Diagnostic utility of artificial intelligence for left ventricular scar identification using cardiac magnetic resonance imaging—A systematic review

BACKGROUND: Accurate, rapid quantification of ventricular scar using cardiac magnetic resonance imaging (CMR) carries importance in arrhythmia management and patient prognosis. Artificial intelligence (AI) has been applied to other radiological challenges with success. OBJECTIVE: We aimed to assess AI methodologies used for left ventricular scar identification in CMR, imaging sequences used for training, and its diagnostic evaluation. METHODS: Following PRISMA recommendations, a systematic search of PubMed, Embase, Web of Science, CINAHL, OpenDissertations, arXiv, and IEEE Xplore was undertaken to June 2021 for full-text publications assessing left ventricular scar identification algorithms. No pre-registration was undertaken. Random-effect meta-analysis was performed to assess Dice Coefficient (DSC) overlap of learning vs predefined thresholding methods. RESULTS: Thirty-five articles were included for final review. Supervised and unsupervised learning models had similar DSC compared to predefined threshold models (0.616 vs 0.633, P = .14) but had higher sensitivity, specificity, and accuracy. Meta-analysis of 4 studies revealed standardized mean difference of 1.11; 95% confidence interval -0.16 to 2.38, P = .09, I(2) = 98% favoring learning methods. CONCLUSION: Feasibility of applying AI to the task of scar detection in CMR has been demonstrated, but model evaluation remains heterogenous. Progression toward clinical application requires detailed, transparent, standardized model comparison and increased model generalizability

Novel techniques for mapping and ablating ventricular Tachycardia

ICD therapies reduce the mortality from VT but result in significant morbidity. VT ablation can prevent these therapies but recurrence rates remain high. I hypothesised that the identification of sources of VT needs alternative mapping approaches with more effective ablation delivery to improve procedure outcomes. Ablation of triggered VT is limited by lack of spontaneous tachycardia peri-procedurally and diagnostic challenges of mapping foci originating from adjacent chambers. I confirmed the feasibility of use of a non-invasive electrocardiographic mapping system at identifying the source of PVCs prior to invasive testing and its superiority in guiding ablation of outflow tract VT compared to conventional algorithms. Ablation of scar-related VT requires mapping during tolerated VT or substrate modification by extensive scar ablation to transect potential VT sustaining channels. Current mapping systems are unable to display the low-amplitude fractionated signals within scar channels. We (PK, NL, MKW, SJC) developed a novel mapping algorithm [Ripple Mapping (RM)] which simultaneously displays electrogram voltage and activation as dynamic bars moving according to their time-voltage relationship relative to a fiduciary reference. Clinical accuracy of RM was validated in atrial tachycardia cases (SJC). Application in VT ablation cases identified slow conduction channels which co-located to VT sustaining sites (SJC). The contribution of scar geometry to the VT substrate is unknown. I investigated this by developing a custom program (with WB) capable of creating a 3-D computational ventricular scar model from LGE-CMR. The scar border thickness gradient was significantly different between patients with and without spontaneous VT. The timing of VT ablation and transmurality of ablation lesions play a role in ablation outcomes. We tested the feasibility, safety and outcome of robotic (SJC) vs manual (VL) ablation in patients with ischaemic VT and whether outcomes of robotic-guided VT ablation would be superior to conservative management in patients presenting with their first device therapy. This thesis describes the development and testing of novel methods for mapping and ablating VT with the potential to reduce recurrence rates from VT. These are now being tested in prospective randomised studies to assess whether long term outcomes can also be improved