Digital twins in gastroenterology: From computational modeling to precision medicine and clinical translation

Table 1 Traditional predictive model vs digital twin

Feature	Traditional predictive model	Gastriontestinal digital twin
Data basis	Population cohort (group-level coefficients)	Individual patient (multimodal, longitudinal)
Temporal score	Single time point; static after deployment	Continuous; updated with each new data input
Update mechism	None (model frozen post-training)	Data assimilation (e.g., ensemble Kalman filter, Bayesian methods)
Intervention testing	Not possible	In silico simulation of drug, dose, or procedure before clinical use
Model architecture	Statistical/data-driven (regression, ML)	Mechanistic, data-driven, or hybrid (physics-informed neural networks)
Personalization	Risk score adjusted by a few covariates	Full virtual replica of individual patient physiology
GI example	Biologic response score (single clinic visit)	IBD immune twin; gastric motility CFD model; microbiome MCMM
Key limitation	Cannot adapt to evolving disease; population averages mask individual variation	Data requirements high; computational infrastructure; validation gap

The bidirectional data loop (afferent/efferent) is the defining architectural feature that distinguishes a digital twin from any static predictive model. CFD: Computational fluid dynamics; MCMM: Microbial community-scale metabolic model; IBD: Inflammatory bowel disease; ML: Machine learning; GI: Gastrointestinal.

Table 2 Summary of digital twin applications across gastrointestinal regions

GI region	Representative models/tools	Key simulation parameters	Target conditions	Potential clinical applications	Current restrictions
Stomach	StomachSim; CFD-based gastric models; dynamic MRI-coupled simulations	Antral contractions. Gastric mixing. Emptying dynamics. Intraluminal pressure. Retropulsive jets. Drug dissolution kinetics. Body posture effects	Gastroparesis; functional dyspepsia	Pre-procedural planning for pyloroplasty and sleeve gastrectomy; pharmacologic efficacy testing; dietary optimization; identification of subtle flow abnormalities	Simplified motility assumptions. Small experimental datasets. Limited patient-specific validation
Small intestine	Peristaltic flow models; CFD luminal transit simulations	Luminal flow velocities. Contractile activity. Transit dynamics. Absorption modeling	Functional bowel disorders; malabsorption states	Nutrient absorption assessment; motility characterization; drug delivery simulation	Early feasibility stage. Complex motility patterns difficult to replicate. Changing luminal content variability
Colon	Digital replicas of experimental colon models; peristaltic fluid dynamics models	Peristaltic fluid movement. Mixing patterns. Flow velocities. Luminal shear stress	Constipation; diarrheal states; colonic dysmotility	Colonic motility disorder characterization; therapeutic intervention planning; microbiome-motility interaction modeling	Validated primarily against experimental models. Minimal patient-specific clinical data. Simplified boundary conditions
Esophagus	CFD bolus transit models; sphincter biomechanical simulations	Bolus transit mechanics. Sphincter function. Intraluminal pressure gradients. Peristaltic wave propagation	Achalasia; spastic motility syndromes; gastroesophageal reflux	Diagnostic support for dysmotility; pre-interventional planning for myotomy or dilation; assessment of sphincter competence	Largely conceptual/early development. Very limited clinical validation data. Complex tissue mechanics not fully modeled

Overview of current computational modeling approaches, simulated parameters, targeted conditions, potential clinical uses, and key limitations for digital twin research across major gastrointestinal organs. CFD: Computational fluid dynamics; GI: Gastrointestinal; MRI: Magnetic resonance imaging.

Table 3 Five inflammatory bowel disease modeling approaches as rows

Modeling method	Representative works	Data inputs	Key targets	Potential clinical applications	Current limitation
Cytokine signaling models	Wendelsdorf et al[67]; systems-level colonic inflammation models	Cytokine profiles. Immune cell populations. Regulatory pathway data	TNF-α, IL-6, IL-12, IL-23, regulatory T-cell networks	In silico drug target validation; identification of dominant inflammatory pathways per patient	Simplified pathway representations. Limited clinical validation
Hybrid immune-clinical model	Shim et al[66]; mechanistic + data-driven framework	Biomarker profiles. Disease activity scores. Longitudinal clinical data	Patient-specific immune parameters; clinical disease activity indices	Individualized disease activity prediction; treatment response stratification prior to biologic initiation	Small derivation cohorts. Requires longitudinal data inputs
Single-cell transcriptomic twins	Karolinska “disease mechanism” twin; scRNA-seq immune network models	scRNA-seq mucosal biopsies. Gene expression networks. Immune cell subset profiles	Regulatory molecular nodes; immune cell subset activity; anti-TNF response predictors	Patient-specific therapy selection; identification of non-responders priorto biologic initiation; precision immune phenotyping	Early development stage. High data acquisition burden. Significant computational complexity
Predictive clinical model	Longitudinal ML models for Crohn’s disease trajectory prediction	CRP, fecal calprotectin. Endoscopic findings. Medication history. Patient-reported symptoms	Disease flare probability; progression to complications; secondary loss of response	Dynamic disease monitoring; early intervention triggering; virtual clinical trial design and patient enrichment	Not full digital twins. Population-level derivation. Limited prospective validation
PK/PD digital twins	Emerging pharmacokinetic-immune signaling coupling models	Drug concentration levels. Antidrug antibody titres. Immune signaling readouts	Biologic drug concentrations; dosing interval optimization; immunogenicity prediction	Precision biologic dosing; prevention of secondary loss of response; individualized therapeutic drug monitoring	Largely conceptual. Requires prospective. PK/PD data. Not yet clinically validated

Cytokine signaling models, hybrid immune-clinical models, single-cell transcriptomic twins, predictive clinical models, and PK/PD digital twins with representative work, data inputs, targets, applications, and limitations. TNF: Tumor necrosis factor; IL: Interleukin; CRP: C-reactive protein; ML: Machine learning.