ArticleNPJ digital medicine2024
Artificial intelligence assisted operative anatomy recognition in endoscopic pituitary surgery.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07568366 (The Application of Artificial Intelligence to Patients Undergoing Endoscopic Transsphenoidal Surgery), which is not on this map. Cited by 19 papers.
What it found
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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.
The Application of Artificial Intelligence to Patients Undergoing Endoscopic Transsphenoidal Surgery: a Single-site Prospective Feasibility and Exploratory Study (IDEAL Stage 1 and 2a)
Who cites it
19 citing papers in PubMed.
- First-in-human evaluation of real-time pixel-level AI-assisted anatomical segmentation in neurosurgery: pituitary surgery as an exemplar.Pituitary · 2026Article
- Accuracy of ChatGPT-4o in Identifying Anatomical Structures on Cadaveric Images: A Practical Anatomy Examination Study.Clinical anatomy (New York, N.Y.) · 2026Article
- Artificial intelligence in pituitary surgery: the path to clinical solutions.Endocrine-related cancer · 2026Review
- Colour-Guided Surgery: A Conceptual Framework for Multimodal Intraoperative Visualisation-A Narrative Review.The international journal of medical robotics + computer assisted surgery : MRCAS · 2026Review
- Artificial intelligence-based anatomical recognition improves surgeon decision-making during robotic gastrectomy.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2026Article
- The role of stem cells in pituitary tumour formation.Endocrine-related cancer · 2026Review
- Educational Impact of Artificial Intelligence-Navigation Surgery on Anatomical Landmark Recognition in Medical Students.Annals of gastroenterological surgery · 2026Article
- Deep learning-based real-time intraoperative detection of thoracic duct.Journal of thoracic disease · 2026Article
- Artificial intelligence for surgical management of benign esophageal disease: scoping review and evidence mapping.Langenbeck's archives of surgery · 2026Article
- Automated Operative Phase and Step Recognition in Vestibular Schwannoma Surgery: Development and Preclinical Evaluation of a Deep Learning Neural Network (IDEAL Stage 0).Neurosurgery · 2026Article
- Artificial intelligence and robotic surgery in emergency gastrointestinal procedures: a review of current evidence and future directions.Journal of robotic surgery · 2026Review
- Frontiers of Innovation and Clinical Application in Endoscopic Endonasal Transsphenoidal Surgery.Journal of clinical medicine · 2026Review
- Article
- Real-time multicenter semantic segmentation for laparoscopic gastrectomy: development, external validation and operating room deployment of an AI navigation system.Military Medical Research · 2026Article
- Deep learning-driven multi-hierarchical granularity integration for surgical scene understanding: experimental study.International journal of surgery (London, England) · 2026Article
- A surgical approach to building impactful artificial intelligence.NPJ digital medicine · 2025Review
- Robotic-Assisted Vascular Surgery: Current Landscape, Challenges, and Future Directions.Journal of clinical medicine · 2025Review
- Deep learning HRNet FCN for blood vessel identification in laparoscopic pancreatic surgery.NPJ digital medicine · 2025Article
- The clinical application of artificial intelligence in cancer precision treatment.Journal of translational medicine · 2025Review
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
Pituitary tumours are surrounded by critical neurovascular structures and identification of these intra-operatively can be challenging. We have previously developed an AI model capable of sellar anatomy segmentation. This study aims to apply this model, and explore the impact of AI-assistance on clinician anatomy recognition. Participants were tasked with labelling the sella on six images, initially without assistance, then augmented by AI. Mean DICE scores and the proportion of annotations encompassing the centroid of the sella were calculated. Six medical students, six junior trainees, six intermediate trainees and six experts were recruited. There was an overall improvement in sella recognition from a DICE of score 70.7% without AI assistance to 77.5% with AI assistance (+6.7; p < 0.001). Medical students used and benefitted from AI assistance the most, improving from a DICE score of 66.2% to 78.9% (+12.8; p = 0.02). This technology has the potential to augment surgical education and eventually be used as an intra-operative decision support tool.
Identifiers
What 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.