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

Diagnostic Utility of Artificial Intelligence for Left Ventricular Scar Identification using Cardiac Magnetic Resonance Imaging – A Systematic Review

Data extracted from 35 studies included in a systematic revie

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, I2 = 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.</p

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, I2 = 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.</p

Clinical impact of “pure” empirical catheter ablation of slow-pathway in patients with non-ECG documented clinical on–off tachycardia

Abstract Background Catheter ablation of slow-pathway (CaSP) has been reported to be effective in patients with dual atrioventricular nodal conduction properties (dcp-AVN) and clinical ECG documentation but without the induction of tachycardia during electrophysiological studies (EPS). However, it is unknown whether CaSP is beneficial in the absence of pre-procedural ECG documentation and without the induction of tachycardia during EPS. The aim of this study was to evaluate long-term results after a “pure” empirical CaSP (peCaSP). Methods 334 consecutive patients who underwent CaSP (91 male, 47.5 ± 17.6 years) were included in this study. Sixty-three patients (19%) who had no pre-procedural ECG documentation, and demonstrated dcp-AVN with a maximum of one echo-beat were assigned to the peCaSP group. The remaining 271 patients (81%) were assigned to the standard CaSP group (stCaSP). Clinical outcomes of the two groups were compared, based on ECG documented recurrence or absence of tachycardia and patients’ recorded symptoms. Results CaSP was performed in all patients without any major complications including atrioventricular block. During follow-up (909 ± 435 days), 258 patients (77%) reported complete cessation of clinical symptoms. There was no statistically significant difference in the incidence of AVNRT recurrence between the peCaSP and stCaSP groups (1/63 [1.6%] vs 3/271 [1.1%], P = 0.75). Complete cessation of clinical symptoms was noted significantly less frequently in patients after peCaSP (39/63 [62%] vs 219/271 [81%], P = 0.0013). The incidence of non-AVNRT atrial tachyarrhythmias (AT) was significantly higher in patients after peCaSP (5/63 [7.9%] vs 1/271 [0.4%], P = 0.0011). Conclusion A higher incidence of other AT and subjective symptom persistence are demonstrated after peCaSP, while peCaSP improves clinical symptoms in 60% of patients with non-documented on–off tachycardia