The long-term effects of the fenestration in patients with extracardiac Fontan circulation-a multicenter Korean cohort study based on national Fontan registry.

Introduction: The long-term effects of fenestration in patients with Fontan circulation remain unclear. We aim to evaluate the fenestration impact on early and late outcomes in patients with extracardiac Fontan (ECF) using a propensity score matching analysis. Methods: We performed an extensive retrospective multicenter clinical data review of the Korean Fontan registry and included 1,233 patients with surgical ECF (779 fenestrated, 454 non-fenestrated). Demographics, baseline, and follow-up data were collected and comprehensively analyzed. Patients were divided into two groups according to the baseline presence or absence of surgical fenestration. Subsequently, patients were sub-divided according to the fenestration status at the last follow-up. Propensity-score matching was performed to account for collected data between the 2 groups using a multistep approach. The primary outcomes were survival and freedom from Fontan failure (FFF). We also looked at postoperative hemodynamics, cardiopulmonary exercise test results, oxygen saturations, and functional status. Results: After propensity-score matching (454 matched pairs), there was no difference in survival or FFF between the 2 groups. However, ECF patients with baseline fenestration had significantly lower oxygen saturation (p = 0.001) and lower functional status (p < 0.001). Patients with fenestration had significantly longer bypass times, higher postoperative central venous pressure, higher postoperative left atrial pressure, and less prolonged pleural effusion in the early postoperative period. The propensity score matching according to the fenestration status at the last follow-up (148 matched pairs) showed that patients with a persistent fenestration had significantly lower oxygen saturation levels (p < 0.001). However there were no intergroup differences in the functional status, survival and FFF. Conclusions: Our results showed no long-term benefits of the Fenestration in terms of survival and FFF. Patients with persistent fenestration showed oxygen desaturation but no difference in exercise intolerance was shown between the 2 groups.

Newly Developed Sex-Specific Z Score Model for Coronary Artery Diameter in a Pediatric Population.

BACKGROUND: This study aimed to generate a Z score calculation model for coronary artery diameter of normal children and adolescents to be adopted as the standard calculation method with consensus in clinical practice. METHODS: This study was a retrospective, multicenter study that collected data from multiple institutions across South Korea. Data were analyzed to determine the model that best fit the relationship between the diameter of coronary arteries and independent demographic parameters. Linear, power, logarithmic, exponential, and square root polynomial models were tested for best fit. RESULTS: Data of 2,030 subjects were collected from 16 institutions. Separate calculation models for each sex were developed because the impact of demographic variables on the diameter of coronary arteries differs according to sex. The final model was the polynomial formula with an exponential relationship between the diameter of coronary arteries and body surface area using the DuBois formula. CONCLUSION: A new coronary artery diameter Z score model was developed and is anticipated to be applicable in clinical practice. The new model will help establish a consensus-based Z score model.

The impact of inappropriate steroid exposure before the diagnosis of Kawasaki disease.

BACKGROUND: Kawasaki disease (KD) is a systemic inflammatory disease characterized by vasculitis. In South Korea, some pediatric doctors empirically prescribe steroids to control febrile pediatric patients. This study aimed to evaluate the clinical characteristics of patients with KD after steroid exposure. METHODS: This was a single-center, retrospective, observational study. This study included patients (aged ≤15 years) between January 2020 and July 2022. We compared two groups, one group exposed to steroids and the other group who were not, using the Student's t-test or analysis of variance; otherwise, the Mann-Whitney U test or Kruskal-Wallis test was conducted. Statistical significance was set at p < 0.05. RESULTS: In total, 190 patients with KD were enrolled; of these, 64 (33.7 %) had a history of steroid exposure, and 126 (66.3 %) had no history of steroid exposure. In the steroid exposure group, prolonged fever duration (6.72 ± 1.72 versus 5.61 ± 1.19, p-value = <0.001), a lower proportion of complete KD (29.69 % vs. 88.10 %, p-value = <0.001), and a significantly lower level of C-reactive protein were observed. However, no significant correlations were observed between the Transthoracic Echocardiography (TTE) results (coronary artery aneurysm, existence of pericardial effusion) and prognostic factors (days of hospitalization, the number of intravenous immunoglobulin administrations, and Kobayashi score) between the two groups. CONCLUSIONS: Patients with KD and previous steroid exposure may exhibit an incomplete KD phenotype with prolonged fever. Although previous steroid exposure does not affect the prognosis of KD, including coronary artery aneurysms, it may mask the classic features of KD, resulting in a delayed diagnosis.

