Artificial intelligence for kinematic (procedural motion) analysis in gastrointestinal endoscopy: A systematic review

Table 1 Characteristics of 58 included studies

Number	Ref.	Domain	Study design	Sample size	AI method	TML	Key finding
1	Gong et al[1], 2020	Withdrawal speed monitoring	RCT	704 patients	CNN (ENDOANGEL)	A	ENDOANGEL ADR 16.3% vs 7.7% control (intention-to-treat)
2	Su et al[2], 2020	Withdrawal speed monitoring	RCT	659 patients	CNN (real-time quality control)	A	ADR 28.9% vs 16.5%; polyp detection rate 383% vs 25.4%
3	Yao et al[9], 2022	Withdrawal speed monitoring	RCT	1076 patients	CNN (ENDOANGEL CAQ)	A	COMBO ADR 30.6% vs CADe-only 21.3% vs CAQ-only 24.5% vs control 148%
4	Liu et al[27], 2025	Withdrawal speed monitoring	RCT	1254 patients	CNN (ENDOANGEL)	A	6-center ADR improved 22.6% to 32.7% in moderate/Low detectors
5	Barua et al[28], 2023	Withdrawal speed monitoring	Prospective	332 patients	CNN (speedometer)	B	No benefit at high-ADR centers (45.8% vs 45.2%); ceiling effect
6	Lu et al[29], 2023	Withdrawal speed monitoring	Retrospective	1780 patients	CNN (ENDOANGEL)	C	AI-assisted arm (CADe + CAQ + combined) eliminated time-of-day quality decline (13.7%-5.7% unassisted; stable 22%-23% with AI)
7	Liu et al[55], 2022	Withdrawal speed monitoring	Prospective	103 colonoscopies	CNN	B	AI-based fold examination quality correlated with ADR (r = 0.852)
8	Lux et al[56], 2023	Withdrawal speed monitoring	Multicenter	100 colonoscopy videos	DL (documentation)	C	AI prototype for automated withdrawal time measurement and photo-documentation (5 centers)
9	Lui et al[57], 2024	Withdrawal speed monitoring	Retrospective	350 videos	DL (real-time)	C	AI real-time monitoring of effective withdrawal time
10	Li et al[58], 2024	Withdrawal speed monitoring	Retrospective	472 videos	YOLOv5	C	Novel withdrawal time indicator based on YOLOv5
11	Wu et al[30], 2019	Coverage/blind spot mapping	RCT	324 patients	CNN (WISENSE)	A	EGD blind spots 5.9% vs 22.5% control
12	Wu et al[31], 2021	Coverage/blind spot mapping	RCT	1050 patients	CNN (ENDOANGEL)	A	5-hospital blind spots 5.38 vs 9.82
13	Chen et al[60], 2020	Coverage/blind spot mapping	RCT	437 patients	DNN (ENDOANGEL)	A	AI reduced blind spots; sedated 3.4% vs 22.4% in EGD
14	Freedman et al[32], 2020	Coverage/blind spot mapping	Algorithm dev	Synthetic + real videos	CNN (depth-based)	E	Coverage quantification; algorithm 0.075 vs expert 0.177 MAE; 93% agreement
15	Wu et al[59], 2019	Coverage/blind spot mapping	Algorithm dev	3170 gastric cancer + 5981 benign images	DNN (ENDOANGEL)	E	EGC detection 92.5% accuracy, 94.0% sensitivity
16	Li et al[61], 2021	Coverage/blind spot mapping	Algorithm dev	170297 images + 5779 videos	DL (IDEA)	D	Real-time 31-site gastric anatomical recognition; 95.3% video accuracy in EGD
17	Cao et al[33], 2023	Workflow recognition	Retro + animal	201026 labeled frames	CNN (AI-Endo)	D	83.5% real-time ESD phase recognition across centers
18	Furube et al[34], 2024	Workflow recognition	Retrospective	94 videos	CNN	C	Esophageal ESD phase recognition 90% accuracy
19	Liu et al[35], 2025	Workflow recognition	Multicenter	195 videos	CNN	C	International 7-center esophageal ESD workflow recognition
20	Chen et al[36], 2025	Workflow recognition	Dataset	66656 frames	Transformer	F	Renji ESD benchmark dataset
21	Biffi et al[37], 2025	Workflow recognition	Dataset	2.7M frames	TCN	F	REAL-colon temporal segmentation benchmark
22	Ward et al[62], 2021	Workflow recognition	Retrospective	50 videos	CNN (LSTM)	D	Automated POEM phase identification
23	Zhang et al[78], 2026	Workflow recognition	Algorithm dev	385 videos	Mamba (SPRMamba)	E	State-space model for ESD phase recognition
24	Nerup et al[38], 2015	Skill assessment	Prospective	10 experienced + 11 trainees in colonoscopy	ML (kinematic)	D	MEI-based kinematic scoring discriminated expert vs trainee
