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Background: Increased afterload in aortic stenosis (AS) induces left ventricle (LV) remodeling to preserve a normal ejection fraction. This compensatory response can become maladaptive and manifest with motion abnormality. It is a clinical challenge to identify contractile and relaxation dysfunction during early subclinical stage to prevent irreversible deterioration. Purpose: To evaluate the changes of regional wall dynamics in 3D + time domain as remodeling progresses in AS. Study Type: Retrospective. Population: A total of 31 AS patients with reduced and preserved ejection fraction (14 AS_rEF: 7 male, 66.5 [7.8] years old; 17 AS_pEF: 12 male, 67.0 [6.0] years old) and 15 healthy (6 male, 61.0 [7.0] years old). Field Strength/Sequence: 1.5 T Magnetic resonance imaging/steady state free precession and late‐gadolinium enhancement sequences. Assessment: Individual LV models were reconstructed in 3D + time domain and motion metrics including wall thickening (TI), dyssynchrony index (DI), contraction rate (CR), and relaxation rate (RR) were automatically extracted and associated with the presence of scarring and remodeling. Statistical Tests: Shapiro–Wilk: data normality; Kruskal–Wallis: significant difference (P < 0.05); ICC and CV: variability; Mann–Whitney: effect size. Results: AS_rEF group shows distinct deterioration of cardiac motions compared to AS_pEF and healthy groups (TIAS_rEF: 0.92 [0.85] mm, TIAS_pEF: 5.13 [1.99] mm, TIhealthy: 3.61 [1.09] mm, ES: 0.48–0.83; DIAS_rEF: 17.11 [7.89]%, DIAS_pEF: 6.39 [4.04]%, DIhealthy: 5.71 [1.87]%, ES: 0.32–0.85; CRAS_rEF: 8.69 [6.11] mm/second, CRAS_pEF: 16.48 [6.70] mm/second, CRhealthy: 10.82 [4.57] mm/second, ES: 0.29–0.60; RRAS_rEF: 8.45 [4.84] mm/second; RRAS_pEF: 13.49 [8.56] mm/second, RRhealthy: 9.31 [2.48] mm/second, ES: 0.14–0.43). The difference in the motion metrics between healthy and AS_pEF groups were insignificant (P‐value = 0.16–0.72). AS_rEF group was dominated by eccentric hypertrophy (47.1%) with concomitant scarring. Conversely, AS_pEF group was dominated by concentric remodeling and hypertrophy (71.4%), which could demonstrate hyperkinesia with slight wall dyssynchrony than healthy. Dysfunction of LV mechanics corresponded to the presence of myocardial scarring (54.9% in AS), which reverted the compensatory mechanisms initiated and performed by LV remodeling. Data Conclusion: The proposed 3D + time modeling technique may distinguish regional motion abnormalities between AS_pEF, AS_rEF, and healthy cohorts, aiding clinical diagnosis and monitoring of AS progression. Subclinical myocardial dysfunction is evident in early AS despite of normal EF. Level of Evidence: 4 Technical Efficacy: Stage 1 [ABSTRACT FROM AUTHOR]

Introduction: The article is a succinct summary of events and process for emergency digitisation and transition to remote teaching during the COVID-19 pandemic. The challenges of such transition included the need for enhanced infrastructure facilities, compliance to directives from regulatory bodies, providing an equivalent learning experience in the virtual learning environment (VLE) and ensuring the end user capacity to utilise the VLE created. Methods: To accomplish this task a suitable instructional design and transition model was utilised to create an integrated Moodle and Microsoft Teams platform as the VLE. The curriculum was recreated in the VLE through review of existing infrastructure and resources, deconstructing the demands of the curriculum, reconstructing the learning experiences of curriculum in VLE and innovating to improve. The end user training was also provided using the same VLE created, which ensured capacity building. Virtual Clinical Assessments (VCA) were created to ensure the completion of assessment tasks. Results: The utilisation of the ACTIONS transition model resulted in the evolution of instructional delivery from a Web Enhanced approach to a customised Web Centric approach and implementation of Virtual Clinical Assessments. Students expressed their satisfaction in the learning experience through VLE, but were anxious about their clinical training and connectivity issues. Conclusion: This transition demonstrated the need of future directions in terms of learner readiness to be more self-directed and self-determined, design thinking for transformation to a Web Centric curriculum, faculty readiness to change and develop the competency of Technological Pedagogical Content Knowledge (TPACK). [ABSTRACT FROM AUTHOR]

Purpose: To evaluate a 2D‐4D registration‐cum‐segmentation framework for the delineation of left ventricle (LV) in late gadolinium enhanced (LGE) MRI and for the localization of infarcts in patient‐specific 3D LV models. Methods: A 3‐step framework was proposed, consisting of: (1) 3D LV model reconstruction from motion‐corrected 4D cine‐MRI; (2) Registration of 2D LGE‐MRI with 4D cine‐MRI; (3) LV contour extraction from the intersection of LGE slices with the LV model. The framework was evaluated against cardiac MRI data from 27 patients scanned within 6 months after acute myocardial infarction. We compared the use of local Pearson's correlation (LPC) and normalized mutual information (NMI) as similarity measures for the registration. The use of 2 and 6 long‐axis (LA) cine‐MRI scans was also compared. The accuracy of the framework was evaluated using manual segmentation, and the interobserver variability of the scar volume derived from the segmented LV was determined using Bland‐Altman analysis. Results: LPC outperformed NMI as a similarity measure for the proposed framework using 6 LA scans, with Hausdorrf distance (HD) of 1.19 ± 0.53 mm versus 1.51 ± 2.01 mm (endocardial) and 1.21 ± 0.48 mm versus 1.46 ± 1.78 mm (epicardial), respectively. Segmentation using 2 LA scans was comparable to 6 LA scans with a HD of 1.23 ± 0.70 mm (endocardial) and 1.25 ± 0.74 mm (epicardial). The framework yielded a lower interobserver variability in scar volumes compared with manual segmentation. Conclusion: The framework showed high accuracy and robustness in delineating LV in LGE‐MRI and allowed for bidirectional mapping of information between LGE‐ and cine‐MRI scans, crucial in personalized model studies for treatment planning. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to assess First Year medical students' readiness for OSCE. Design: This is a retrospective study where secondary data comprising both quantitative and qualitative data, were analysed. Materials and Methods: Three cohorts of First Year medical students (n = 454) took a 5-station OSCE. Two categories of tasks were assessed. Category A assessed patient and doctor interaction while Category B assessed clinical skills. A student must be scored as satisfactory in at least four out of five stations for a pass in Category A and at least three out of five stations for a pass in Category B. A pass in both Categories A and B is required to pass the OSCE. For each cohort, overall passing percentage, as well as passing percentage for Category A and Category B of each station, was computed. Examiners' feedback on students' performance during OSCE for each station was examined. Feedback from students regarding the OSCE was also sought. Results: For Cohort 2013, Cohort 2014 and Cohort 2015, 174/179 (97.21%), 118/129 (91.47%) and 140/147 (95.24%) of students passed the OSCE respectively. Cohort 2013, Cohort 2014 and Cohort 2015 recorded mean percent pass of (95.31%, 88.83%), (89.15%, 83.10%) and (98.36%, 84.52%) for Category A and Category B respectively. Examiners' feedback was generally favourable. Feedback from students was mixed but constructive and generally encouraging. Conclusions: Based on students' performance in the OSCE as well as feedback from both examiners and students, First Year medical students appeared to be ready for OSCE assessment. [ABSTRACT FROM AUTHOR]