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JMIR Medical Informatics Jun 2024The pursuit of groundbreaking health care innovations has led to the convergence of artificial intelligence (AI) and traditional Chinese medicine (TCM), thus marking a...
The pursuit of groundbreaking health care innovations has led to the convergence of artificial intelligence (AI) and traditional Chinese medicine (TCM), thus marking a new frontier that demonstrates the promise of combining the advantages of ancient healing practices with cutting-edge advancements in modern technology. TCM, which is a holistic medical system with >2000 years of empirical support, uses unique diagnostic methods such as inspection, auscultation and olfaction, inquiry, and palpation. AI is the simulation of human intelligence processes by machines, especially via computer systems. TCM is experience oriented, holistic, and subjective, and its combination with AI has beneficial effects, which presumably arises from the perspectives of diagnostic accuracy, treatment efficacy, and prognostic veracity. The role of AI in TCM is highlighted by its use in diagnostics, with machine learning enhancing the precision of treatment through complex pattern recognition. This is exemplified by the greater accuracy of TCM syndrome differentiation via tongue images that are analyzed by AI. However, integrating AI into TCM also presents multifaceted challenges, such as data quality and ethical issues; thus, a unified strategy, such as the use of standardized data sets, is required to improve AI understanding and application of TCM principles. The evolution of TCM through the integration of AI is a key factor for elucidating new horizons in health care. As research continues to evolve, it is imperative that technologists and TCM practitioners collaborate to drive innovative solutions that push the boundaries of medical science and honor the profound legacy of TCM. We can chart a future course wherein AI-augmented TCM practices contribute to more systematic, effective, and accessible health care systems for all individuals.
PubMed: 38941141
DOI: 10.2196/58491 -
Pediatric Cardiology Jun 2024Research has shown that X-rays and fundus images can classify gender, age group, and race, raising concerns about bias and fairness in medical AI applications. However,...
Research has shown that X-rays and fundus images can classify gender, age group, and race, raising concerns about bias and fairness in medical AI applications. However, the potential for physiological sounds to classify sociodemographic traits has not been investigated. Exploring this gap is crucial for understanding the implications and ensuring fairness in the field of medical sound analysis. We aimed to develop classifiers to determine gender (men/women) based on heart sound recordings and using machine learning (ML). Data-driven ML analysis. We utilized the open-access CirCor DigiScope Phonocardiogram Dataset obtained from cardiac screening programs in Brazil. Volunteers < 21 years of age. Each participant completed a questionnaire and underwent a clinical examination, including electronic auscultation at four cardiac points: aortic (AV), mitral (MV), pulmonary (PV), and tricuspid (TV). We used Mel-frequency cepstral coefficients (MFCCs) to develop the ML classifiers. From each patient and from each auscultation sound recording, we extracted 10 MFCCs. In sensitivity analysis, we additionally extracted 20, 30, 40, and 50 MFCCs. The most effective gender classifier was developed using PV recordings (AUC ROC = 70.3%). The second best came from MV recordings (AUC ROC = 58.8%). AV and TV recordings produced classifiers with an AUC ROC of 56.4% and 56.1%, respectively. Using more MFCCs did not substantially improve the classifiers. It is possible to classify between males and females using phonocardiogram data. As health-related audio recordings become more prominent in ML applications, research is required to explore if these recordings contain signals that could distinguish sociodemographic features.
PubMed: 38937337
DOI: 10.1007/s00246-024-03561-2 -
Animals : An Open Access Journal From... Jun 2024Heart murmurs in puppies can be innocent or pathologic; the latter is almost always related to a congenital heart disease. Differentiating between these murmurs can be...
BACKGROUND
Heart murmurs in puppies can be innocent or pathologic; the latter is almost always related to a congenital heart disease. Differentiating between these murmurs can be challenging for practicing veterinarians, but this differentiation is essential to ensure the best prognosis for puppies having a congenital heart disease. Our study aimed to reveal how veterinarians manage puppies with a heart murmur.
METHODS
A web-based questionnaire was sent to Dutch and Belgian veterinary practices.
