Image-based detection of the internal carotid arteries and sella turcica in endoscopic endonasal transsphenoidal surgery.

OBJECTIVE

Endoscopic endonasal transsphenoidal surgery (EETS) is a minimally invasive procedure that accesses the sellar and parasellar regions. Various anatomical structures must be identified during the operation, particularly the sella turcica and internal carotid artery (ICA) bilaterally. In the present retrospective cohort study, authors aimed to evaluate the performance of a deep learning (DL) model in detecting the sella turcica and ICA bilaterally in EETS video footage, with the goal of recognizing crucial landmarks and preventing potentially fatal injury.

METHODS

The authors collected images from the endoscopic video footage of 98 patients who had undergone EETS from January 2015 to June 2024. The ICAs and sella turcica were labeled by neurosurgeons, and the entire dataset was divided into training, validation, and test datasets at a ratio of 7:2:1. The model for ICA and sella turcica detection was trained using the YOLOv5s object detection architecture, and precision, recall, mean average precision (mAP)@0.5, and mAP@0.5:0.95 were reported during the validation process. Moreover, the confusion matrix and area under the receiver operating characteristic curve (AUC) were assessed from the model using unseen images from the test dataset.

RESULTS

The DL model had precision, recall, mAP@0.5, and mAP@0.5:0.95 of 0.942, 0.955, 0.969, and 0.617, respectively, for all objects in the training processes with validation. For testing the model with unseen images, the AUC was 0.97 (95% CI 0.95-0.98), whereas average precision was 0.99 (95% CI 0.99-1.00). For ICA detection with a multiclass approach, the AUCs were 0.98 (95% CI 0.97-0.99) for the absence of any ICA, 0.93 (95% CI 0.91-0.95) for 1 ICA in the images, and 0.95 (95% CI 0.93-0.96) for both ICAs in the image. Additionally, accuracy for the ICA and sella turcica was 0.958 and 0.965, respectively.

CONCLUSIONS

Complex anatomical landmarks should be recognized during EETS. The computer vision model was effective in detecting the sella turcica and ICA bilaterally, as well as in identifying and avoiding fatal complications. For the model to generalize with reliability, it requires novel, unseen data from various settings to refine it and facilitate transfer learning.