25	Vilmann et al[39], 2020	Skill assessment	Prospective	24 endoscopists	ML (simulation)	D	Computerized assessment validated in simulation
26	Yao et al[40], 2024	Skill assessment	RCT	685 patients	CNN (ENDOANGEL)	A	AI novice miss rate 188% vs control novice 437% vs expert 27.0%
27	Wittbrodt et al[63], 2024	Skill assessment	Retrospective	50 colonoscopies	ML (random forest)	D	ML colonoscopy skill; withdrawal time r = 0.99
281	Cold et al[79], 2024	Skill assessment	Prior systematic review (cross-reference)	13 studies	Various	-	Systematic review of computer-aided colonoscopy competence assessment
29	Martin et al[41], 2020	Autonomous navigation	Animal study	2 pigs; 10 novices	CNN	E	Completion rates 58% direct vs 96% intelligent teleop vs 100% semi-autonomous
30	Hwang et al[42], 2026	Autonomous navigation	Simulation	50 experiments	Supervised DL	F	Autonomous robotic colonoscopy; 90% success rate
31	Corsi et al[43], 2023	Autonomous navigation	Simulation	Simulation	Constrained RL	F	Safe colonoscopy navigation with formal verification
32	Prendergast et al[64], 2018	Autonomous navigation	Phantom	Phantom	ML (localization)	F	Autonomous haustral fold detection for robotic endoscopy
33	Huang et al[65], 2021	Autonomous navigation	Simulation	Simulation	ML (magnetic)	F	Autonomous navigation of magnetic colonoscope
34	Lazo et al[66], 2022	Autonomous navigation	Simulation	Simulation	DL visual servoing	F	Autonomous soft-robot intraluminal navigation
35	Pore et al[67], 2022	Autonomous navigation	Simulation	Simulation	End-to-end RL	F	Deep visuomotor control for colonoscopy
36	Tan et al[68], 2025	Autonomous navigation	Simulation	Simulation	Human-intervention RL	F	Safe navigation via human-intervention-based RL
37	Ma et al[44], 2021	SLAM/3D reconstruction	Algorithm dev	Real + phantom sequences	RNN-SLAM	E	38%-46% drift reduction in 3D colon reconstruction
38	Ozyoruk et al[45], 2021	SLAM/3D reconstruction	Dataset + algo	42700 frames	Self-supervised	E	EndoSLAM dataset; unsupervised depth estimation
39	Bonilla et al[46], 2024	SLAM/3D reconstruction	Algorithm dev	Endoscopy videos	3DGS	E	Gaussian Pancakes for endoscopic reconstruction
40	Wang et al[47], 2024	SLAM/3D reconstruction	Algorithm dev	Endoscopy videos	3DGS + SLAM	E	EndoGSLAM: Real-time dense reconstruction (> 100 fps)
41	Recasens et al[69], 2021	SLAM/3D reconstruction	Algorithm dev	Endoscopy videos	DL (depth + motion)	E	Endo-Depth-and-Motion tracking
42	Shao et al[70], 2022	SLAM/3D reconstruction	Algorithm dev	Endoscopy videos	Self-supervised	E	Self-supervised monocular depth in endoscopy
43	Hayoz et al[71], 2023	SLAM/3D reconstruction	Algorithm dev	Endoscopy videos	DL (pose est)	E	Robust camera pose estimation in endoscopy
44	Azagra et al[72], 2023	SLAM/3D reconstruction	Dataset	96 procedures	SLAM baseline	F	Endomapper dataset of calibrated procedures
45	Bobrow et al[73], 2023	SLAM/3D reconstruction	Dataset	10015 frames	NeRF (2D-3D reg)	E	Colonoscopy 3D dataset with paired depth
46	Shi et al[74], 2023	SLAM/3D reconstruction	Algorithm dev	Colonoscopy videos	NeRF (ColonNeRF)	E	High-fidelity long-sequence colonoscopy reconstruction
47	Elvira et al[75], 2024	SLAM/3D reconstruction	Algorithm dev	Full procedures	CudaSIFT-SLAM	E	Multiple-map SLAM for full procedure mapping
48	Guo et al[80], 2025	SLAM/3D reconstruction	Algorithm dev	Endoscopy videos	NeRF (UC-NeRF)	E	Uncertainty-aware NeRF from sparse endoscopic views
49	Kaleta et al[81], 2025	SLAM/3D reconstruction	Algorithm dev	Endoscopy videos	3DGS (PR-ENDO)	E	Physically based relightable Gaussian Splatting
50	Trovato et al[48], 2010	Robotic control/instrument tracking	Simulation	Simulation	Q-learning	F	First RL-based colon-endoscope-robot locomotion