RESULTS
Data from 452 respondents were analyzed. Though 88% of the respondents find detecting a heart murmur easy, only 9% find differentiating innocent murmurs from pathologic murmurs in puppies easy. Of the respondents, only 80% recommend immediate additional examination when detecting a loud heart murmur during the first veterinary health check at 6 weeks of age. Most of the respondents are aware that normal growth and the absence of clinical signs do not exclude severe congenital heart disease. Of the respondents, 31% were uncertain whether early surgical intervention could lead to improved outcomes.
CONCLUSIONS
Veterinarians are aware of the importance of echocardiography for puppies with a loud heart murmur, and recognize their limitations when differentiating an innocent from a pathological heart murmur in a puppy.
PubMed: 38929440
DOI: 10.3390/ani14121821 -
Bioengineering (Basel, Switzerland) Jun 2024Respiratory diseases are among the leading causes of death, with many individuals in a population frequently affected by various types of pulmonary disorders. Early...
Respiratory diseases are among the leading causes of death, with many individuals in a population frequently affected by various types of pulmonary disorders. Early diagnosis and patient monitoring (traditionally involving lung auscultation) are essential for the effective management of respiratory diseases. However, the interpretation of lung sounds is a subjective and labor-intensive process that demands considerable medical expertise, and there is a good chance of misclassification. To address this problem, we propose a hybrid deep learning technique that incorporates signal processing techniques. Parallel transformation is applied to adventitious respiratory sounds, transforming lung sound signals into two distinct time-frequency scalograms: the continuous wavelet transform and the mel spectrogram. Furthermore, parallel convolutional autoencoders are employed to extract features from scalograms, and the resulting latent space features are fused into a hybrid feature pool. Finally, leveraging a long short-term memory model, a feature from the latent space is used as input for classifying various types of respiratory diseases. Our work is evaluated using the ICBHI-2017 lung sound dataset. The experimental findings indicate that our proposed method achieves promising predictive performance, with average values for accuracy, sensitivity, specificity, and F1-score of 94.16%, 89.56%, 99.10%, and 89.56%, respectively, for eight-class respiratory diseases; 79.61%, 78.55%, 92.49%, and 78.67%, respectively, for four-class diseases; and 85.61%, 83.44%, 83.44%, and 84.21%, respectively, for binary-class (normal vs. abnormal) lung sounds.
PubMed: 38927822
DOI: 10.3390/bioengineering11060586 -
JMIR Cardio Jun 2024Heart failure (HF) contributes greatly to morbidity, mortality, and health care costs worldwide. Hospital readmission rates are tracked closely and determine federal...
BACKGROUND
Heart failure (HF) contributes greatly to morbidity, mortality, and health care costs worldwide. Hospital readmission rates are tracked closely and determine federal reimbursement dollars. No current modality or technology allows for accurate measurement of relevant HF parameters in ambulatory, rural, or underserved settings. This limits the use of telehealth to diagnose or monitor HF in ambulatory patients.
OBJECTIVE
This study describes a novel HF diagnostic technology using audio recordings from a standard mobile phone.
METHODS
This prospective study of acoustic microphone recordings enrolled convenience samples of patients from 2 different clinical sites in 2 separate areas of the United States. Recordings were obtained at the aortic (second intercostal) site with the patient sitting upright. The team used recordings to create predictive algorithms using physics-based (not neural networks) models. The analysis matched mobile phone acoustic data to ejection fraction (EF) and stroke volume (SV) as evaluated by echocardiograms. Using the physics-based approach to determine features eliminates the need for neural networks and overfitting strategies entirely, potentially offering advantages in data efficiency, model stability, regulatory visibility, and physical insightfulness.
RESULTS
Recordings were obtained from 113 participants. No recordings were excluded due to background noise or for any other reason. Participants had diverse racial backgrounds and body surface areas. Reliable echocardiogram data were available for EF from 113 patients and for SV from 65 patients. The mean age of the EF cohort was 66.3 (SD 13.3) years, with female patients comprising 38.3% (43/113) of the group. Using an EF cutoff of ≤40% versus >40%, the model (using 4 features) had an area under the receiver operating curve (AUROC) of 0.955, sensitivity of 0.952, specificity of 0.958, and accuracy of 0.956. The mean age of the SV cohort was 65.5 (SD 12.7) years, with female patients comprising 34% (38/65) of the group. Using a clinically relevant SV cutoff of <50 mL versus >50 mL, the model (using 3 features) had an AUROC of 0.922, sensitivity of 1.000, specificity of 0.844, and accuracy of 0.923. Acoustics frequencies associated with SV were observed to be higher than those associated with EF and, therefore, were less likely to pass through the tissue without distortion.
