[Diagnosis and treatment of feline persistent ductus arteriosus: a retrospective evaluation of 17 cats]. / Diagnose und Therapie eines persistierenden Ductus arteriosus: Eine retrospektive Evaluation bei 17 Katzen.

OBJECTIVE: The objective of this study was to evaluate the signalement, clinical features, and echocardiographic findings of cats diagnosed with patent ductus arteriosus (PDA) as well as short- and medium-term outcome after successful ligation of the PDA. MATERIAL AND METHODS: Over a 10-year period 17 cats were diagnosed with PDA by transthoracic echocardiography. Thirteen cats were surgically treated by thoracotomy and ligation of the PDA. RESULTS: In all cats, a heart murmur was detected. In 88.2% of the cases, this presented as grade 4 out of 6 murmur (15/17 cats). A continuous murmur was more common (10/17 cats; 58.9%) than a systolic murmur (7/17 cats; 41.1%). Echocardiography showed that left ventricular internal diameter end diastole (LVIDd) and left ventricular internal diameter end systole (LVIDs) were significantly above reference values in the majority of cats. Mean diameter of the PDA measured at the widest point of the vessel was 3.4 mm (± 1.08 mm) and mean maximum flow velocity amounted to 5,06 m/sec (2,6m/sec-6,4m/sec). Surgery was successfully performed in all cats treated by surgical ligation and all of these patients were discharged after postoperative inpatient therapy. One cat experienced perioperative bleeding from the PDA, which was stopped efficaciously. This cat exhibited a residual shunt directly postoperatively; this could no longer be visualized in a re-check echocardiography 3 months later. Six cats were followed over a longer period of time. CONCLUSIONS: The surgical prognosis in this case study is very good with a postoperative survival rate of 100%. CLINICAL RELEVANCE: Surgical treatment of PDA is curative in animals not displaying advanced cardiac lesions. The auscultation of a heart murmur can provide initial findings indicative of PDA. Therefore, cardiac auscultation is warranted at every first presentation of a kitten. It must however be taken into consideration that not every cat with PDA necessarily has a continuous murmur but may display a systolic heart murmur. Therefore, it is important give utmost attention to the patients' clinical signs.

Spontaneous rupture of the right aortic sinus resulting in a sterile aorto-atrial fistula in a pet rabbit (Oryctolagus cuniculus).

Aortocardiac fistula is a broad term used to describe defects between the aorta and other cardiac chambers that can occur in humans and animals. A 1.5-year-old, 1.7 kg, male castrated Holland lop rabbit (Oryctolagus cuniculus) was presented for a two-week history of a heart murmur with corresponding cardiomegaly on radiographs. Physical examination confirmed a grade-V/VI continuous heart murmur on the right sternal border with a regular rhythm and a gallop sound. Echocardiography revealed an aortic-to-right-atrial fistula causing severe left-sided volume overload. Based on the echocardiographic findings, rupture of the right aortic sinus was suspected. Due to the poor prognosis, euthanasia was elected. On necropsy, a fistula was found connecting the right aortic sinus with the right atrium, without evidence of an inflammatory response nor evidence of an infectious etiology. The sudden onset of a heart murmur supported acquired fistulation from a ruptured aortic sinus (also known as the sinus of Valsalva), though a congenital malformation could not be completely excluded.

Murmur identification and outcome prediction in phonocardiograms using deep features based on Stockwell transform.

Traditionally, heart murmurs are diagnosed through cardiac auscultation, which requires specialized training and experience. The purpose of this study is to predict patients' clinical outcomes (normal or abnormal) and identify the presence or absence of heart murmurs using phonocardiograms (PCGs) obtained at different auscultation points. A semi-supervised model tailored to PCG classification is introduced in this study, with the goal of improving performance using time-frequency deep features. The study begins by investigating the behavior of PCGs in the time-frequency domain, utilizing the Stockwell transform to convert the PCG signal into two-dimensional time-frequency maps (TFMs). A deep network named AlexNet is then used to derive deep feature sets from these TFMs. In feature reduction, redundancy is eliminated and the number of deep features is reduced to streamline the feature set. The effectiveness of the extracted features is evaluated using three different classifiers using the CinC/Physionet challenge 2022 dataset. For Task I, which focuses on heart murmur detection, the proposed approach achieved an average accuracy of 93%, sensitivity of 91%, and F1-score of 91%. According to Task II of the CinC/Physionet challenge 2022, the approach showed a clinical outcome cost of 5290, exceeding the benchmark set by leading methods in the challenge.

