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Background and Objective: Left ventricular non-compaction (LVNC) is an uncommon cardiomyopathy characterised by a thick and spongy left ventricle wall caused by the high presence of trabeculae (hyper-trabeculation). Recently, the percentage of the trabecular volume to the total volume of the external wall of the left ventricle (VT%) has been proposed to diagnose this illness., Methods: This paper presents the use of a deep learning-based method to measure the (VT%) value and diagnose this rare cardiomyopathy. The population used in this research was composed of 277 patients suffering from hypertrophic cardiomyopathy. 134 patients only suffered hypertrophic cardiomyopathy, and 143 also suffered left ventricular non-compaction. Our deep learning solution is based on a 2D U-Net. This artificial neural network (ANN) was trained on short-axis magnetic resonance imaging to segment the left ventricle's internal cavity, external wall, and trabecular tissue. 5-fold cross-validation was performed to ensure the robustness of the results. The Dice coefficient of the three classes was computed as a measure of the precision of the segmentation. Based on this segmentation, the percentage of the trabecular volume (VT%) was computed. Two specialist cardiologists rated the segmentation produced by the neural network for 25 patients to evaluate the clinical validity of the outputs. The computed VT% was used to automatically diagnose the 277 patients depending on whether or not a given threshold was exceeded. A receiver operating characteristic analysis was also performed., Results: According to the cross-validation results, the average and standard deviation of the Dice coefficient for the internal cavity, external wall, and trabeculae were 0.96±0.00, 0.89±0.00, and 0.84±0.00, respectively. The cardiologists rated 99.5% of the evaluated segmentations as clinically valid for diagnosis, outperforming existing automatic traditional tools. The area under the ROC curve was 0.94 (95% confidence interval, 0.91-0.96). The accuracy, sensitivity, and specificity values of diagnosis using a threshold of 25% were 0.87, 0.93, and 0.80, respectively., Conclusions: The U-Net neural network can achieve excellent results in the delineation of different cardiac structures of short-axis cardiac MRI. The high-quality segmentation allows for the correct measurement of left ventricular hyper-trabeculation and a definitive diagnosis of LVNC illness. Using this kind of solution could lead to more objective and faster analysis, reducing human error and time spent by cardiologists., Competing Interests: Declaration of Competing Interest The authors declare that there is no conflict of interest., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background: In selected type 1 diabetic (T1DM) patients with end-stage renal disease (ESRD), simultaneous pancreas-kidney transplantation (SPKT) offers higher long-term graft and patient survival, but also higher initial morbidity and mortality than cadaveric kidney transplantation alone (CKTA). The development of new immunosuppressive regimens and surgical approach has improved this initial outcome, but little is known about their effect on short-term renal function and surgical complications related to the renal graft., Methods: We analysed retrospectively the short-term follow-up of 45 T1DM patients consecutively transplanted during 42 months (20 SPKT and 25 CKTA) in order to compare short-term (6 months) renal allograft function and surgical complications related to the renal allograft in both groups., Results: There were no differences in donor characteristics. SPKT recipients had a significantly shorter time on dialysis and cold ischaemia time, with a higher number of HLA mismatches. There was no difference in acute rejection incidence, but delayed kidney graft function was less frequent in SPKT (5% vs 32%; P<0.05). Plasma creatinine level at discharge and 6 months was significantly lower in SPKT (1.1+/-0.3 vs 1.6+/-0.7; P<0.005 and 1.1+/-0.3 vs 1.5+/-0.6; P<0.05, respectively). There were no differences in surgical renal complications (haemorrhage, thrombosis or arterial stenosis, ureter leaks or stricture, lymphoceles or dehiscences). Two SPKT patients needed reintervention on the renal allograft and only one CKTA patient., Conclusions: In the modern transplant era, SPKT in ESRD diabetic patients, offers a slightly better short-term kidney allograft function without significant increase in surgical morbidity, compared with CKTA.