CONCLUSIONS
This work describes the use of mobile phone auscultation recordings obtained with unaltered cellular microphones. The analysis reproduced the estimates of EF and SV with impressive accuracy. This technology will be further developed into a mobile app that could bring screening and monitoring of HF to several clinical settings, such as home or telehealth, rural, remote, and underserved areas across the globe. This would bring high-quality diagnostic methods to patients with HF using equipment they already own and in situations where no other diagnostic and monitoring options exist.
PubMed: 38924781
DOI: 10.2196/57111 -
Journal of Cardiovascular Development... May 2024The congenital Gerbode defect is defined as an abnormal communication between the left ventricle and the right atrium. This review aimed to summarize existing evidence,... (Review)
Review
The congenital Gerbode defect is defined as an abnormal communication between the left ventricle and the right atrium. This review aimed to summarize existing evidence, shed light on the clinical implications, and identify knowledge gaps. The systematic literature search was conducted in the PubMed and Google Scholar medical databases using specifically selected keywords. The inclusion of each publication was assessed according to predefined eligibility criteria based on the PICOM (Population, Phenomenon of Interest, Context, Methodology) schema. Titles and abstracts were screened independently by two authors. Available full-text versions of included publications were reviewed and relevant information was extracted. A total of 78 reports were included. The compilation of all congenital Gerbode defect cases described in the literature revealed a variety of clinical presentations comprising dyspnea, palpitations, growth retardation, and asymptomatology. A suitable multimodal diagnostic approach for newborns consists of auscultation, TTE, and optionally TEE and MRI. Because of its rarity, diversity of findings, unknown pathophysiology, and similarity to more common cardiac diseases, the diagnostic challenge remains significant. To prevent untreated long-term sequelae, early individualized treatment is recommended. Surgical defect closure is preferred to device closure for evidence reasons, although major developments are currently taking place. In conclusion, the congenital Gerbode defect provides a diagnostic challenge for pediatricians to allow early diagnosis and intervention in order to improve patients' quality of life.
PubMed: 38921666
DOI: 10.3390/jcdd11060166 -
Resuscitation Plus Sep 2024To examine speed and accuracy of newborn heart rate measurement by various assessment methods employed at birth. (Review)
Review
AIM
To examine speed and accuracy of newborn heart rate measurement by various assessment methods employed at birth.
METHODS
A search of Medline, SCOPUS, CINAHL and Cochrane was conducted between January 1, 1946, to until August 16, 2023. (CRD 42021283364) Study selection was based on predetermined criteria. Reviewers independently extracted data, appraised risk of bias and assessed certainty of evidence.
RESULTS
Pulse oximetry is slower and less precise than ECG for heart rate assessment. Both auscultation and palpation are imprecise for heart rate assessment. Other devices such as digital stethoscope, Doppler ultrasound, an ECG device using dry electrodes incorporated in a belt, photoplethysmography and electromyography are studied in small numbers of newborns and data are not available for extremely preterm or bradycardic newborns receiving resuscitation. Digital stethoscope is fast and accurate. Doppler ultrasound and dry electrode ECG in a belt are fast, accurate and precise when compared to conventional ECG with gel adhesive electrodes.
LIMITATIONS
Certainty of evidence was low or very low for most comparisons.
CONCLUSION
If resources permit, ECG should be used for fast and accurate heart rate assessment at birth. Pulse oximetry and auscultation may be reasonable alternatives but have limitations. Digital stethoscope, doppler ultrasound and dry electrode ECG show promise but need further study.
PubMed: 38912532
DOI: 10.1016/j.resplu.2024.100668 -
Medicina 2024Takotsubo syndrome, was described in Japan in 1990, it is a stress cardiomyopathy, predominantly in women, usually postmenopausal. Cardiac hypokinesia occurs, with...
