AI‐Based Automated Instrument Tracking During Endoscopic Skull Base Surgery: A Pilot Feasibility Study
- 1Department of Otolaryngology‐Head and Neck Surgery Stanford University School of Medicine Stanford California USA
- 2Surgical Data Science Collaborative (SDSC) Washington District of Columbia USA
- 3Department of Neurosurgery Stanford University School of Medicine Stanford California USA
Abstract
Background Endoscopic endonasal approaches (EEA) to skull base tumors are a technically challenging procedure for both surgeons and trainees. Artificial intelligence and computer vision (CV) may offer an objective analysis that provides surgical assessment on technique and enhanced educational feedback for surgeons of all levels. Methods Patients undergoing EEA from a tertiary care center between January 2022 and January 2023 were retrospectively analyzed. A large, publicly available CV model fine‐tuned on endoscopic surgical video in collaboration with Surgical Data Science Collective (SDSC) was used for frame‐by‐frame detection of 14 different surgical instruments. Tool usage was depicted across different surgical phases of EEA. Dice‐Sørensen Similarity Coefficients (DICE) were used to identify similarities between tool motion patterns across patients. Results Ten patients (5 male, 5 female, ages 52.1 ± 13.8 years) were included, with over 35 h of video footage analyzed encompassing over 1.9 million individual frames. The sellar phase accounted for the majority of the analyzed frames. Within the sellar phase, the most frequently identified tools included suction (661,785), drill (138,031), Rhoton dissector (116,823), and cotton patties (105,038). Overall, DICE with higher frequency use exhibited substantially increased DICE scores: 0.52 for drill, 0.63 for Rhoton dissector, 0.67 for cotton patties, and 0.74 for suction. Conclusion CV models reliably identified surgical instruments and can be used to capture both high‐frequency patterns and deviations from common patterns in instrument usage across cases of endoscopic surgery.
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BibTeX
@article{grimm2026aibased,
title = {{AI‐Based Automated Instrument Tracking During Endoscopic Skull Base Surgery: A Pilot Feasibility Study}},
author = {David R. Grimm and Dhiraj Pangal and Danyal Z. Khan and Margaux Masson‐Forsythe and Kaan Duman and Ayesha Syeda and Jack Cook and Peter H. Hwang and Jayakar Nayak and Zara M. Patel and Noel Ayoub and Robert Dodd and Juan Fernandez‐Miranda and Daniel Donoho and Michael T. Chang},
journal = {World Journal of Otorhinolaryngology-Head and Neck Surgery},
year = {2026},
month = oct,
doi = {10.1002/wjo2.70162},
url = {https://doi.org/10.1002/wjo2.70162},
note = {CC BY},
}
RIS
TY - JOUR
TI - AI‐Based Automated Instrument Tracking During Endoscopic Skull Base Surgery: A Pilot Feasibility Study
AU - David R. Grimm
AU - Dhiraj Pangal
AU - Danyal Z. Khan
AU - Margaux Masson‐Forsythe
AU - Kaan Duman
AU - Ayesha Syeda
AU - Jack Cook
AU - Peter H. Hwang
AU - Jayakar Nayak
AU - Zara M. Patel
AU - Noel Ayoub
AU - Robert Dodd
AU - Juan Fernandez‐Miranda
AU - Daniel Donoho
AU - Michael T. Chang
PY - 2026
DA - 2026/10/23
JO - World Journal of Otorhinolaryngology-Head and Neck Surgery
DO - 10.1002/wjo2.70162
UR - https://doi.org/10.1002/wjo2.70162
AB - Background Endoscopic endonasal approaches (EEA) to skull base tumors are a technically challenging procedure for both surgeons and trainees. Artificial intelligence and computer vision (CV) may offer an objective analysis that provides surgical assessment on technique and enhanced educational feedback for surgeons of all levels. Methods Patients undergoing EEA from a tertiary care center between January 2022 and January 2023 were retrospectively analyzed. A large, publicly available CV model fine‐tuned on endoscopic surgical video in collaboration with Surgical Data Science Collective (SDSC) was used for frame‐by‐frame detection of 14 different surgical instruments. Tool usage was depicted across different surgical phases of EEA. Dice‐Sørensen Similarity Coefficients (DICE) were used to identify similarities between tool motion patterns across patients. Results Ten patients (5 male, 5 female, ages 52.1 ± 13.8 years) were included, with over 35 h of video footage analyzed encompassing over 1.9 million individual frames. The sellar phase accounted for the majority of the analyzed frames. Within the sellar phase, the most frequently identified tools included suction (661,785), drill (138,031), Rhoton dissector (116,823), and cotton patties (105,038). Overall, DICE with higher frequency use exhibited substantially increased DICE scores: 0.52 for drill, 0.63 for Rhoton dissector, 0.67 for cotton patties, and 0.74 for suction. Conclusion CV models reliably identified surgical instruments and can be used to capture both high‐frequency patterns and deviations from common patterns in instrument usage across cases of endoscopic surgery.
SN - 2589-1081
SN - 2095-8811
ER -