NeoSSNet: Real-Time Neonatal Chest Sound Separation Using Deep Learning.

Goal: 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. 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. Results: 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. Conclusions: The proposed model could be a suitable preprocessing step for any health monitoring system where only the heart sound or lung sound is desired.

Head-to-Head Comparison Between Respiratory Syncytial Virus and Human Metapneumovirus Bronchiolitis in the Setting of Increased Viral Testing.

We compared the epidemiology, severity and management of hospitalized respiratory syncytial virus (n = 305) and human metapneumovirus (n = 39) bronchiolitis in a setting with high respiratory virus testing (95% of admissions tested). Respiratory syncytial virus-positive infants were younger and tended to require more hydration support and longer hospital stays compared to human metapneumovirus-positive infants. Respiratory support requirements were similar between groups despite significant age differences.

Artificial intelligence for the diagnosis of retinopathy of prematurity: A systematic review of current algorithms.

BACKGROUND/OBJECTIVES: With the increasing survival of premature infants, there is an increased demand to provide adequate retinopathy of prematurity (ROP) services. Wide field retinal imaging (WFDRI) and artificial intelligence (AI) have shown promise in the field of ROP and have the potential to improve the diagnostic performance and reduce the workload for screening ophthalmologists. The aim of this review is to systematically review and provide a summary of the diagnostic characteristics of existing deep learning algorithms. SUBJECT/METHODS: Two authors independently searched the literature, and studies using a deep learning system from retinal imaging were included. Data were extracted, assessed and reported using PRISMA guidelines. RESULTS: Twenty-seven studies were included in this review. Nineteen studies used AI systems to diagnose ROP, classify the staging of ROP, diagnose the presence of pre-plus or plus disease, or assess the quality of retinal images. The included studies reported a sensitivity of 71%-100%, specificity of 74-99% and area under the curve of 91-99% for the primary outcome of the study. AI techniques were comparable to the assessment of ophthalmologists in terms of overall accuracy and sensitivity. Eight studies evaluated vascular severity scores and were able to accurately differentiate severity using an automated classification score. CONCLUSION: Artificial intelligence for ROP diagnosis is a growing field, and many potential utilities have already been identified, including the presence of plus disease, staging of disease and a new automated severity score. AI has a role as an adjunct to clinical assessment; however, there is insufficient evidence to support its use as a sole diagnostic tool currently.

Noisy Neonatal Chest Sound Separation for High-Quality Heart and Lung Sounds.

Stethoscope-recorded chest sounds provide the opportunity for remote cardio-respiratory health monitoring of neonates. However, reliable monitoring requires high-quality heart and lung sounds. This paper presents novel artificial intelligence-based Non-negative Matrix Factorisation (NMF) and Non-negative Matrix Co-Factorisation (NMCF) methods for neonatal chest sound separation. To assess these methods and compare them with existing single-channel separation methods, an artificial mixture dataset was generated comprising heart, lung, and noise sounds. Signal-to-noise ratios were then calculated for these artificial mixtures. These methods were also tested on real-world noisy neonatal chest sounds and assessed based on vital sign estimation error, and a signal quality score of 1-5, developed in our previous works. Overall, both the proposed NMF and NMCF methods outperform the next best existing method by 2.7 dB to 11.6 dB for the artificial dataset, and 0.40 to 1.12 signal quality improvement for the real-world dataset. The median processing time for the sound separation of a 10 s recording was found to be 28.3 s for NMCF and 342 ms for NMF. With the stable and robust performance of our proposed methods, we believe these methods are useful to denoise neonatal heart and lung sounds in the real-world environment.

Prediction of Neonatal Respiratory Distress in Term Babies at Birth from Digital Stethoscope Recorded Chest Sounds.