Takotsubo syndrome, was described in Japan in 1990, it is a stress cardiomyopathy, predominantly in women, usually postmenopausal. Cardiac hypokinesia occurs, with involvement of multiple coronary territories. In intensive care unit (ICU), it is considered underdiagnosed. Manifestations of severe dengue fever include cardiovascular involvement, mainly arrhythmias and systolic dysfunction. A case of a 72-year-old man is presented, who was hospitalized in ICU for dengue fever, with plateletopenia (15000 cells/mm3) and dehydration. After fluid management the patient reported respiratory discomfort, auscultating crackling rales. A pulmonary ultrasound was made where bilateral B lines were found with B7 pattern compatible with interstitial syndrome and pulmonary edema. Basal hyperkinesia, medial and apical hypokinesia with an image consistent with apical ballooning were observed in the transthoracic echocardiogram. The electrocardiogram showed complete right bundle branch block. Chagas serology was negative and quantitative troponin I was increased. In the context of severe dengue, a Takotsubo syndrome was diagnosed. The patient evolved favorably. After discharge, a normalization of the cardiac function was stated in ultrasound images. The case is of clinical importance due to the low association of these two diseases and the need to screen for cardiac involvement in severe dengue.
Topics: Humans; Takotsubo Cardiomyopathy; Aged; Male; Dengue; Electrocardiography; Severe Dengue; Echocardiography
PubMed: 38907979
DOI: No ID Found -
American Family Physician Jun 2024Pregnancy dating is determined by the patient's last menstrual period or an ultrasound measurement. A full-term pregnancy is considered 37 weeks' gestation or more.... (Review)
Review
Pregnancy dating is determined by the patient's last menstrual period or an ultrasound measurement. A full-term pregnancy is considered 37 weeks' gestation or more. Spontaneous labor begins when regular painful uterine contractions result in a cervical change. Active labor begins at 6 cm dilation and is marked by more predictable, accelerated cervical change. In the absence of pregnancy complications, intermittent fetal auscultation may be considered as an alternative to continuous electronic fetal monitoring, which is associated with a high false-positive rate. Intravenous antibiotic prophylaxis is indicated in patients with group B streptococcus colonization or those at high risk to prevent newborn early-onset group B streptococcus. The likelihood of vaginal delivery is increased by providing continuous nonmedical support during labor, encouraging mobility, and using a peanut ball with epidural analgesia. Neuraxial analgesia is more effective for pain control than systemic opioids and is associated with fewer adverse effects. Delayed pushing during the second stage of labor has risks but does not affect the mode of delivery. Routine oropharyngeal suctioning of the newborn is not recommended, even with meconium-stained amniotic fluid. Delayed cord clamping reduces newborn anemia. Prevention of postpartum hemorrhage in patients at risk includes prophylactic uterotonic administration and controlled cord traction. Perineal lacerations that alter anatomy or are not hemostatic should be repaired. (Am Fam Physician. 2024;109(6):525-532.
Topics: Humans; Female; Pregnancy; Delivery, Obstetric; Infant, Newborn; Labor, Obstetric
PubMed: 38905550
DOI: No ID Found -
IEEE Open Journal of Engineering in... 2024Auscultation for neonates is a simple and non-invasive method of diagnosing cardiovascular and respiratory disease. However, obtaining high-quality chest sounds...
Auscultation for neonates is a simple and non-invasive method of diagnosing cardiovascular and respiratory disease. However, obtaining high-quality chest sounds containing only heart or lung sounds is non-trivial. Hence, this study introduces a new deep-learning model named NeoSSNet and evaluates its performance in neonatal chest sound separation with previous methods. We propose a masked-based architecture similar to Conv-TasNet. The encoder and decoder consist of 1D convolution and 1D transposed convolution, while the mask generator consists of a convolution and transformer architecture. The input chest sounds were first encoded as a sequence of tokens using 1D convolution. The tokens were then passed to the mask generator to generate two masks, one for heart sounds and one for lung sounds. Each mask is then applied to the input token sequence. Lastly, the tokens are converted back to waveforms using 1D transposed convolution. Our proposed model showed superior results compared to the previous methods based on objective distortion measures, ranging from a 2.01 dB improvement to a 5.06 dB improvement. The proposed model is also significantly faster than the previous methods, with at least a 17-time improvement. The proposed model could be a suitable preprocessing step for any health monitoring system where only the heart sound or lung sound is desired.
PubMed: 38899018
DOI: 10.1109/OJEMB.2024.3401571