Two-Dimensional and Doppler trans-thoracic echocardiographic patterns of suspected pediatric heart diseases at Tibebe--Ghion specialized Teaching Hospital and Adinas General Hospital, Bahir Dar, North-west Ethiopia:-An experience from an LMIC.

BACKGROUND: Transthoracic Echocardiography is the first-line, non-invasive, and accessible imaging modality to evaluate heart disease anatomy, physiology, and hemodynamics. We aim to describe the trans-thoracic echocardiography pattern of pediatric heart diseases and reasons for referral in children referred to Bahir Dar University Tibebe-Ghion Hospital and Adinas General Hospital. METHOD: A descriptive cross-sectional study of the archived Transthoracic, Two Dimensional, and Doppler Echocardiography assessments of children from birth to fifteen years of age performed between June 2019 to May 2023 was done. Data were collected retrospectively from February 01, 2023 -May 31, 2023. Categorical variables like gender, referral reasons for echocardiography, and patterns of pediatric heart lesions were analyzed in the form of proportions and presented in tables and figures. Discrete variables including age were summarized as means (SD) and medians(IQR). RESULTS: Out of 3,647 Children enrolled; 1,917 (52.6%) were males and 1,730 (47.4%) were females. The median (IQR) age of children enrolled was 24 months (5 to 96). Cardiac murmur (33%) was the most common reason for echocardiography followed by, Respiratory Distress (18%), Syndromic Child (15%), easy fatigability/ Diaphoresis (14.3%), congestive heart failure (14%), and rheumatic fever (13.2%). Congenital heart defect (CHD) accounts for 70% of all heart diseases, followed by rheumatic heart disease (21%). Isolated ventricular septal defect(VSD) was the most common CHD (21%) followed by isolated Patent ductus arteriosus (15%), isolated atrial septal defect (10%), Isolated atrioventricular septal defect (6%) and isolated pulmonary stenosis (5%). Cyanotic CHD accounts for 11.5% of all heart diseases. Tetralogy of Fallot (30%), d-TGA (20%), and double outlet right ventricle (19%) were the most common cyanotic CHDs. CONCLUSIONS: In our study, congenital heart lesions are the most common diagnosis and cardiac murmurs are the most common presenting reasons for echocardiography evaluation.

HBNET: A blended ensemble model for the detection of cardiovascular anomalies using phonocardiogram.

BACKGROUND: Cardiac diseases are highly detrimental illnesses, responsible for approximately 32% of global mortality [1]. Early diagnosis and prompt treatment can reduce deaths caused by cardiac diseases. In paediatric patients, it is challenging for paediatricians to identify functional murmurs and pathological murmurs from heart sounds. OBJECTIVE: The study intends to develop a novel blended ensemble model using hybrid deep learning models and softmax regression to classify adult, and paediatric heart sounds into five distinct classes, distinguishing itself as a groundbreaking work in this domain. Furthermore, the research aims to create a comprehensive 5-class paediatric phonocardiogram (PCG) dataset. The dataset includes two critical pathological classes, namely atrial septal defects and ventricular septal defects, along with functional murmurs, pathological and normal heart sounds. METHODS: The work proposes a blended ensemble model (HbNet-Heartbeat Network) comprising two hybrid models, CNN-BiLSTM and CNN-LSTM, as base models and Softmax regression as meta-learner. HbNet leverages the strengths of base models and improves the overall PCG classification accuracy. Mel Frequency Cepstral Coefficients (MFCC) capture the crucial audio signal characteristics relevant to the classification. The amalgamation of these two deep learning structures enhances the precision and reliability of PCG classification, leading to improved diagnostic results. RESULTS: The HbNet model exhibited excellent results with an average accuracy of 99.72% and sensitivity of 99.3% on an adult dataset, surpassing all the existing state-of-the-art works. The researchers have validated the reliability of the HbNet model by testing it on a real-time paediatric dataset. The paediatric model's accuracy is 86.5%. HbNet detected functional murmur with 100% precision. CONCLUSION: The results indicate that the HbNet model exhibits a high level of efficacy in the early detection of cardiac disorders. Results also imply that HbNet has the potential to serve as a valuable tool for the development of decision-support systems that aid medical practitioners in confirming their diagnoses. This method makes it easier for medical professionals to diagnose and initiate prompt treatment while performing preliminary auscultation and reduces unnecessary echocardiograms.

