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

Table 5 GRADE certainty of evidence assessment across 8 kinematic artificial intelligence domains

Domain	n	Risk of bias	Inconsistency	Indirectness	Imprecision	Pub. bias	GRADE
Withdrawal speed	10	Not serious1	Not serious	Not serious	Not serious	Unlikely	Moderate
Coverage mapping	6	Not serious1	Not serious	Not serious	Serious2	Unlikely	Moderate
Workflow recognition	7	Serious3	Not serious	Serious4	Serious	Undetected	Low
Skill assessment	5	Serious	Serious5	Very serious6	Very serious	Undetected	Very low
Navigation (pre-clinical)	8	Serious	Not serious	Very serious7	Very serious	Undetected	Very low
SLAM/3D (pre-clinical)	13	Serious8	Not serious	Very serious7	Very serious	Suspected9	Very low
Robotic control (pre-clinical)	5	Serious	Serious	Very serious7	Very serious	Undetected	Very low
Instrument tracking (pre-clinical)	4	Serious	Not serious	Very serious7	Very serious	Undetected	Very low

¹Not serious despite inherent unblinding, which is structurally unavoidable in real-time artificial intelligence trials and does not selectively affect kinematic studies.

²No colonoscopy coverage randomized controlled trial; small total sample.

³Only 1 randomized controlled trial component; predominantly retrospective.

⁴Phase-recognition accuracy is a surrogate, not a patient outcome.

⁵Only 2 ML-based studies with different methods.

⁶No study uses DL–kinematic sensor integration for flexible endoscopy.

⁷All studies in simulation, phantom, or animal models only.

⁸37% lacked external validation.

⁹Positive-result publication bias suspected given absence of negative/null technical studies.

SLAM: Simultaneous localization and mapping; 3D: Three-dimensional.