Computer vision in laparoscopic gastrointestinal surgery: A review of anatomical recognition and clinical navigation

Table 1 Existing applications of computer vision in laparoscopic gastrointestinal surgery

CV task	Target	AI approach	Dataset and sample characteristic	Study design and validation	Key performance	Clinical relevance	Translational maturity
Surgical phase recognition[17]	Laparoscopic gastric cancer	VTR-Net	Single-center dataset; 100 surgical videos from 100 patients	Retrospective study (80-case training/20-case test split); internal validation	Frame-level phase recognition: Accuracy, 90.1%; F1 score, 87.0%; precision, 87.31%	Workflow recognition and automated documentation; clinical benefit not yet established	T1
Vessel segmentation[26]	IMA in colorectal surgery	DeepLab v3+	Multicenter LapSig300; 1200 annotated images from 60 surgical videos	Retrospective study; case-level 5-fold cross-validation; internal validation	Image-level: DSC, 0.798 ± 0.016; inference speed, > 12 FPS	Real-time vascular visualization and localization	T1
Ureter segmentation[32]	Ureter recognition in laparoscopic surgery	UreterNet	14069 annotated images from 304 laparoscopic colorectal surgery videos	Internal static-split validation	Image-level: Precision, 0.712; DSC, 0.716; inference latency, 143 ms with display	Real-time ureter visualization; reduction in ureteral injury not yet demonstrated	T1
Biliary structure segmentation[36]	Laparoscopic cholecystectomy	U-Net++, UNETR, and DeepLabV3+	CholecSeg8k public dataset; 8080 annotated frames from 17 laparoscopic cholecystectomy	Procedure-level data split; internal validation	Frame-level: Dice, 0.62; mean IoU, 0.55; recall, 0.72; precision, 0.73	Anatomical segmentation; clinical impact on bile duct injury remains uncertain	T1
3D scene reconstruction[54]	Abdominal soft tissue	DROID-SLAM	Experimental animal videos; 6 laparoscopic videos, divided into 18 image sequences	Experimental technical validation against optical motion-capture ground truth under static and dynamic lighting conditions	Sequence-level: Mostly sub-centimeter trajectory error in successfully tracked sequences; dynamic-lighting ATE RMSE, 0.0076-0.0167 m	Technical basis for 3D reconstruction, camera tracking, and future AR registration and navigation	T1
AR-assisted registration[56]	Liver portal segments in laparoscopic anatomical liver resection	Detectron2 + 3D slicer + vuforia-based AR	Clinical laparoscopic liver surgery videos; 380 annotated frames; 5 AR evaluation scenes	Technical feasibility study (evaluation on 5 surgical scenes); technical validation	Scene-level: Mean error, 14.5 mm vs 31.2 mm without AI	Assist laparoscopic anatomical liver resection	T1
Functional tissue assessment[59]	Tissue ischemia	MSI+INN-based out-of-distribution detection	Single-center clinical dataset; 10 patients undergoing partial nephrectomy	In-human feasibility study (5 retrospective/5 prospective patients); ICG-referenced validation	Patient-level: Mean AU-ROC, 1.00/0.9; real-time inference, 140 Hz per ROI	Potential contrast-free ischemia assessment	T3
Real-time anatomical instrument segmentation[67]	Organs and instruments in laparoscopic gastrectomy	YOLOv10	Multicenter clinical dataset; 18 surgical videos (6832 annotated frames) from 4 hospitals; 15 patients for intraoperative deployment	Multicenter study; internal validation and external validation	Frame-level: External mAP@50, 0.832; mask mAP@50, 0.836; real-time inference, 31.8 ± 10.7 ms/frame	Real-time intraoperative navigation and usability	T3

CV: Computer vision; DSC: Dice similarity coefficient; ATE: Absolute trajectory error; RMSE: Root mean square error; AI: Artificial intelligence; AR: Augmented reality; AU-ROC: Area under the receiver operating characteristic curve; ROI: Region of interest; IMA: Inferior mesenteric artery; ICG: Indocyanine green; 3D: Three dimensional.