Identification of Congenital Valvular Murmurs in Young Patients Using Deep Learning-Based Attention Transformers and Phonocardiograms.

One in every four newborns suffers from congenital heart disease (CHD) that causes defects in the heart structure. The current gold-standard assessment technique, echocardiography, causes delays in the diagnosis owing to the need for experts who vary markedly in their ability to detect and interpret pathological patterns. Moreover, echo is still causing cost difficulties for low- and middle-income countries. Here, we developed a deep learning-based attention transformer model to automate the detection of heart murmurs caused by CHD at an early stage of life using cost-effective and widely available phonocardiography (PCG). PCG recordings were obtained from 942 young patients at four major auscultation locations, including the aortic valve (AV), mitral valve (MV), pulmonary valve (PV), and tricuspid valve (TV), and they were annotated by experts as absent, present, or unknown murmurs. A transformation to wavelet features was performed to reduce the dimensionality before the deep learning stage for inferring the medical condition. The performance was validated through 10-fold cross-validation and yielded an average accuracy and sensitivity of 90.23 % and 72.41 %, respectively. The accuracy of discriminating between murmurs' absence and presence reached 76.10 % when evaluated on unseen data. The model had accuracies of 70 %, 88 %, and 86 % in predicting murmur presence in infants, children, and adolescents, respectively. The interpretation of the model revealed proper discrimination between the learned attributes, and AV channel was found important (score 0.75) for the murmur absence predictions while MV and TV were more important for murmur presence predictions. The findings potentiate deep learning as a powerful front-line tool for inferring CHD status in PCG recordings leveraging early detection of heart anomalies in young people. It is suggested as a tool that can be used independently from high-cost machinery or expert assessment.

Rare cardiac tumour-nodular fasciitis.

Left ventricular tumour is a rare condition in children. The causes include vegetations, thrombus, and fibroma. 2-year-old asymptomatic female presented with an innocent heart murmur at 6 months of age. Subsequent follow-ups at 18 months of age showed left ventricular mass. Surgical pathology revealed "nodular fasciitis." This type of tumour has never been described in the heart before.

Occurrence of cardiovascular events in 168 cats with acute urinary tract obstruction.

Background: Cardiovascular dysfunction associated with acute kidney injury has been recently described in veterinary medicine, but limited information is available for cats with urinary tract obstruction (UTO). Objective: This retrospective study aimed to describe the type, frequency, timeline, and risk factors for cardiovascular events (CVEs) in cats treated for acute UTO. Animals and procedures: Medical records of cats admitted to the intensive care unit for either upper (ureteral: UUTO) or lower (urethral: LUTO) UTO from 2016 to 2021 were reviewed. Cardiovascular events were defined as development of arrhythmia, heart murmur or gallop sound, clinical signs consistent with fluid overload (CRFO), or decreased tissue perfusion (DTP). Results: One hundred and sixty-eight cats with UTO were recruited (56 with UUTO and 112 with LUTO). Cardiovascular events were reported in 61.9% of cases, including arrhythmia (33.6%), gallop rhythm (28.1%), heart murmur (15.3%), CRFO (14.4%), and DTP (8.6%). Potassium concentration, preexisting chronic kidney disease, and renal pelvic dilation at abdominal ultrasonography were associated with CVE occurrence in multivariate analysis. Conclusions: This study highlighted frequent CVEs in cats treated for UTO, with a potential strong impact on outcome. Therefore, cardiovascular parameters of cats with preexisting chronic kidney disease or those admitted with hyperkalemia or renal pelvic dilation should be closely monitored.