Improvement in Yield of Extracellular Vesicles Derived from Edelweiss Callus Treated with LED Light and Enhancement of Skin Anti-Aging Indicators.

The process of skin aging is currently recognized as a disease, and extracellular vesicles (EVs) are being used to care for it. While various EVs are present in the market, there is a growing need for research on improving skin conditions through microbial and plant-derived EVs. Edelweiss is a medicinal plant and is currently an endangered species. Callus culture is a method used to protect rare medicinal plants, and recently, research on EVs using callus culture has been underway. In this study, the researchers used LED light to increase the productivity of Edelweiss EVs and confirmed that productivity was enhanced by LED exposure. Additionally, improvements in skin anti-aging indicators were observed. Notably, M-LED significantly elevated callus fresh and dry weight, with a DW/FW ratio of 4.11%, indicating enhanced proliferation. Furthermore, M-LED boosted secondary metabolite production, including a 20% increase in total flavonoids and phenolics. The study explores the influence of M-LED on EV production, revealing a 2.6-fold increase in concentration compared to darkness. This effect is consistent across different plant species (Centella asiatica, Panax ginseng), demonstrating the universality of the phenomenon. M-LED-treated EVs exhibit a concentration-dependent inhibition of reactive oxygen species (ROS) production, surpassing dark-cultured EVs. Extracellular melanin content analysis reveals M-LED-cultured EVs' efficacy in reducing melanin production. Additionally, the expression of key skin proteins (FLG, AQP3, COL1) is significantly higher in fibroblasts treated with M-LED-cultured EVs. These results are expected to provide valuable insights into research on improving the productivity of plant-derived EVs and enhancing skin treatment using plant-derived EVs.

Outcomes of surgery for coarctation of the aorta based on a new classification system.

OBJECTIVES: To evaluate early- and long-term outcomes of the surgical treatment for coarctation of the aorta based on a new classification system. METHODS: A retrospective clinical review of 111 patients with coarctation of the aorta who underwent surgery (March 2011 to August 2020) was performed. We categorised coarctation of the aorta into type I, with all three head vessels tightly packed; type II, with the left subclavian artery separated from the two other head vessels; and type III, with all three head vessels separated from one another. Each type included subtype a, with a short isthmic portion, and subtype b, with a long isthmic portion. RESULTS: The median patient age and weight at operation were 8 (range, 1-1490) days and 3.2 (range, 1.9-18.5) kg, respectively. Extended end-to-end anastomosis was performed via sternotomy in 54, via thoracotomy in 12, end-to-side anastomosis in 31, autologous main pulmonary artery patch augmentation in 12, and modified end-to-end anastomosis combined with subclavian artery flap aortoplasty in two patients. There was one (0.9%) case of early mortality and 12 (10.8%) cases of post-operative complications. Two (1.8%) late deaths occurred during follow-up. Five (4.5%) patients underwent balloon dilatation and three (2.7%) underwent reoperation for restenosis of coarctation of the aorta. All patients with type Ia (21 patients, 18.9%) underwent extended end-to-end anastomosis via sternotomy or thoracotomy. CONCLUSIONS: According to the early and late outcomes observed in this study, surgical treatment of coarctation of the aorta using the new classification system could be safe and low risk.

Bio-metric authentication with electrocardiogram (ECG) by considering variable signals.

The use of conventional bio-signals such as an electrocardiogram (ECG) for biometric authentication is vulnerable to a lack of verification of continuity of signals; this is because the system does not consider the change in signals caused by a change in the situation of a person, that is, conventional biological signals. Prediction technology based on tracking and analyzing new signals can overcome this shortcoming. However, since the biological signal data sets are massive, their utilization is crucial for higher accuracy. In this study, we defined a 10 × 10 matrix for 100 points based on the R-peak point and an array for the dimension of the signals. Furthermore, we defined the future predicted signals by analyzing the continuous points in each array of the matrices at the same point. As a result, the accuracy of user authentication was 91%.

Development and validation of a deep learning model to diagnose COVID-19 using time-series heart rate values before the onset of symptoms.

