ReviewAdvances in experimental medicine and biology2024
Machine Intelligence in Cerebrovascular and Endovascular Neurosurgery.
Review in Advances in experimental medicine and biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- THINKERS-X: multisequence MRI radiomics in a mixture-of-experts model for outcome prediction and exploratory dose-scenario evaluation in lung cancer brain metastases treated with radiosurgery.Clinical & experimental metastasis · 2026Article
- From severity scoring to predictive analytics: the emerging role of AI in neurosurgery.Frontiers in neurologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The advent of different realms of computational neurosurgery-including not only machine intelligence but also visualization techniques such as mixed reality and robotic applications-is beginning to impact both open vascular as well as endovascular neurosurgery. Especially in this relatively common patient population of often very fragile patients, with potential for devastating complications and clinical outcomes and sometimes highly complex pathologies, computer assistance could prove particularly useful. In this chapter, state-of-the-art applications of machine learning toward vascular patients are elucidated: Beginning from simple clinical diagnostic, prognostic, and predictive modeling, to the interpretation of medical imaging (radiomics, segmentation, and diagnostic assistance) and synthetic imaging (image modality conversion, super-resolution, and 2D-to-3D-synthesis), up to intraoperative applications of computer vision (robotic steering, rapid intraoperative histopathology, and anatomical and surgical phase recognition), and natural language processing (enabling model training and big data, documentation, and large language models)-this chapter provides a "tour de force" of machine intelligence in the realm of neurovascular medicine.
Indexed as
Identifiers
39523278What OpenQuestion holds
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.