Percutaneous coronary intervention outcomes rely heavily on accurate lesion assessment and procedural planning. Invasive tools, such as fractional flow reserve, nonhyperemic pressure ratios, intravascular ultrasound, and optical coherence tomography, provide essential physiological and anatomic insights but are resource-intensive, prolong procedures, and increase contrast and radiation exposure. Coronary computed tomography (CT) angiography has emerged as a noninvasive modality with high diagnostic accuracy for coronary artery disease, capable of detailing plaque composition, lesion length, and vessel geometry. With the integration of CT-derived fractional flow reserve and CT myocardial perfusion imaging, coronary CT angiography now offers both anatomic and functional evaluation, bridging diagnostic and interventional decision-making. Despite guideline endorsement for coronary artery disease diagnosis, its role in guiding percutaneous coronary intervention strategies remains underutilized and absent from revascularization recommendations. This review outlines a practical, step-by-step framework for integrating coronary CT angiography into contemporary percutaneous coronary intervention planning, covering acquisition protocols, software platforms, lesion assessment, and stent strategy optimization. It also explores emerging intraprocedural applications, including fusion imaging, augmented and virtual reality, and holographic visualization. By synthesizing current evidence and identifying gaps, this review positions coronary CT angiography as a promising adjunct in precision-based percutaneous coronary intervention.

Background: Myocardial scar and fibrosis predict adverse cardiac outcomes. Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) is the reference standard for detection. However, it requires gadolinium-based contrast agents (GBCAs), which may be unsuitable for some patients. Cine balanced steady-state free precession (bSSFP) sequences are universally acquired in routine CMR. They may enable contrast-free scar detection via radiomics analysis. Aim: To systematically review the diagnostic accuracy of cine CMR radiomics for myocardial scar or fibrosis detection. The reference standard is visual or threshold-based LGE. Methods: This review followed PRISMA guidelines and was registered in PROSPERO (CRD420251121699). We searched MEDLINE, Embase, and Cochrane Library up to 8 August 2025. Eligible studies compared cine CMR radiomics with LGE-based assessment in patients with suspected or known scar/fibrosis. Quality was assessed using QUADAS-2 and Radiomics Quality Score (RQS). Results: Five retrospective studies (n = 1484) were included. Two focused on myocardial infarction, two on hypertrophic cardiomyopathy, and one on ischaemic versus dilated cardiomyopathy. Diagnostic performance was good to excellent (AUC 0.74–0.96). Methodological heterogeneity was substantial in reference standards, segmentation, preprocessing, feature selection, and modelling. Only one study used external validation. QUADAS-2 showed high bias risk in patient selection and index test domains. RQS scores were low (30–42%), indicating limited reproducibility and validation. Conclusions: Cine CMR radiomics shows promise as a non-contrast alternative for detecting myocardial scar and fibrosis. However, methodological standardisation, multicentre validation, and prospective studies are needed before clinical adoption.

Background/Objectives: Twiddler’s syndrome is an uncommon but clinically important complication of implantable cardiac devices, in which generator rotation within the pocket results in lead torsion, lead retraction, and device malfunction. Recurrence can necessitate repeated surgical intervention and may be preventable through early risk identification and procedural strategies. Methods: We describe a single-centre case series of three female patients with pacemaker-associated Twiddler’s syndrome. Clinical presentation, timing of lead retraction, management strategies (including pocket location and fixation approach), recurrence, and follow-up outcomes were reviewed. Results: All patients were older women and developed symptomatic device failure early after implantation, with radiographic confirmation of lead retraction and coiling occurring within three weeks in all cases. Recurrence was observed when enhanced preventive measures were not employed. Notably, in one patient, recurrence occurred after an initial revision in a second prepectoral pocket, prompting subsequent reimplantation in a subpectoral location with reinforced fixation and structured patient and family counselling, after which no further recurrence occurred at one year. In the remaining cases, revision with reinforced generator fixation and counselling was associated with stable lead position and satisfactory device function during follow-up. Conclusions: Twiddler’s syndrome most commonly presents in the first weeks following implantation. Proactive identification of at-risk patients and consideration of reinforced fixation and pocket strategies at the index procedure may reduce recurrence and avoid repeat interventions.

Background: Accounting for approximately 50% of heart failure, heart failure with preserved ejection fraction (HFpEF) is becoming increasingly common as populations age and multimorbidity grows. Diagnosis remains challenging, requiring multimodal testing with echocardiography, biomarkers and sometimes invasive haemodynamics. Artificial intelligence applied to the electrocardiogram (AI‐ECG) offers a low‐cost, scalable means of detecting HFpEF by extracting patterns beyond human interpretation. Methods: We conducted a systematic review and meta‐analysis in accordance with PRISMA, registered prospectively with PROSPERO. PubMed, Embase, Web of Science, and IEEE Xplore were searched to 1 August 2025 for studies evaluating AI/ML models applied to ECGs for the diagnosis of HFpEF or left ventricular diastolic dysfunction (LVDD). Eligible studies reported diagnostic performance compared with a recognized reference standard. Risk of bias was assessed with QUADAS‐AI. AUROC values were pooled using a logit transformation and random‐effects model, with results back‐transformed for interpretability. Results: Ten studies (2021–2025) met inclusion criteria, encompassing > 270,000 participants across diverse populations. Seven studies provided sufficient data for pooling, contributing 11 independent cohorts. The pooled AUROC was 0.84 (95% CI 0.78–0.88), indicating good discriminatory ability, though heterogeneity was extreme (I ² = 100%). Three additional studies reported diagnostic metrics without AUROC variance and were synthesized narratively. Risk of bias was moderate to high in several domains, driven by selective cohorts, inconsistent reference standards, and incomplete reporting. Conclusions: AI‐ECG shows promise for the detection of HFpEF, but the current evidence base is predominantly retrospective, methodologically heterogeneous, and limited by variable reference standards and insufficient external validation. No prospective, outcome‐based studies have yet established its clinical utility, and real‐world implementation remains untested.