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Veterinary Research Communications Apr 2024The present research aimed to document the incidence, clinical signs, haematological, and serum biochemical alterations, as well as electrocardiography and...
The present research aimed to document the incidence, clinical signs, haematological, and serum biochemical alterations, as well as electrocardiography and echocardiography findings in 62 buffaloes (selected from a total of 240) infected with Trypanosoma evansi. The study spanned one year, from January 2022 to December 2022. Morphological identification of Trypanosoma evansi was done by the presence of a centrally positioned nucleus with a small sub-terminal kinetoplast at the posterior position through microscopic examination of Giemsa stained peripheral blood smears. The incidence of trypanosomosis were determined to be 26% (62/240) using stained blood smear examination and 41% (98/240) through polymerase chain reaction assay. Clinical signs exhibited by buffaloes with trypanosomosis included the lack of rumination (94%; 58/62), anorexia (90%; 56/62), emaciation (87%; 54/62), loss of milk yield (84%; 52/62), ocular discharges (82%; 51/62), depressed demeanour (81%; 50/62), sunken eye balls (61%; 38/62), fever (60%; 37/62), scleral congestion (56%; 35/62) and intermittent fever (42%; 26/62). Cardiovascular clinical findings in affected buffaloes included tachycardia (44%; 27/62), cardiac arrhythmia (24%; 15/62), cardiac murmurs (19%; 12/62) and muffled heart sounds (18%; 11/62). In the present study, buffaloes with trypanosomosis exhibited significant reduction in haemoglobin (p = 0.008), packed cell volume (p = 0.004), total erythrocyte count (p = 0.003), mean corpuscular volume (p = 0.042), total leucocyte count (p = 0.048) and absolute neutrophil count (p = 0.012); a significant increase in absolute eosinophil count (p = 0.011) and absolute monocyte count (p = 0.008) compared to the apparently healthy buffaloes. Additionally significant decrease in albumin (p = 0.001), A/G ratio (p = 0.007), calcium (p = 0.008), glucose (p = 0.007), phosphorous (p = 0.048), sodium (p = 0.008), potassium (p = 0.041) and chloride (p = 0.046) were observed in buffaloes with trypanosomosis compared to healthy ones. Buffaloes with trypanosomosis also showed significant increase in globulin (p = 0.004), aspartate aminotransferase (p = 0.008), bilirubin (p = 0.034), blood urea nitrogen (p = 0.071), creatinine (p = 0.029), cholesterol (p = 0.046), lactate dehydrogenase (p = 0.009), gamma-glutamyl transferase (p = 0.004) and creatine kinase-myoglobin binding levels (p = 0.005). Electrocardiography explorations in buffaloes with trypanosomosis revealed sinus tachycardia, low voltage QRS complex, ST segment elevation, wide QRS complex, sinus arrhythmia, sinus bradycardia, wandering pace maker, first degree atrio ventricular block, biphasic T wave and tall T wave. Echocardiography examination unveiled cardiac chamber dilatation, ventricular wall thickening and indications of pericarditis/cardiac tamponade. Necropsy was carried on the dead buffaloes during the study period disclosed severely congested blood vessels on epicardial surface, endocardial haemorrhages, and presence of pericardial fluid. Histopathological examination of the heart revealed hyaline degeneration, haemorrhages in the cardiac muscles and varying degrees of degenerative changes. Additionally, the pericardium displayed increased thickness due to presence of more elastic fibres, fibroblast cells in the myocardium, discontinuity of muscle layers, vascular congestion, perivascular mono nuclear cell infiltration and augmented thickness of the endocardium with fibroblast cell proliferation. The study's conclusion highlights cardiac alterations as secondary complications in buffaloes infected with Trypanosoma evansi. Further investigations are recommended to elucidate therapeutic modifications and refine the treatment paradigm.
PubMed: 38652411
DOI: 10.1007/s11259-024-10381-5 -
Medical & Biological Engineering &... Apr 2024Obtaining accurate cardiac auscultation signals, including basic heart sounds (S1 and S2) and subtle signs of disease, is crucial for improving cardiac diagnoses and...