One of the effective ways to minimize the spread of COVID-19 infection is to diagnose it as early as possible before the onset of symptoms. In addition, if the infection can be simply diagnosed using a smartwatch, the effectiveness of preventing the spread will be greatly increased. In this study, we aimed to develop a deep learning model to diagnose COVID-19 before the onset of symptoms using heart rate (HR) data obtained from a smartwatch. In the deep learning model for the diagnosis, we proposed a transformer model that learns HR variability patterns in presymptom by tracking relationships in sequential HR data. In the cross-validation (CV) results from the COVID-19 unvaccinated patients, our proposed deep learning model exhibited high accuracy metrics: sensitivity of 84.38%, specificity of 85.25%, accuracy of 84.85%, balanced accuracy of 84.81%, and area under the receiver operating characteristics (AUROC) of 0.8778. Furthermore, we validated our model using external multiple datasets including healthy subjects, COVID-19 patients, as well as vaccinated patients. In the external healthy subject group, our model also achieved high specificity of 77.80%. In the external COVID-19 unvaccinated patient group, our model also provided similar accuracy metrics to those from the CV: balanced accuracy of 87.23% and AUROC of 0.8897. In the COVID-19 vaccinated patients, the balanced accuracy and AUROC dropped by 66.67% and 0.8072, respectively. The first finding in this study is that our proposed deep learning model can simply and accurately diagnose COVID-19 patients using HRs obtained from a smartwatch before the onset of symptoms. The second finding is that the model trained from unvaccinated patients may provide less accurate diagnosis performance compared with the vaccinated patients. The last finding is that the model trained in a certain period of time may provide degraded diagnosis performances as the virus continues to mutate.

Model for Predicting In-Hospital Mortality of Physical Trauma Patients Using Artificial Intelligence Techniques: Nationwide Population-Based Study in Korea.

BACKGROUND: Physical trauma-related mortality places a heavy burden on society. Estimating the mortality risk in physical trauma patients is crucial to enhance treatment efficiency and reduce this burden. The most popular and accurate model is the Injury Severity Score (ISS), which is based on the Abbreviated Injury Scale (AIS), an anatomical injury severity scoring system. However, the AIS requires specialists to code the injury scale by reviewing a patient's medical record; therefore, applying the model to every hospital is impossible. OBJECTIVE: We aimed to develop an artificial intelligence (AI) model to predict in-hospital mortality in physical trauma patients using the International Classification of Disease 10th Revision (ICD-10), triage scale, procedure codes, and other clinical features. METHODS: We used the Korean National Emergency Department Information System (NEDIS) data set (N=778,111) compiled from over 400 hospitals between 2016 and 2019. To predict in-hospital mortality, we used the following as input features: ICD-10, patient age, gender, intentionality, injury mechanism, and emergent symptom, Alert/Verbal/Painful/Unresponsive (AVPU) scale, Korean Triage and Acuity Scale (KTAS), and procedure codes. We proposed the ensemble of deep neural networks (EDNN) via 5-fold cross-validation and compared them with other state-of-the-art machine learning models, including traditional prediction models. We further investigated the effect of the features. RESULTS: Our proposed EDNN with all features provided the highest area under the receiver operating characteristic (AUROC) curve of 0.9507, outperforming other state-of-the-art models, including the following traditional prediction models: Adaptive Boosting (AdaBoost; AUROC of 0.9433), Extreme Gradient Boosting (XGBoost; AUROC of 0.9331), ICD-based ISS (AUROC of 0.8699 for an inclusive model and AUROC of 0.8224 for an exclusive model), and KTAS (AUROC of 0.1841). In addition, using all features yielded a higher AUROC than any other partial features, namely, EDNN with the features of ICD-10 only (AUROC of 0.8964) and EDNN with the features excluding ICD-10 (AUROC of 0.9383). CONCLUSIONS: Our proposed EDNN with all features outperforms other state-of-the-art models, including the traditional diagnostic code-based prediction model and triage scale.

Adaptive scheduling of acceleration and gyroscope for motion artifact cancelation in photoplethysmography.

