Work

SurgflowNet: Leveraging unannotated video for consistent endoscopic pituitary surgery workflow recognition

Year: 2025

Type: article

Abstract: -score and 13.4% in Edit Score over the SOTA, SurgflowNetdemonstrates a significant improvement in workflow recognition for endoscopic pituitary surgery.

Source: Artificial Intelligence in Medicine

DOI: 10.1016/j.artmed.2025.103309

Authors: Anjana Wijekoon, Adrito Das, Zhehua Mao, Danyal Z. Khan, John G. Hanrahan, Danail Stoyanov, Hani J. Marcus, Sophia Bano

Institutions: University College London, National Hospital for Neurology and Neurosurgery

Language: English

Topic: Surgical Simulation and Training

Subfield: Surgery

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $3,480

Funders: Wellcome Trust, National Institute for Health and Care Research, Wellcome / EPSRC Centre for Interventional and Surgical Sciences, Manchester Biomedical Research Centre, Engineering and Physical Sciences Research Council

Awards: UKRI Centre for Doctoral Training in AI-enabled healthcare systems, Wellcome Trust Centre for Surgical and Interventional Sciences, Context Aware Augmented Reality for Endonasal Endoscopic Surgery, AID-PitSurg: AI-enabled Decision support in Pituitary Surgery to reduce complications, G8482718267, G868016207

OpenAlex ID: W4416702125

FWCI:

Cites: 19

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