ArticleFrontiers in surgery2022
Machine Learning-Based Surgical Planning for Neurosurgery: Artificial Intelligent Approaches to the Cranium.
Article in Frontiers in surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 3 of them syntheses that pooled it.
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Who cites it
30 citing papers in PubMed, 3 syntheses or guidelines pooled it, 61 citations in OpenAlex.
- Ethical Concerns of AI in Neurosurgery: A Systematic Review.Brain and behavior · 2025Pooled it
- Revolutionizing spinal interventions: a systematic review of artificial intelligence technology applications in contemporary surgery.BMC surgery · 2024Pooled it
- Automatic Path-Planning Techniques for Minimally Invasive Stereotactic Neurosurgical Procedures-A Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled it
- AI-powered semantic segmentation model for enhanced ureteral mapping and real-time instrument feedback in robotic colorectal surgery.Journal of robotic surgery · 2026Article
- Obstacles and Trials in Treating Primary Brain Tumors.International journal of molecular sciences · 2026Review
- ChatNSG: An Overview of Contemporary and Emerging Artificial Intelligence Models for the Neurosurgeon.Journal of neurological surgery. Part B, Skull base · 2026Article
- Deep learning approach for critical exposure during division of the inferior mesenteric artery in colorectal surgery.Journal of robotic surgery · 2026Article
- Artificial intelligence in brain tumor diagnosis and surgical planning: Recent advances.Surgical neurology international · 2026Review
- Artificial Intelligence in Ambulatory Surgery: Current Applications, Challenges, and Future Directions.Journal of medical systems · 2025Review
- Artificial Intelligence in the Diagnosis and Treatment of Brain Gliomas.Biomedicines · 2025Review
- Opportunities for Artificial Intelligence in Oncology: From the Lens of Clinicians and Patients.JCO oncology practice · 2025Review
- Machine learning decision support model construction for craniotomy approach of pineal region tumors based on MRI images.BMC medical imaging · 2025Article
- The Transformative Role of Artificial Intelligence in Plastic and Reconstructive Surgery: Challenges and Opportunities.Journal of clinical medicine · 2025Review
- Comparison of surgical approaches to the hippocampal formation with artificial intelligence.Neurosurgical review · 2025Article
- Artificial Intelligence for Patient Safety and Surgical Education in Neurosurgery.JMA journal · 2025Review
- Radiomic Features as Artificial Intelligence Prognostic Models in Glioblastoma: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2024Review
- Leveraging State-of-the-Art AI Algorithms in Personalized Oncology: From Transcriptomics to Treatment.Diagnostics (Basel, Switzerland) · 2024Article
- Personalizing neurosurgical strategies for epidermoid cysts with artificial intelligence.Neurosurgical review · 2024Article
- Artificial Intelligence-Enhanced Neurocritical Care for Traumatic Brain Injury : Past, Present and Future.Journal of Korean Neurosurgical Society · 2024Article
- Artificial Intelligence and Deep Learning in Revolutionizing Brain Tumor Diagnosis and Treatment: A Narrative Review.Cureus · 2024Review
Corrections and comments
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Authors and funding
13 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: Artificial intelligence (AI) applications in neurosurgery have an increasing momentum as well as the growing number of implementations in the medical literature. In recent years, AI research define a link between neuroscience and AI. It is a connection between knowing and understanding the brain and how to simulate the brain. The machine learning algorithms, as a subset of AI, are able to learn with experiences, perform big data analysis, and fulfill human-like tasks. Intracranial surgical approaches that have been defined, disciplined, and developed in the last century have become more effective with technological developments. We aimed to define individual-safe, intracranial approaches by introducing functional anatomical structures and pathological areas to artificial intelligence. Methods: Preoperative MR images of patients with deeply located brain tumors were used for planning. Intracranial arteries, veins, and neural tracts are listed and numbered. Voxel values of these selected regions in cranial MR sequences were extracted and labeled. Tumor tissue was segmented as the target. Q-learning algorithm which is a model-free reinforcement learning algorithm was run on labeled voxel values (on optimal paths extracted from the new heuristic-based path planning algorithm), then the algorithm was assigned to list the cortico-tumoral pathways that aim to remove the maximum tumor tissue and in the meantime that functional anatomical tissues will be least affected. Results: The most suitable cranial entry areas were found with the artificial intelligence algorithm. Cortico-tumoral pathways were revealed using Q-learning from these optimal points. Conclusions: AI will make a significant contribution to the positive outcomes as its use in both preoperative surgical planning and intraoperative technique equipment assisted neurosurgery, its use increased.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.