Gaze responses to false-positive computer-aided detection prompts during colonoscopy: a paired-video and real-time eye-tracking study

Background and Objective: Colonoscopy recording practice preserves text reports and still photographs, while the spatial information already present in the recorded video - where the scope traveled, where a lesion was observed, and whether the same lesion was seen again - is discarded when the procedure ends. This study determines whether a lesion-centered spatial record can be assembled and validated without full-colon 3D reconstruction. Methods: A four-layer hierarchical pipeline was assembled - (1) a global topological map, (2) lesion-level spatio-temporal tracks, (3) on-demand local 3D reconstruction, and (4) persistent lesion identity across repeated observations - and ran end to end on four public videos (two C3VDv2 sequences with ground-truth depth and two full REAL-Colon procedures; 40,245 frames). All components are published, individually validated methods; the contribution is their lesion-centered assembly, linking rules, and evaluation. Results: Revisits, impossible under forward-only mapping by construction, were detected by entry-map Bayesian localization: 5,614 and 4,043 revisit events (56 and 68 distinct nodes) in the two full procedures. Lesion-identity merging at the adopted threshold 0.5 maintained ground-truth purity 1.0 while auto-merging 20 of 231 candidate pairs. The endoscopy-specific geometry engine outperformed a general-purpose foundation model on all metrics (overall absolute relative error (AbsRel) 0.2276 vs. 0.3523). Conclusions: The results are partial but establish a concrete near-term path: revisit detection, lesion identity, and local 3D each returned quantitative, reproducible output without waiting for complete geometric reconstruction; validating the record on clinical data is the next step.

Geometric estimation including depth estimation and scene reconstruction is a crucial technique for colonoscopy which can provide surgeons with 3D spatial perception and navigation. However, geometric ground truth in colonoscopy is difficult to obtain due to narrow and enclosed space of the colon, while there is a large feature gap between simulated data and realistic data caused by artifacts and illumination. In this paper, we present CoGE, a novel framework for online monocular geometric estimation during colonoscopy. Firstly, we propose an illumination-aware supervision module based on the Retinex theory to address illumination diversity in different colonoscopy scenes. Moreover, a structure-aware perception module is proposed based on wavelet decomposition to extract common structural and local features of the colon. Both quantitative and qualitative results demonstrate that the proposed model solely trained on simulated data achieves state-of-the-art performance in geometric estimation for both simulated and realistic scenes.

Purpose: Gaze-following, the task of inferring where individuals are looking, has been widely studied in computer vision, advancing research in visual attention modeling, social scene understanding, and human-robot interaction. However, gaze-following has never been explored in the operating room (OR), a complex, high-stakes environment where visual attention plays an important role in surgical workflow analysis. In this work, we introduce the concept of gaze-following to the surgical domain, and demonstrate its great potential for understanding clinical roles, surgical phases, and team communications in the OR. Methods: We extend the 4D-OR dataset with gaze-following annotations, and extend the Team-OR dataset with gaze-following and a new team communication activity annotations. Then, we propose novel approaches to address clinical role prediction, surgical phase recognition, and team communication detection using a gaze-following model. For role and phase recognition, we propose a gaze heatmap-based approach that uses gaze predictions solely; for team communication detection, we train a spatial-temporal model in a self-supervised way that encodes gaze-based clip features, and then feed the features into a temporal activity detection model. Results: Experimental results on the 4D-OR and Team-OR datasets demonstrate that our approach achieves state-of-the-art performance on all downstream tasks. Quantitatively, our approach obtains F1 scores of 0.92 for clinical role prediction and 0.95 for surgical phase recognition. Furthermore, it significantly outperforms existing baselines in team communication detection, improving previous best performances by over 30%. Conclusion: We introduce gaze-following in the OR as a novel research direction in surgical data science, highlighting its great potential to advance surgical workflow analysis in computer-assisted interventions.