ABSTRACT

Background and Objectives We report the first intraoperative deployment of a real-time machine vision system in neurosurgery, derived from our previous anatomical detection work, automatically identifying structures during endoscopic endonasal surgery. Existing systems demonstrate promising performance in offline anatomical recognition, yet so far none have been implemented during live operations.

Methods A real-time anatomy detection model was trained using the YOLOv8 architecture (Ultralytics). Following training completion in the PyTorch environment, the model was exported to ONNX format and further optimized using the NVIDIA TensorRT engine. Deployment was carried out using the NVIDIA Holoscan SDK, the system ran on an NVIDIA Clara AGX developer kit. We used the model for real-time recognition of intraoperative anatomical structures and compared it with the same video labelled manually as reference. Model performance was reported using the average precision at an intersection-over-union threshold of 0.5 (AP50). Furthermore, end-to-end delay from frame acquisition to the display of the annotated output was measured.

Results A mean AP50 of 0.56 was achieved. The model demonstrated reliable detection of the most relevant landmarks in the transsphenoidal corridor. The mean end-to-end latency of the model was 47.81 ms (median 46.57 ms).

Conclusion For the first time, we demonstrate that clinical-grade, real-time machine-vision assistance during neurosurgery is feasible and can provide continuous, automated anatomical guidance from the surgical field. This approach may enhance intraoperative orientation, reduce cognitive load, and offer a powerful tool for surgical training. These findings represent an initial step toward integrating real-time AI support into routine neurosurgical workflows.

Competing Interest Statement

Some authors are employees of NVIDIA. The company provided hardware support used in this study. No other personal or financial conflicts of interest are declared. NVIDIA had no role in the design, execution, analysis, or reporting of the study.

Funding Statement

This research is supported by SNF Project IZKSZ3_218786 research grant (Swiss National Funds)

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Ethikkommission of Zurich, Switzerland gave ethical approval for this work (KEK 2023-02265)

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Footnotes

Funding: This research was funded by the SNSF (Project IZKSZ3 218786). Some hardware for the project was provided by NVIDIA.

Disclosures: Some authors are employees of NVIDIA. The company provided hardware support used in this study. No other personal or financial conflicts of interest are declared. NVIDIA had no role in the design, execution, analysis, or reporting of the study.

Data Availability

The data used is not publicly available.

AbbreviationsSupMSuperior Nasal MeatusMidMMiddle Nasal MeatusInfMInferior Nasal MeatusRecSphEthmSphenoethmoidal RecessAP50average precision at an intersection-over-union threshold of 0.5.