate sustainable conservation soil.

Language: Английский

Citations

1
The Role of Artificial Intelligence in Diagnostic Neurosurgery: A Systematic Review DOI
William Li, Armand Gumera, Shiv Surya

et al.

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: April 4, 2025

Abstract Background: Artificial intelligence (AI) is increasingly applied in diagnostic neurosurgery, enhancing precision and decision-making neuro-oncology, vascular, functional, spinal subspecialties. Despite its potential, variability outcomes necessitates a systematic review of performance applicability. Methods: A comprehensive search PubMed, Cochrane Library, Embase, CNKI, ClinicalTrials.gov was conducted from January 2020 to 2025. Inclusion criteria comprised studies utilizing AI for reporting quantitative metrics. Studies were excluded if they focused on non-human subjects, lacked clear metrics, or did not directly relate applications neurosurgery. Risk bias assessed using the PROBAST tool. This study registered PROSPERO, number CRD42025631040 26th, Results: Within 186 studies, neural networks (29%) hybrid models (49%) dominated. categorised into neuro-oncology (52.69%), vascular neurosurgery (19.89%), functional (16.67%), (11.83%). Median accuracies exceeded 85% most categories, with achieving high accuracy tumour detection, grading, segmentation. Vascular excelled stroke intracranial haemorrhage median AUC values 97%. Functional showed promising results, though sensitivity specificity underscores need standardised datasets validation. Discussion: The review’s limitations include lack data weighting, absence meta-analysis, limited collection timeframe, quality, risk some studies. Conclusion: AI shows potential improving across neurosurgical domains. Models used stroke, ICH, aneurysm conditions such as Parkinson’s disease epilepsy demonstrate results. However, sensitivity, specificity, further research model refinement ensure clinical viability effectiveness.

Language: Английский

Citations

0