Artificial intelligence in gastrointestinal surgery: A systematic review

Table 3 Summary of studies related to artificial intelligence-based intraoperative guidance in gastrointestinal surgery

Ref.	Surgical context	AI method/model	Guidance function	Data type/source	Real time capability	Performance metrics
Sato et al[24], 2022	Thoracoscopic esophagectomy	DeepLabv3+ (CNN based semantic segmentation)	Recurrent laryngeal nerve identification and navigation	3000 annotated intraoperative images from 20 videos (train/val) + 40 test images from 8 other videos	Yes	Dice: AI 0.58, expert 0.62, general surgeons 0.47
Niikura et al[25], 2022	Upper GI endoscopy	Single shot multibox detector (CNN)	Detection of gastric cancer in endoscopic images	23892 white light images from 500 patients (1:1 matched to expert endoscopists)	No	Per patient: 100%, per image: 99.87%, IOU: 0.842
Yang et al[26], 2022	EUS for subepithelial lesions	ResNet 50 (CNN based deep learning)	Differentiation of gastrointestinal stromal tumors and leiomyomas using EUS	10439 EUS images from 752 patients (multicenter); 132 prospective patients for clinical validation	Yes	AUC: 0.986 (internal), 0.642 (external), accuracy: 96.2%
Schnelldorfer et al[27], 2024	Staging laparoscopy	YOLOv5 (detection), ensemble ResNet18 CNN (classification)	Identification and classification of peritoneal surface metastases	4287 lesions from 132 patients (365 biopsied lesions; 3650 image patches)	No	AUC PR: 0.69, AUROC: 0.78, accuracy: 78%
Guo et al[28], 2021	GI endoscopy (multi lesion)	Deep CNN (ResNet 50 with TTA and transfer learning)	Detecting four lesion categories to support endoscopic diagnosis	327121 WLI images for training; 33959 images in validation from 1734 cases	No (0.05 seconds/image)	Sensitivity: 88.3%, specificity: 90.3%, accuracy: 89.7%
Houwen et al[29], 2022	Colonoscopy		Defines competence standards (SODA) for AI or endoscopists in optical diagnosis	Simulation studies + systematic literature review; ESGE Delphi consensus panel	Yes	Sensitivity ≥ 90%, specificity ≥ 80% (leave in situ); ≥ 80% both (resect and discard)
Tatar and Çubukçu[30], 2024	Colonoscopy	YOLOv8 based CNN (ColoNet)	Identification of neoplastic/premalignant/malignant lesions; biopsy decision support	1760 colonoscopy images (306 patients) for training/validation; 91 external images for real time testing	Yes	mAP50: 0.832, accuracy: 82.4%, sensitivity: 70.7%, specificity: 92.0%

AI: Artificial intelligence; CNN: Convolutional neural network; GI: Gastrointestinal; IOU: Intersection over union; EUS: Endoscopic ultrasonography; AUC: Area under the curve; AUC PR: Area under the precision-recall curve; AUROC: Area under the receiver operating characteristic curve; TTA: Test time augmentation; WLI: White light imaging; SODA: Simple optical diagnosis accuracy; ESGE: European Society of Gastrointestinal Endoscopy; mAP: Mean average precision.