BACKGROUND AND OBJECTIVE: Recently, various algorithms have been introduced using wrist-worn photoplethysmography (PPG) to provide high accuracy of instantaneous heart rate (HR) estimation, including during high-intensity exercise. Most studies focus on using acceleration and/or gyroscope signals for the motion artifact (MA) reference, which attenuates or cancels out noise from the MA-corrupted PPG signals. We aim to open and pave the path to find an appropriate MA reference selection for MA cancelation in PPG. METHODS: We investigated how the acceleration and gyroscope reference signals correlate with the MAs of the distorted PPG signals and derived both mathematically and experimentally an adaptive MA reference selection approach. We applied our algorithm to five state-of-the-art (SOTA) methods for the performance evaluation. In addition, we compared the four MA reference selection approaches, i.e. with acceleration signal only, with gyroscope signal only, with both signals, and using our proposed adaptive selection. RESULTS: When applied to 47 PPG recordings acquired during intensive physical exercise from two different datasets, our proposed adaptive MA reference selection method provided higher accuracy than the other MA selection approaches for all five SOTA methods. CONCLUSION: Our proposed adaptive MA reference selection approach can be used in other MA cancelation methods and reduces the HR estimation error. SIGNIFICANCE: We believe that this study helps researchers to address acceleration and gyroscope signals as accurate MA references, which eventually improves the overall performance for estimating HRs through the various algorithms developed by research groups.

Real-time realizable mobile imaging photoplethysmography.

Photoplethysmography imaging (PPGI) sensors have attracted a significant amount of attention as they enable the remote monitoring of heart rates (HRs) and thus do not require any additional devices to be worn on fingers or wrists. In this study, we mounted PPGI sensors on a robot for active and autonomous HR (R-AAH) estimation. We proposed an algorithm that provides accurate HR estimation, which can be performed in real time using vision and robot manipulation algorithms. By simplifying the extraction of facial skin images using saturation (S) values in the HSV color space, and selecting pixels based on the most frequent S value within the face image, we achieved a reliable HR assessment. The results of the proposed algorithm using the R-AAH method were evaluated by rigorous comparison with the results of existing algorithms on the UBFC-RPPG dataset (n = 42). The proposed algorithm yielded an average absolute error (AAE) of 0.71 beats per minute (bpm). The developed algorithm is simple, with a processing time of less than 1 s (275 ms for an 8-s window). The algorithm was further validated on our own dataset (BAMI-RPPG dataset [n = 14]) with an AAE of 0.82 bpm.

Coronary artery spasm due to acute myocarditis in an adolescent: a case report.

BACKGROUND: Myocarditis refers to the inflammation of the myocardium caused by infection or autoimmune disease that may or may not present with clinical manifestations, such as gastrointestinal symptoms, dyspnea, chest pain, or sudden death. Although myocarditis and coronary artery vasospasm may mimic ST-segment elevation myocardial infarction (STEMI) with normal coronary arteries on angiography, acute myocarditis rarely causes coronary artery spasm. Here, we report a case of coronary artery spasm with reversible electrocardiographic changes mimicking STEMI in an adolescent with acute myocarditis. CASE PRESENTATION: A 15-year-old boy present with sudden-onset repeated chest pain following a 3-day history of flu-like illness. Cardiac biomarkers were significantly elevated. Electrocardiography showed ST-segment elevation in the absence of detectable vasospasm on coronary angiography. These findings were consistent with the diagnosis of coronary artery spasm secondary to acute myocarditis. Treatment with immunoglobulin for 2 days improved his condition. The patient was discharged on the 12th day with complete resolution of symptoms and normalization of electrocardiogram findings. CONCLUSIONS: We reported a case of coronary artery spasm due to acute myocarditis. This study highlights the importance of considering coronary artery spasm due to acute myocarditis as a differential diagnosis in patients presenting with signs of STEMI as these diseases have different medical management strategies.

Right Pulmonary Artery Originating from Ascending Aorta (Hemitruncus Arteriosus) with VACTERL Association in a Neonate: A Case Report.

Vertebral, anal, cardiac, tracheo-esophageal fistula, renal and limb (VACTERL) association is defined as a condition including at least three of the above-mentioned anomalies in the same infant. Several cardiac defects that have been reported as a part of the VACTERL association are ventricular and atrial septal defects, hypoplastic left heart syndrome, transposition of the great arteries and tetralogy of Fallot. Anomalous origin of pulmonary artery (AOPA) from the ascending aorta is an unusual and critical cardiovascular anomaly, which frequently involves the right pulmonary artery (RPA). A male neonate was delivered by normal spontaneous vaginal delivery at 39 weeks and 3 days gestation, weighting 2660 gm. He was diagnosed with VACTERL association with five abnormalities: vertebral abnormalities, anal atresia, cardiovascular anomaly (right pulmonary artery originating from ascending aorta), tracheo-esophageal fistula and renal anomalies. AOPA origination from ascending aorta as part of the VACTERL association in a neonate is a rare congenital cardiovascular malformation. Here we present a rare case of RPA originating from the ascending aorta seen with VACTERL association in a neonate.