Survenue d'anomalies cardio-vasculaires chez 168 chats présentés pour obstruction du tractus urinaire. Contexte: Si des anomalies cardiovasculaires secondaires à une insuffisance rénale aigue ont été décrites récemment en médecine vétérinaire, ces données restent limitées concernant les obstructions du tractus urinaire (OTU) chez le chat. Objectif: Décrire le type, la fréquence, le délai d'apparition et les facteurs de risques d'anomalies cardio-vasculaires (ACV) chez des chats hospitalisés pour OTU aigue. Animaux et protocoles: Les dossiers médicaux des chats admis en unité de soins intensifs pour obstruction du tractus urinaire haut ( urétérales-OTUH) et bas (urétrales-OTUB) entre 2016 et 2021 ont été consultés. Les ACV retenus étaient des arythmies cardiaques, le développement de souffles cardiaques et de bruits de galop, les signes relatifs à une surcharge en fluide (SRSF) et de diminution de la perfusion tissulaire (SDPT). Résultats: Cent soixante-huit chats avec des OTU ont été recrutés (56 OTUH, 112 OTUB). Des ACV ont été observés dans 61,9 % des cas, incluant des arythmies (33,6 %), l'apparition de bruits de galop (28,1 %) et de souffles cardiaques (15,3 %), des SRSF (14,4 %) et des SDPT (8,6 %). La concentration plasmatique en potassium, la présence d'une MRC sous-jacente et d'une dilatation pyélique à l'échographie abdominale ont été associées à la présence d'ACV par l'analyse multivariée. Conclusions: Cette étude montre que les ACV surviennent fréquemment chez les chats présentés pour OTU, et suggère un impact sur la survie de ces animaux. Les animaux avec un historique de MRC, ceux présentés avec une hyperkaliémie et/ou avec une dilatation pyélique à l'échographie abdominale devraient être surveillés avec plus de précautions que les autres.(Traduit per les auteurs).

[Rheumatic cardiopathies and its risk factors: about 50 cases]. / Les complications cardiaques du rhumatisme articulaire aigu et ses facteurs de risque : étude de 50 observations.

INTRODUCTION: Acute rheumatic fever (ARF) is a multi-systemic disease, in which cardiac involvement is the most serious major manifestation of disease. The aim of this study was to analyse cardiac involvement in children with ARF and his risk factors. MATERIALS AND METHODS: It were a retrospective study including all children under the age of 14 years who were hospitalized for ARF in the pediatric department of the CHU Hédi Chaker of Sfax, during a period of twelve years (2010-2022). RESULTS: We collected 50 cases (31 boys and 19 girls). Twenty-two patients (44%) developed cardiac lesions. The mean age at diagnosis was 9.6 years [5-14 years]. A pathological heart murmur was detected in 14 cases (n = 14/22) was classified as mild carditis in 15 cases, moderate carditis in 5 cases and severe in 2 cases. The median follow-up time was 3,3 years. Nineteen patients developed valvular sequelae Risk factors of cardiac lesions was: age more than 8 years, heart murmur, allonged PR, CRP > 100 mg/l and VS > 100 mm. CONCLUSION: CR is still a public health problem in Tunisia. It is a serious pathology that can cause serious increases in morbidity rates. Thus, we must strengthen preventive strategies.

Major congenital heart defects are rarely diagnosed after newborns' hospital discharge with modern screening.

AIM: Our aim was to assess undiagnosed congenital heart defects (CHD) after newborns' hospital discharge in patients with a murmur or CHD suspicion, to find out the signs that predict CHDs and to estimate the costs of the examinations. METHODS: We reviewed retrospective medical records of patients (n = 490) referred for the evaluation of CHD suspicion during 2017-2018. RESULTS: The median age of the patients was 2.5 (IQR 0.5-7.4) years. Sixty-three (13%) patients had an abnormal echocardiography. Neither ductal-dependent nor cyanotic CHDs were found. Cardiac interventions were performed for 14 out of 63 (22%) patients. Clinical signs indicating CHDs were murmur grade ≥3 (10/11 [91%] vs. 53/479 [11%], p < 0.001) and harsh murmur (15/44 [34%] vs. 48/446 [11%], p < 0.001). Abnormal electrocardiography did not indicate CHD (8/40 [20%] vs. 55/447 [12%], p = 0.165). The total cost of the examinations was 259 700€. The share of the cost of studies assessed as benign was 59%. CONCLUSION: Only a few CHDs were found after newborn hospital discharge among patients who received foetal and newborn screening and were examined due to CHD suspicion. The high number of benign murmurs in children leads to many referrals, resulting in unnecessary healthcare costs.

Early-warning of Cardiac Condition through Detection of Murmur in Heart Sound - A Case Study.

