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

Table 4 Risk of bias assessment (Quality Assessment of Diagnostic Accuracy Studies-2 for diagnostic accuracy studies)

Ref.	Patient selection	Index test	Reference standard	Flow and timing	Overall
Barua et al[28], 2023	Low (consecutive patients in implementation trial)	Low (AI speedometer threshold pre-defined)	Low (withdrawal time by independent timer)	Low (all patients received same assessment)	Low (all domains low risk)
Lu et al[29], 2023	Unclear (single-center convenience sample; time-of-day subgroups)	Low (ENDOANGEL AI output pre-specified)	Low (histopathology for ADR; blinded pathologist)	Low (all patients received colonoscopy and pathology)	Unclear (patient selection concern)
Liu et al[55], 2022	Unclear (single-center; convenience sampling)	Low (AI withdrawal assessment pre-defined)	Low (expert endoscopist consensus)	Low (all patients assessed by both AI and experts)	Unclear (patient selection concern)
Lui et al[57], 2024	Unclear (retrospective video selection; single center)	Low (AI withdrawal monitoring pre-specified)	Low (manual review by experienced endoscopist)	Low (all videos analyzed by both methods)	Unclear (patient selection concern)
Li et al[58], 2024	Unclear (retrospective convenience sample)	Low (YOLOv5 threshold pre-specified)	Low (manual withdrawal time measurement)	Low (all videos assessed)	Unclear (patient selection concern)
Li et al[61], 2021	Unclear (single-center convenience sample)	Low (IDEA system output pre-defined)	Low (expert annotation of anatomical landmarks)	Low (all patients received same EGD protocol)	Unclear (patient selection concern)
Cao et al[33], 2023	Unclear (retrospective video collection; single center + animal)	Low (AI-endo phase output pre-specified)	Low (expert surgeon frame-level annotation)	Low (all videos fully annotated)	Unclear (patient selection concern)
Furube et al[34], 2024	Unclear (retrospective single-center video selection)	Low (AI phase recognition threshold pre-defined)	Low (expert endoscopist annotation)	Low (all videos received complete annotation)	Unclear (patient selection concern)
Liu et al[35], 2025	Low (multicenter; prospective + retrospective cohorts)	Low (AI workflow recognition pre-specified)	Low (expert panel annotation consensus)	Low (all cases assessed by AI and experts)	Low (all domains low risk)
Ward et al[62], 2021	Unclear (retrospective single-center video selection)	Low (AI POEM phase output pre-defined)	Low (expert surgeon phase annotation)	Low (all videos fully annotated)	Unclear (patient selection concern)
Nerup et al[38], 2015	High (small convenience sample; 12 endoscopists only)	Unclear (MEI kinematic thresholds derived from same cohort)	Low (expert/trainee classification pre-defined)	Low (all endoscopists assessed)	High (small sample + index test concern)
Vilmann et al[39], 2020	High (small convenience sample; 27 endoscopists in simulation)	Unclear (computerized metrics derived from training data overlap)	Low (GRS expert assessment as reference)	Low (all participants completed assessment)	High (small sample + index test concern)

AI: Artificial intelligence; ADR: Adenoma detection rate; POEM: Peroral endoscopic myotomy; MEI: Magnetic endoscopic imaging; IDEA: Intelligent detection endoscopic assistant; EGD: Esophagogastroduodenoscopy; GRS: Global Rating Scale.