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Electrocardiographic imaging (ECGI) provides high-density body surface potential (BSP) measurements but remains largely descriptive. Computational modelling offers a mechanistic framework to interpret BSP signals in terms of underlying tissue-level properties.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Methods and findings<\/jats:title>\n                    <jats:p>\n                      We developed a BSP-driven workflow to construct patient-specific electrophysiology (EP) models of HCM by integrating multimodal clinical imaging with Bayesian model calibration. Anatomically detailed torso-heart finite-element models were generated for 17 HCM patients using thoracic computed tomography (CT), cardiac magnetic resonance imaging (CMR), and 252-electrode BSP recordings. Ventricular depolarisation and repolarisation were simulated using a reaction-eikonal (RE) formulation coupled to a biophysically detailed ToR-ORd-dynCl ionic model. Emulator-based Bayesian history matching (HM) was used to personalise EP parameters, with staged calibration of QRS and T-wave morphology informed by targeted sensitivity analysis. The calibrated cohort reproduced clinical BSP morphology with Pearson correlation coefficient (PCC)\n                      <jats:inline-formula id=\"pcbi.1014555.e001\">\n                        <jats:alternatives>\n                          <jats:graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" id=\"pcbi.1014555.e001g\" mimetype=\"image\" position=\"anchor\" xlink:href=\"info:doi\/10.1371\/journal.pcbi.1014555.e001\" xlink:type=\"simple\"\/>\n                          <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"inline\" id=\"M1\">\n                            <mml:mrow>\n                              <mml:mo>\u2265<\/mml:mo>\n                              <mml:mn>0.6<\/mml:mn>\n                            <\/mml:mrow>\n                          <\/mml:math>\n                        <\/jats:alternatives>\n                      <\/jats:inline-formula>\n                      for a median of 94.0% (IQR: 91.6 to 96.8%) of electrodes, achieving a median PCC of 0.89 (IQR: 0.80 to 0.94) across the full 252-electrode vest. Calibration substantially reduced uncertainty in the high-dimensional EP parameter space while yielding physiologically plausible conduction and repolarisation properties. Models calibrated exclusively to sinus rhythm robustly generalised to right-ventricular (RV) apical pacing without parameter retuning, reproducing clinically observed pacing-induced trends in depolarisation and repolarisation. Exploratory analysis revealed biologically consistent associations between inferred EP parameters and patient demographics.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study demonstrates that high-density BSP data can be used to functionally personalise mechanistic EP in HCM. The framework captures intrinsic patient-specific EP properties and generalises beyond the calibration condition, supporting its use for mechanistic investigation of arrhythmogenic substrate.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pcbi.1014555","type":"journal-article","created":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T17:38:27Z","timestamp":1785173907000},"page":"e1014555","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Body surface potential driven personalisation of electrophysiological digital twins in hypertrophic 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