Cardiovascular disease, particularly Rheumatic Heart Disease (RHD), is one of the leading causes of death in many developing countries. RHD is manageable and treatable with early detection. However, multiple countries across the globe suffer from a scarcity of experienced physicians who can perform screening at large scales. Advancements in machine learning and signal processing have paved way for Phonocardiogram (PCG)-based automatic heart sound classification. The direct implication of such methods is that it is possible to enable a person without specialized training to detect potential cardiac conditions with just a digital stethoscope. Hospitalization or life-threatening situations can be dramatically reduced via such early screenings. Towards this, we conducted a case study amongst a population from a particular geography using machine learning and deep learning methods for the detection of murmur in heart sounds. The methodology consists of first pre-processing and identifying normal vs. abnormal heart sound signals using 3 state-of-the-art methods. The second step further identifies the murmur to be systolic or diastolic by capturing the auscultation location. Abnormal findings are then sent for early attention of clinicians for proper diagnosis. The case study investigates the efficacy of the automated method employed for early screening of potential RHD and initial encouraging results of the study are presented.

Deep Learning Algorithms to Detect Murmurs Associated With Structural Heart Disease.

Background The success of cardiac auscultation varies widely among medical professionals, which can lead to missed treatments for structural heart disease. Applying machine learning to cardiac auscultation could address this problem, but despite recent interest, few algorithms have been brought to clinical practice. We evaluated a novel suite of Food and Drug Administration-cleared algorithms trained via deep learning on >15 000 heart sound recordings. Methods and Results We validated the algorithms on a data set of 2375 recordings from 615 unique subjects. This data set was collected in real clinical environments using commercially available digital stethoscopes, annotated by board-certified cardiologists, and paired with echocardiograms as the gold standard. To model the algorithm in clinical practice, we compared its performance against 10 clinicians on a subset of the validation database. Our algorithm reliably detected structural murmurs with a sensitivity of 85.6% and specificity of 84.4%. When limiting the analysis to clearly audible murmurs in adults, performance improved to a sensitivity of 97.9% and specificity of 90.6%. The algorithm also reported timing within the cardiac cycle, differentiating between systolic and diastolic murmurs. Despite optimizing acoustics for the clinicians, the algorithm substantially outperformed the clinicians (average clinician accuracy, 77.9%; algorithm accuracy, 84.7%.) Conclusions The algorithms accurately identified murmurs associated with structural heart disease. Our results illustrate a marked contrast between the consistency of the algorithm and the substantial interobserver variability of clinicians. Our results suggest that adopting machine learning algorithms into clinical practice could improve the detection of structural heart disease to facilitate patient care.

Aortic valve insufficiency caused by a hypoplastic aortic valve in a calf.

A 1-month-old crossbred calf was referred for examination due to marked systolic heart murmurs and poor growth. The heart murmur was most audible on the right side of the cranial thorax. Cardiomegaly was evident on chest radiography, and echocardiography demonstrated aortic regurgitation and decreased fractional shortening. Cardiomegaly, aortic root dilation and cardiac displacement were confirmed by computed tomography. At necropsy, the heart was enlarged, and all three aortic valve leaflets were irregularly shaped. In calves with chronic aortic insufficiency, remodeling displacement of the heart and aorta causes changes in the location and timing of heart murmurs. Therefore, aortic insufficiency cannot be ruled out when a systolic heart murmur can be observed in the right chest wall.

Pediatric Murmurs.

Many healthy children may be found to have a murmur on physical exam. Whether this murmur is discovered at a routine health maintenance visit or as a result of a focused exam on a child with illness, it is just one finding and must be considered in the context of the child's history and other physical exam findings. Murmurs associated with heart defect or dysfunction occur most often in infancy. Most murmurs discovered in children, especially after infancy, between ages 3 to 6 and in young-adulthood, are innocent or benign murmurs and less likely a symptom of cardiac dysfunction or defect.

Short-Term Effects of Different Transcatheter Edge-to-Edge Devices on Mitral Valve Geometry.