Neonatal respiratory distress is a common condition that if left untreated, can lead to short- and long-term complications. This paper investigates the usage of digital stethoscope recorded chest sounds taken within 1 min post-delivery, to enable early detection and prediction of neonatal respiratory distress. Fifty-one term newborns were included in this study, 9 of whom developed respiratory distress. For each newborn, 1 min anterior and posterior recordings were taken. These recordings were pre-processed to remove noisy segments and obtain high-quality heart and lung sounds. The random undersampling boosting (RUSBoost) classifier was then trained on a variety of features, such as power and vital sign features extracted from the heart and lung sounds. The RUSBoost algorithm produced specificity, sensitivity, and accuracy results of 85.0%, 66.7% and 81.8%, respectively. Clinical relevance--- This paper investigates the feasibility of digital stethoscope recorded chest sounds for early detection of respiratory distress in term newborn babies, to enable timely treatment and management.

Survey on the use of general practice telehealth services for children during the COVID-19 pandemic.

In 2020, the Australian Government introduced temporary Medicare Benefits Schedule item numbers for GP telehealth consultations to combat the spread of the COVID-19 pandemic. Patient satisfaction has been positive; however, the paediatric cohort has not been sufficiently investigated. We aimed to explore the rates of satisfaction of paediatric patients undergoing telehealth compared with standard consultations, as well as looking at any barriers faced. We developed and distributed an online survey to eligible patients (or their guardian) aged 0-17years who underwent a general practice telehealth consultation between March 2020 and May 2020 at 12 participating medical centres in Perth. We received 68 total responses with 35 deemed complete. The mean (s.d.) age of participants was 8.22 (5.34) years. A total of 88.2% of participants indicated that the level of care provided via telehealth was equal to or better than a standard consultation. A total of 70.6% of patients reported no barriers faced, with the most common barrier being lack of examination (20.6%). This study describes high public satisfaction with telehealth GP consultations for paediatric patients, with a good level of patient outcomes and minimal barriers. There may be benefit to widespread and ongoing use of telehealth consultations for the paediatric population and the extension of the temporary Medicare Benefits Schedule items.

A New Non-Negative Matrix Co-Factorisation Approach for Noisy Neonatal Chest Sound Separation.

Obtaining high quality heart and lung sounds enables clinicians to accurately assess a newborns cardio-respiratory health and provide timely care. However, noisy chest sound recordings are common, hindering timely and accurate assessment. A new Non-negative Matrix Co-Factorisation based approach is proposed to separate noisy chest sound recordings into heart, lung and noise components to address this problem. This method is achieved through training with 20 high quality heart and lung sounds, in parallel with separating the sounds of the noisy recording. The method was tested on 68 10-second noisy recordings containing both heart and lung sounds and compared to the current state of the art Non-negative Matrix Factorisation methods. Results show significant improvements in heart and lung sound quality scores respectively, and improved accuracy of 3.6bpm and 1.2bpm in heart and breathing rate estimation respectively, when compared to existing methods.

Neonatal Heart and Lung Sound Quality Assessment for Robust Heart and Breathing Rate Estimation for Telehealth Applications.

With advances in digital stethoscopes, internet of things, signal processing and machine learning, chest sounds can be easily collected and transmitted to the cloud for remote monitoring and diagnosis. However, low quality of recordings complicates remote monitoring and diagnosis, particularly for neonatal care. This paper proposes a new method to objectively and automatically assess the signal quality to improve the accuracy and reliability of heart rate (HR) and breathing rate (BR) estimation from noisy neonatal chest sounds. A total of 88 10-second long chest sounds were taken from 76 preterm and full-term babies. Six annotators independently assessed the signal quality, number of detectable beats, and breathing periods from these recordings. For quality classification, 187 and 182 features were extracted from heart and lung sounds, respectively. After feature selection, class balancing, and hyperparameter optimization, a dynamic binary classification model was trained. Then HR and BR were automatically estimated from the chest sound and several approaches were compared.The results of subject-wise leave-one-out cross-validation, showed that the model distinguished high and low quality recordings in the test set with 96% specificity, 81% sensitivity and 93% accuracy for heart sounds, and 86% specificity, 69% sensitivity and 82% accuracy for lung sounds. The HR and BR estimated from high quality sounds resulted in significantly less median absolute error (4 bpm and 12 bpm difference, respectively) compared to those from low quality sounds. The methods presented in this work, facilitates automated neonatal chest sound auscultation for future telehealth applications.