51	Turan et al[49], 2019	Robotic control/instrument tracking	Simulation	Simulation	Deep RL	F	Learning to navigate endoscopic capsule robots
52	İncetan et al[50], 2021	Robotic control/instrument tracking	Simulation	Simulation	Deep RL (VR-caps)	F	Virtual environment for capsule endoscopy
53	Jha et al[51], 2020	Robotic control/instrument tracking	Dataset	590 frames	DL (segmentation)	F	Kvasir-Instrument: GIE tool-segmentation dataset
54	Ali et al[52], 2021	Robotic control/instrument tracking	Challenge	2531 detection + 643 segmentation frames	Transformer/CNN	F	DL for artefact and disease detection and segmentation
55	Brand et al[53], 2022	Robotic control/instrument tracking	Multicenter retro	580 videos	DL	D	DL model to improve polyp-detection usability
56	Jha et al[54], 2025	Robotic control/instrument tracking	Challenge	Multiple datasets	Various DL	F	Polyp and instrument segmentation benchmark
57	Zhang et al[76], 2022	Robotic control/instrument tracking	Simulation	Simulation	Deep RL	F	RL-based stomach coverage scanning of WCE
58	Ng et al[77], 2024	Robotic control/instrument tracking	Simulation	Simulation	Deep RL	F	RL navigation of tendon-driven flexible robotic endoscope

¹Cold et al[79] (2024) is a prior systematic review retained for cross-reference in the skill-assessment domain; it is not counted within the design-level synthesis (8 randomized controlled trials + 4 prospective + 10 retrospective + 35 algorithm/simulation/phantom/dataset studies = 57 primary studies).

Note on heterogeneous sample size units: Because the included studies span clinical and engineering literatures, the sample size column reports the unit native to each study type, and these units are not directly comparable as indicators of translational maturity. For interpretation: (1) ‘Patients’ denotes prospective clinical enrolment in randomized controlled trials or prospective cohorts [translational maturity level (TML) A and B, direct clinical evidence]; (2) ‘Videos’, ‘colonoscopies’, or ‘cases’ denote clinical material analysed retrospectively offline (TML C and D, clinical-but-non-prospective data); (3) ‘Images’, ‘frames’, or annotated-dataset counts denote algorithm-development studies with no patient-level outcomes (TML D and E, engineering-stage evidence); and (4) ‘Phantom’, ‘simulation’, or ‘animal’ entries denote pre-clinical evaluation furthest from clinical translation (TML F). Readers should therefore weigh entries by reading the sample size column together with the TML column. A large frame or image count does not imply greater clinical maturity than a smaller patient cohort, and conversely a small patient enrolment may carry greater translational weight than a multi-million-frame dataset. TML: Translational maturity level. A: Randomized controlled trial with patient outcomes; B: Prospective non-randomized clinical study; C: Retrospective clinical study; D: Algorithm development with external validation; E: Algorithm development with internal validation only; F: Simulation/phantom/dataset study; EGD: Esophagogastroduodenoscopy; ESD: Endoscopic submucosal dissection; ADR: Adenoma detection rate; CNN: Convolutional neural network; DL: Deep learning; ML: Machine learning; RL: Reinforcement learning; NeRF: Neural radiance fields; 3DGS: Three-dimensional gaussian splatting; SLAM: Simultaneous localization and mapping; POEM: Peroral endoscopic myotomy; WCE: Wireless capsule endoscopy; MEI: Magnetic endoscopic imaging; RCT: Randomized controlled trial; AI: Artificial intelligence; CAQ: Computer-aided quality; CADe: Computer-aided detection; IDEA: Intelligent detection endoscopic assistant; TCN: Temporal convolutional network; LSTM: Long short-term memory; RNN: Recurrent neural network; MAE: Mean absolute error; GIE: Gastrointestinal endoscopy; 3D: Three-dimensional.