Background Short-term effects on mitral valve (MV) anatomy after transcatheter edge-to-edge repair using the PASCAL system remain unknown. Precise quantification might allow for an advanced analysis of predictors for mean transmitral gradients. Methods and Results Consecutive patients undergoing transcatheter edge-to-edge repair for secondary mitral regurgitation using PASCAL or MitraClip systems were included. Quantification of short-term MV changes throughout the cardiac cycle was performed using peri-interventional 3-dimensional MV images. Predictors for mean transmitral gradients were identified in univariable and multivariable regression analysis. Long-term results were described during 1-year follow-up. A total of 100 patients undergoing transcatheter edge-to-edge repair using PASCAL (n=50) or MitraClip systems (n=50) were included. Significant reductions of anterior-posterior diameter, annular circumference, and area throughout the cardiac cycle were found in both cohorts (P<0.05 for all). Anatomic MV orifice area remained larger in the PASCAL cohort in mid (2.8±1.0 versus 2.4±0.9 cm2; P=0.049) and late diastole (2.7±1.1 versus 2.2±0.8 cm2; P=0.036) compared with the MitraClip cohort. Besides a device-specific profile of independent predictor of mean transmitral gradients, reduction of middiastolic anatomic MV orifice area was identified as an independent predictor in both the PASCAL (ß=-0.410; P=0.001) and MitraClip cohorts (ß=-0.318; P=0.028). At follow-up, reduction of mitral regurgitation grade to mild or less was more durable in the PASCAL cohort (90% versus 72%; P=0.035). Conclusions PASCAL and MitraClip showed comparable short-term effects on MV geometry. However, PASCAL might better preserve MV function and demonstrated more durable mitral regurgitation reduction during follow-up. Identification of independent predictors for mean transmitral gradients might potentially help to guide device selection in the future.

Neutrophil-to-lymphocyte ratio is increased in dogs with acute congestive heart failure secondary to myxomatous mitral valve disease compared to both dogs with heart murmurs and healthy controls.

OBJECTIVE: To retrospectively evaluate neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) as a biomarker for severity and short-term outcomes of congestive heart failure (CHF) secondary to myxomatous mitral valve disease (MMVD) in dogs. ANIMALS: 47 dogs with CHF secondary to MMVD, 47 dogs with presumptive preclinical MMVD, and 47 control dogs. METHODS: Medical record data (signalment, physical examination findings, medical treatments instituted, American College of Veterinary Internal Medicine MMVD stage, length of hospitalization, outcome, and hospital re-presentation due to CHF) from March 2012 through March 2022 for each dog were collected. Statistical analyses were performed with Mann-Whitney, Spearman correlation, and Fisher exact tests. RESULTS: NLR (but not PLR) was significantly higher in dogs with CHF secondary to MMVD (6.41) compared to presumptive preclinical MMVD dogs (4.66; P < .001) and control dogs (3.95; P < .001). Dogs with higher NLR and PLR received significantly higher cumulative dosages of loop-diuretic therapy during hospitalization (ρ = 0.3, P = .04; and ρ = 0.4, P = .02, respectively). There was a positive association between NLR and duration of oxygen supplementation within the CHF group (ρ = 0.4; P = .01). CLINICAL RELEVANCE: The increased diuretic dose and time receiving oxygen supplementation may represent increased disease severity for which NLR (and to a lesser extent PLR) may serve as a readily available marker. The data presented provide information regarding some of the systemic inflammatory changes seen in CHF secondary to MMVD in dogs. Future research should include prospective, longitudinal studies to provide insight into the long-term prognostic value of NLR and PLR in dogs with CHF.

StethAid: A Digital Auscultation Platform for Pediatrics.

(1) Background: Mastery of auscultation can be challenging for many healthcare providers. Artificial intelligence (AI)-powered digital support is emerging as an aid to assist with the interpretation of auscultated sounds. A few AI-augmented digital stethoscopes exist but none are dedicated to pediatrics. Our goal was to develop a digital auscultation platform for pediatric medicine. (2) Methods: We developed StethAid-a digital platform for artificial intelligence-assisted auscultation and telehealth in pediatrics-that consists of a wireless digital stethoscope, mobile applications, customized patient-provider portals, and deep learning algorithms. To validate the StethAid platform, we characterized our stethoscope and used the platform in two clinical applications: (1) Still's murmur identification and (2) wheeze detection. The platform has been deployed in four children's medical centers to build the first and largest pediatric cardiopulmonary datasets, to our knowledge. We have trained and tested deep-learning models using these datasets. (3) Results: The frequency response of the StethAid stethoscope was comparable to those of the commercially available Eko Core, Thinklabs One, and Littman 3200 stethoscopes. The labels provided by our expert physician offline were in concordance with the labels of providers at the bedside using their acoustic stethoscopes for 79.3% of lungs cases and 98.3% of heart cases. Our deep learning algorithms achieved high sensitivity and specificity for both Still's murmur identification (sensitivity of 91.9% and specificity of 92.6%) and wheeze detection (sensitivity of 83.7% and specificity of 84.4%). (4) Conclusions: Our team has created a technically and clinically validated pediatric digital AI-enabled auscultation platform. Use of our platform could improve efficacy and efficiency of clinical care for pediatric patients, reduce parental anxiety, and result in cost savings.