Obtaining accurate cardiac auscultation signals, including basic heart sounds (S1 and S2) and subtle signs of disease, is crucial for improving cardiac diagnoses and making the most of telehealth. This research paper introduces an innovative approach that utilizes a modified cosine transform (MCT) and a masking strategy based on long short-term memory (LSTM) to effectively distinguish heart sounds and murmurs from background noise and interfering sounds. The MCT is used to capture the repeated pattern of the heart sounds, while the LSTMs are trained to construct masking based on the repeated MCT spectrum. The proposed strategy's performance in maintaining the clinical relevance of heart sounds continues to demonstrate effectiveness, even in environments marked by increased noise and complex disruptions. The present work highlights the clinical significance and reliability of the suggested methodology through in-depth signal visualization and rigorous statistical performance evaluations. In comparative assessments, the proposed approach has demonstrated superior performance compared to recent algorithms, such as LU-Net and PC-DAE. Furthermore, the system's adaptability to various datasets enhances its reliability and practicality. The suggested method is a potential way to improve the accuracy of cardiovascular diagnostics in an era of rapid advancement in medical signal processing. The proposed approach showed an enhancement in the average signal-to-noise ratio (SNR) by 9.6 dB at an input SNR of - 6 dB and by 3.3 dB at an input SNR of 10 dB. The average signal distortion ratio (SDR) achieved across a variety of input SNR values was 8.56 dB.
PubMed: 38627355
DOI: 10.1007/s11517-024-03088-x -
Journal of Cardiothoracic Surgery Apr 2024In this case report, we present the unique and intriguing case of a 57-year-old man who experienced exertional palpitations and shortness of breath for 5 years. He was...
In this case report, we present the unique and intriguing case of a 57-year-old man who experienced exertional palpitations and shortness of breath for 5 years. He was diagnosed with idiopathic heart failure three years ago, leading to diuretic treatment. Physical examination revealed notable left lower extremity swelling, severe varicose veins, and cardiac murmurs. Echocardiography showed significant cardiac enlargement and severe functional mitral and tricuspid valve regurgitation. Computed tomography (CT) imaging uncovered a 10 mm left common iliac arteriovenous fistula, causing abnormal early filling of the inferior vena cava (IVC) and marked IVC dilation. Open surgical repair of the arteriovenous fistula resulted in symptom relief and improved cardiac function. This case underscores the importance of considering unusual causes in heart failure patients and highlights the value of early diagnosis and intervention in complex cardiac-vascular interactions.
Topics: Humans; Male; Middle Aged; Arteriovenous Fistula; Arteriovenous Shunt, Surgical; Echocardiography; Heart Failure; Tricuspid Valve Insufficiency; Vena Cava, Inferior
PubMed: 38594763
DOI: 10.1186/s13019-024-02664-4 -
Scientific Reports Mar 2024Traditionally, heart murmurs are diagnosed through cardiac auscultation, which requires specialized training and experience. The purpose of this study is to predict...
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.
Topics: Humans; Phonocardiography; Algorithms; Signal Processing, Computer-Assisted; Heart Murmurs; Heart Auscultation
PubMed: 38555390
DOI: 10.1038/s41598-024-58274-6 -
PLOS Digital Health Mar 2024Cardiovascular diseases (CVDs) account for a high fatality rate worldwide. Heart murmurs can be detected from phonocardiograms (PCGs) and may indicate CVDs. Still, they...
OBJECTIVE
Cardiovascular diseases (CVDs) account for a high fatality rate worldwide. Heart murmurs can be detected from phonocardiograms (PCGs) and may indicate CVDs. Still, they are often overlooked as their detection and correct clinical interpretation require expert skills. In this work, we aim to predict the presence of murmurs and clinical outcomes from multiple PCG recordings employing an explainable multitask model.
APPROACH
Our approach consists of a two-stage multitask model. In the first stage, we predict the murmur presence in single PCGs using a multiple instance learning (MIL) framework. MIL also allows us to derive sample-wise classifications (i.e. murmur locations) while only needing one annotation per recording ("weak label") during training. In the second stage, we fuse explainable hand-crafted features with features from a pooling-based artificial neural network (PANN) derived from the MIL framework. Finally, we predict the presence of murmurs and the clinical outcome for a single patient based on multiple recordings using a simple feed-forward neural network.
MAIN RESULTS
We show qualitatively and quantitatively that the MIL approach yields useful features and can be used to detect murmurs on multiple time instances and may thus guide a practitioner through PCGs. We analyze the second stage of the model in terms of murmur classification and clinical outcome. We achieved a weighted accuracy of 0.714 and an outcome cost of 13612 when using the PANN model and demographic features on the CirCor dataset (hidden test set of the George B. Moody PhysioNet challenge 2022, team "Heart2Beat", rank 12 / 40).
SIGNIFICANCE
To the best of our knowledge, we are the first to demonstrate the usefulness of MIL in PCG classification. Also, we showcase how the explainability of the model can be analyzed quantitatively, thus avoiding confirmation bias inherent to many post-hoc methods. Finally, our overall results demonstrate the merit of employing MIL combined with handcrafted features for the generation of explainable features as well as for a competitive classification performance.