A Deep Residual U-Net Algorithm for Automatic Detection and Quantification of Ascites on Abdominopelvic Computed Tomography Images Acquired in the Emergency Department: Model Development and Validation.

BACKGROUND: Detection and quantification of intra-abdominal free fluid (ie, ascites) on computed tomography (CT) images are essential processes for finding emergent or urgent conditions in patients. In an emergency department, automatic detection and quantification of ascites will be beneficial. OBJECTIVE: We aimed to develop an artificial intelligence (AI) algorithm for the automatic detection and quantification of ascites simultaneously using a single deep learning model (DLM). METHODS: We developed 2D DLMs based on deep residual U-Net, U-Net, bidirectional U-Net, and recurrent residual U-Net (R2U-Net) algorithms to segment areas of ascites on abdominopelvic CT images. Based on segmentation results, the DLMs detected ascites by classifying CT images into ascites images and nonascites images. The AI algorithms were trained using 6337 CT images from 160 subjects (80 with ascites and 80 without ascites) and tested using 1635 CT images from 40 subjects (20 with ascites and 20 without ascites). The performance of the AI algorithms was evaluated for diagnostic accuracy of ascites detection and for segmentation accuracy of ascites areas. Of these DLMs, we proposed an AI algorithm with the best performance. RESULTS: The segmentation accuracy was the highest for the deep residual U-Net model with a mean intersection over union (mIoU) value of 0.87, followed by U-Net, bidirectional U-Net, and R2U-Net models (mIoU values of 0.80, 0.77, and 0.67, respectively). The detection accuracy was the highest for the deep residual U-Net model (0.96), followed by U-Net, bidirectional U-Net, and R2U-Net models (0.90, 0.88, and 0.82, respectively). The deep residual U-Net model also achieved high sensitivity (0.96) and high specificity (0.96). CONCLUSIONS: We propose a deep residual U-Net-based AI algorithm for automatic detection and quantification of ascites on abdominopelvic CT scans, which provides excellent performance.

Classification of vertices on social networks by multiple approaches.

Due to the advent of the expressions of data other than tabular formats, the topological compositions which make samples interrelated came into prominence. Analogically, those networks can be interpreted as social connections, dataflow maps, citation influence graphs, protein bindings, etc. However, in the case of social networks, it is highly crucial to evaluate the labels of discrete communities. The reason for such a study is the importance of analyzing graph networks to partition the vertices by only using the topological features of network graphs. For each interaction-based entity, a social graph, a mailing dataset, and two citation sets are selected as the testbench repositories. The research mainly focused on evaluating the significance of three artificial intelligence approaches on four different datasets consisting of vertices and edges. Overall, one of these methods so-called "harmonic functions", resulted in the best form to classify those constituents of graph-shaped datasets. This research not only accessed the most valuable method but also determined how graph neural networks work and the need to improve against non-neural network approaches which are faster and computationally cost-effective. Also in this paper, we will show that there is a limit to be accessed by prospective graph neural network variations by using the topological features of trialed networks.

Artificial intelligence to predict in-hospital mortality using novel anatomical injury score.

The aim of the study is to develop artificial intelligence (AI) algorithm based on a deep learning model to predict mortality using abbreviate injury score (AIS). The performance of the conventional anatomic injury severity score (ISS) system in predicting in-hospital mortality is still limited. AIS data of 42,933 patients registered in the Korean trauma data bank from four Korean regional trauma centers were enrolled. After excluding patients who were younger than 19 years old and those who died within six hours from arrival, we included 37,762 patients, of which 36,493 (96.6%) survived and 1269 (3.4%) deceased. To enhance the AI model performance, we reduced the AIS codes to 46 input values by organizing them according to the organ location (Region-46). The total AIS and six categories of the anatomic region in the ISS system (Region-6) were used to compare the input features. The AI models were compared with the conventional ISS and new ISS (NISS) systems. We evaluated the performance pertaining to the 12 combinations of the features and models. The highest accuracy (85.05%) corresponded to Region-46 with DNN, followed by that of Region-6 with DNN (83.62%), AIS with DNN (81.27%), ISS-16 (80.50%), NISS-16 (79.18%), NISS-25 (77.09%), and ISS-25 (70.82%). The highest AUROC (0.9084) corresponded to Region-46 with DNN, followed by that of Region-6 with DNN (0.9013), AIS with DNN (0.8819), ISS (0.8709), and NISS (0.8681). The proposed deep learning scheme with feature combination exhibited high accuracy metrics such as the balanced accuracy and AUROC than the conventional ISS and NISS systems. We expect that our trial would be a cornerstone of more complex combination model.