Assessment of breath sounds at birth using digital stethoscope technology.

Newborn transition is a phase of complex change involving lung fluid clearance and lung aeration. We aimed to use a digital stethoscope (DS) to assess the change in breath sound characteristics over the first 2 h of life and its relationship to mode of delivery. A commercially available DS was used to record breath sounds of term newborns at 1-min and 2-h post-delivery via normal vaginal delivery (NVD) or elective caesarean section (CS). Sound analysis was conducted, and two comparisons were carried out: change in frequency profiles over 2 h, and effect of delivery mode. There was a significant drop in the frequency profile of breath sounds from 1 min to 2 h with mean (SD) frequency decreasing from 333.74 (35.42) to 302.71 (47.19) Hz, p < 0.001, and proportion of power (SD) in the lowest frequency band increasing from 0.27 (0.11) to 0.37 (0.15), p < 0.001. At 1 min, NVD infants had slightly higher frequency than CS but no difference at 2 h.Conclusion: We were able to use DS technology in the transitioning infant to depict significant changes to breath sound characteristics over the first 2 h of life, reflecting the process of lung aeration.What is Known:• Lung fluid clearance and lung aeration are critical processes that facilitate respiration and mode of delivery can impact this• Digital stethoscopes offer enhanced auscultation and have been used in the paediatric population for the assessment of pulmonary and cardiac soundsWhat is New:• This is the first study to use digital stethoscope technology to assess breath sounds at birth• We describe a change in breath sound characteristics over the first 2 h of life and suggest a predictive utility of this analysis to predict the development of respiratory distress in newborns prior to the onset of symptoms.

Acoustic analysis of neonatal breath sounds using digital stethoscope technology.

BACKGROUND: There is no published literature regarding the use of the digital stethoscope (DS) and computerized breath sound analysis in neonates, despite neonates experiencing a high burden of respiratory disease. We aimed to determine if the DS could be used to study breath sounds of term and preterm neonates without respiratory disease, and detect a difference in acoustic characteristics between them. METHODS: A commercially available DS was used to record breath sounds of term and preterm neonates not receiving respiratory support between 24 and 48 hours after birth. Recordings were extracted, filtered, and computer analysis performed to obtain power spectra and mel frequency cepstral coefficient (MFCC) profiles. RESULTS: Recordings from 26 term and 26 preterm infants were obtained. The preterm cohort had an average gestational age (median and interquartile range) of 32 (31-33) weeks and term 39 (38-39) weeks. Birth weight (mean and SD) was 1767 (411) g for the preterm and 3456 (442) g for the term cohort. Power spectra demonstrated the greatest power in the low-frequency range of 100 to 250 Hz for both groups. There were significant differences (P < .05) in the average power at low (100-250 Hz), medium (250-500 Hz), high (500-1000 Hz), and very high (1000-2000 Hz) frequency bands. MFCC profiles also demonstrated significant differences between groups (P < .05). CONCLUSION: It is feasible to use DS technology to analyze breath sounds in neonates. DS was able to determine significant differences between the acoustic characteristics of term and preterm infants breathing in room air. Further investigation of DS technology for neonatal breath sounds is warranted.

Digital stethoscopes in paediatric medicine.

AIM: To explore, synthesise and discuss currently available digital stethoscopes (DS) and the evidence for their use in paediatric medicine. METHODS: Systematic review and narrative synthesis of digital stethoscope use in paediatrics following searches of OVID Medline, Embase, Scopus, PubMed and Google Scholar databases. RESULTS: Six digital stethoscope makes were identified to have been used in paediatric focused studies so far. A total of 25 studies of DS use in paediatrics were included. We discuss the use of digital stethoscope technology in current paediatric medicine, comment on the technical properties of the available devices, the effectiveness and limitations of this technology, and potential uses in the fields of paediatrics and neonatology, from telemedicine to computer-aided diagnostics. CONCLUSION: Further validation and testing of available DS devices is required. Comparison studies between different types of DS would be useful in identifying strengths and flaws of each DS as well as identifying clinical situations for which each may be most appropriately suited.