Future perspectives in esophageal manometry

Table 1 Main features of current and novel techniques used in the evaluation of esophageal motility disorders

Technology		Features	Primary applications	Clinical impact/future role
FLIP		Measures EGJ distensibility and cross-sectional area	EGJ outflow evaluation, intraoperative guidance during POEM and fundoplication	Guides extent of myotomy or wrap; predicts treatment response[9]
Ambulatory HREM		24-hour, high-resolution pressure mapping with symptom correlation	Offers a better detection of clinically relevant abnormalities, with evaluation of posture- or meal-related symptoms	Has a higher diagnostic yield, especially in spastic or hypercontractile disorders, as well as in patients with non-cardiac chest pain, and non-obstructive dysphagia[102]
Novel catheters		Vision-enabled or solid-state catheters, improved sensor density	Easier placement, circumferential pressure measurement	Enhanced spatial resolution and patient comfort[37]
Solid swallows/test meals		Use of solid or semi-solid boluses during HREM	Detection of motility abnormalities by mimicking real-life eating conditions, improved symptom correlation	Adjunctive test important in borderline cases. Reveals EGJ outflow obstruction or type III achalasia not evident on liquid swallows[54]
AI analysis	Deep learning algorithms	Extracts hierarchical spatial and temporal features from manometric images or time-series data	Classifies motility patterns, identifies subtle or evolving abnormalities, supports temporal modeling of swallows	Enables end-to-end automated diagnosis; captures complex motility dynamics; reduces observer bias[88,89]
	Physiological modeling	Reproduces esophageal pressure dynamics and motility mechanisms	Models normal vs pathological motor activity; identifies disorder-specific mechanical profiles (e.g., achalasia, DES, EGJOO)	Provides physiological insight, aids data compression and standardization for AI training, and helps refine diagnostic thresholds[86]
	Integrative & generative AI (LLMs)	Large language or multimodal models to optimize data processing, model development, and interpretability	Feature selection, code generation, and workflow automation; potential integration of HREM, FLIP, and clinical data	Facilitates explainability, enhances model transparency, and expands AI applications to education and system design[96]

AI: Artificial intelligence; HREM: High-resolution esophageal manometry; FLIP: Functional luminal imaging probe; EGJ: Esophagogastric junction; POEM: Peroral endoscopic myotomy; LLMs: Large language models; DES: Distal esophageal spasm; EGJOO: Esophagogastric junction outflow obstruction.