Persistent fifth aortic arch associated with aortic coarctation: a case of surgical correction without artificial material.

BACKGROUND: Persistent fifth aortic arch (PFAA) is a rare anomaly often associated with aortic coarctation or interruption, and various surgical techniques for this anomaly have been reported. Herein, we show a case of an infant with PFAA and severe aortic coarctation. CASE PRESENTATION: A 41-day-old female infant was admitted for sustained fever. Initially, the patient was diagnosed with bacterial meningitis, and echocardiography showed PFAA with severe aortic coarctation. Because the patient presented progressive oliguria and metabolic acidosis, she was transferred for emergency cardiac surgical intervention. The aortic arch was reconstructed using end-to-side anastomosis between the fifth aortic arch and the descending aorta without any artificial conduit or patching material. CONCLUSIONS: PFAA with aortic coarctation can be repaired by various surgical methods. Among them, our surgical approach is easy and effective, has growth potential, and an additional surgery is not needed.

Influence of Features on Accuracy of Anomaly Detection for an Energy Trading System.

The biggest problem with conventional anomaly signal detection using features was that it was difficult to use it in real time and it requires processing of network signals. Furthermore, analyzing network signals in real-time required vast amounts of processing for each signal, as each protocol contained various pieces of information. This paper suggests anomaly detection by analyzing the relationship among each feature to the anomaly detection model. The model analyzes the anomaly of network signals based on anomaly feature detection. The selected feature for anomaly detection does not require constant network signal updates and real-time processing of these signals. When the selected features are found in the received signal, the signal is registered as a potential anomaly signal and is then steadily monitored until it is determined as either an anomaly or normal signal. In terms of the results, it determined the anomaly with 99.7% (0.997) accuracy in f(4)(S0) and in case f(4)(REJ) received 11,233 signals with a normal or 171anomaly judgment accuracy of 98.7% (0.987).

Effectiveness of posterior aortopexy for the left pulmonary vein obstruction between the left atrium and the descending aorta.

BACKGROUND: Left pulmonary vein (PV) obstruction can occur due to compression between the left atrium (LA) and the descending aorta (DA). One of the effective solutions for this problem is posterior aortopexy. In this study, we have reported five cases of posterior aortopexy to relieve left PV obstruction between the LA and the DA. METHODS: Since August 2012, five patients have undergone posterior aortopexy for compression of the left PV between the LA and the DA. The median age and weight of the patients at the time of operation were 5.5 months (range, 1-131 months) and 5.2 kg (range, 4.2-29.5 kg), respectively. The left PV obstruction was initially diagnosed on echocardiography in four patients and computed tomography angiography in one patient. The median peak pressure gradient across the obstructed left PV was 7.3 mmHg (range, 4-20 mmHg). Concomitant procedures were ventricular septal defect closure in one patient and patent ductus arteriosus ligation in one patient. RESULTS: There was no PV obstruction on echocardiography in any of the patients after the operation except in the case of one patient who had diffuse pulmonary vein stenosis. The median follow-up duration was 34 months (range, 14-89 months), and during follow-up no incidence of the left PV obstruction was observed in any of the surviving patients. CONCLUSIONS: The posterior aortopexy technique could be a good surgical option for the left PV obstruction caused by compression between the LA and the anteriorly positioned DA.

A Case of Prenatally Diagnosed Uhl's Anomaly with Absent Pulmonary Valve Leaflets and Dysplastic Tricuspid Valve.

Uhl's anomaly is a very rare malformation of unknown cause, characterized by complete or partial absence of the right ventricular myocardium. The cardiac malformation causes progressive right heart failure, increased right-sided cardiac pressure, massive peripheral edema, and ascites. Patients usually present in infancy and rarely survive to adulthood. Previously, diagnosis was made at post-mortem evaluation, but advances in cardiac imaging now permit diagnosis during fetal life. We report a case of Uhl's anomaly in a newborn baby imaged at 23 + 3 weeks of gestation by fetal echocardiography. There was an aneurysmally dilated thin-walled right ventricle with hypertrophy of the right ventricular apical muscles, the tricuspid valve was dysplastic, and the pulmonary valve leaflets were absent.