PubMed: 38502666
DOI: 10.1371/journal.pdig.0000461 -
International Journal of Cardiology.... Apr 2024Insufficient clinicians' auscultation ability delays the diagnosis and treatment of valvular heart disease (VHD); artificial intelligence provides a solution to...
BACKGROUND
Insufficient clinicians' auscultation ability delays the diagnosis and treatment of valvular heart disease (VHD); artificial intelligence provides a solution to compensate for the insufficiency in auscultation ability by distinguishing between heart murmurs and normal heart sounds. However, whether artificial intelligence can automatically diagnose VHD remains unknown. Our objective was to use deep learning to process and compare raw heart sound data to identify patients with VHD requiring intervention.
METHODS
Heart sounds from patients with VHD and healthy controls were collected using an electronic stethoscope. Echocardiographic findings were used as the gold standard for this study. According to the chronological order of enrollment, the early-enrolled samples were used to train the deep learning model, and the late-enrollment samples were used to validate the results.
RESULTS
The final study population comprised 499 patients (354 in the algorithm training group and 145 in the result validation group). The sensitivity, specificity, and accuracy of the deep-learning model for identifying various VHDs ranged from 71.4 to 100.0%, 83.5-100.0%, and 84.1-100.0%, respectively; the best diagnostic performance was observed for mitral stenosis, with a sensitivity of 100.0% (31.0-100.0%), a specificity of 100% (96.7-100.0%), and an accuracy of 100% (97.5-100.0%).
CONCLUSIONS
Based on raw heart sound data, the deep learning model effectively identifies patients with various types of VHD who require intervention and assists in the screening, diagnosis, and follow-up of VHD.
PubMed: 38482387
DOI: 10.1016/j.ijcha.2024.101368 -
PloS One 2024Transthoracic Echocardiography is the first-line, non-invasive, and accessible imaging modality to evaluate heart disease anatomy, physiology, and hemodynamics. We aim...
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.
Topics: Male; Female; Child; Humans; Child, Preschool; Retrospective Studies; Cross-Sectional Studies; Developing Countries; Ethiopia; Hospitals, General; Heart Defects, Congenital; Echocardiography, Doppler; Heart Septal Defects, Ventricular; Echocardiography; Hospitals, University; Heart Murmurs
PubMed: 38466681
DOI: 10.1371/journal.pone.0292694 -
Journal of Veterinary Cardiology : the... Apr 2024Aortocardiac 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,...
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.
Topics: Animals; Rabbits; Male; Sinus of Valsalva; Aortic Rupture; Heart Atria; Rupture, Spontaneous; Fistula; Vascular Fistula; Echocardiography; Heart Diseases; Heart Murmurs
PubMed: 38458041
DOI: 10.1016/j.jvc.2024.02.006 -
Cureus Jan 2024One of the many physical exam skills introduced to medical students during their pre-clerkship education is cardiac auscultation, one purpose of which is to teach the... (Review)
Review
One of the many physical exam skills introduced to medical students during their pre-clerkship education is cardiac auscultation, one purpose of which is to teach the detection and identification of heart murmurs. Cardiac auscultation with a stethoscope has been the standard method of teaching. Another method, point-of-care ultrasound (POCUS), has been recently introduced as another modality by which students learn to detect and identify murmurs. The emerging popularity of POCUS in undergraduate medical curricula has led many institutions to include it in their curricula; however, doing so is challenging. Not only is cost a major factor, but reorganizing curricula to allow sufficient time for POCUS training has proven to be difficult. Additionally, the presence of notable gaps in the literature regarding the efficacy of POCUS for teaching the detection and identification of heart murmur has increased scrutiny of its value. Studies that assessed teaching cardiac auscultation to medical students in their pre-clinical years via stethoscope have used different teaching methods. However, evaluation of these studies identified numerous limitations, one being little long-term retention of cardiac auscultation knowledge. Furthermore, several barriers to integration of POCUS in undergraduate medical education were identified. The purpose of this review is to synthesize the literature comparing the effectiveness of these different tools of a cardiac exam for detection of heart murmurs in undergraduate medical education and identify gaps in literature requiring future exploration.
PubMed: 38410315
DOI: 10.7759/cureus.53013 -
HBNET: A blended ensemble model for the detection of cardiovascular anomalies using phonocardiogram.Technology and Health Care : Official... 2024Cardiac diseases are highly detrimental illnesses, responsible for approximately 32% of global mortality [1]. Early diagnosis and prompt treatment can reduce deaths...
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.
Topics: Humans; Phonocardiography; Child; Heart Sounds; Deep Learning; Neural Networks, Computer; Heart Murmurs; Child, Preschool
PubMed: 38393859
DOI: 10.3233/THC-231290