Objective . Auscultation of lung sound plays an important role in the early diagnosis of lung diseases. This work aims to develop an automated adventitious lung sound detection method to reduce the workload of physicians. Approach . We propose a deep learning architecture, LungAttn, which incorporates augmented attention convolution into ResNet block to improve the classification accuracy of lung sound. We adopt a feature extraction method based on dual tunable Q -factor wavelet transform and triple short-time Fourier transform to obtain a multi-channel spectrogram. Mixup method is introduced to augment adventitious lung sound recordings to address the imbalance dataset problem. Main results . Based on the ICBHI 2017 challenge dataset, we implement our framework and compare with the state-of-the-art works. Experimental results show that LungAttn has achieved the Sensitivity, S _e , Specificity, S _p and Score of 36.36%, 71.44% and 53.90%, respectively. Of which, our work has improved the Score by 1.69% compared to the state-of-the-art models based on the official ICBHI 2017 dataset splitting method. Significance . Multi-channel spectrogram based on different oscillatory behavior of adventitious lung sound provides necessary information of lung sound recordings. Attention mechanism is introduced to lung sound classification methods and has proved to be effective. The proposed LungAttn model can potentially improve the speed and accuracy of lung sound classification in clinical practice.

The unprecedented rapid growth of digital health has brought new opportunities to the health field. However, elderly patients with chronic diseases, as an important potential beneficiary group, are affected by the digital divide, leading to unsatisfactory usage of digital health technologies (DHTs). Our study focused on the factors influencing the adoption of DHTs among this vulnerable group. To extend the UTAUT theory, technology anxiety and several demographic predictors were included to address the age characteristics of the respondents. An on-site survey was conducted in general, district, and community hospitals in Shanghai (n = 309). Facilitating conditions negatively influenced technology anxiety. Technology anxiety hindered behavioural intention. Social influence had a significant but negative impact on behavioural intention. Education, whether older adults have had experience with DHTs and previous smartphone usage experiences were significantly associated with technology anxiety. The findings provide valuable information for multiple stakeholders, including family members of elderly users, product designers, and policymakers. Ameliorating facilitating conditions, improving devices’ usage experience, encouraging attempts and focusing on groups with lower educational levels can help to reduce technology anxiety and promote DHT acceptance and use in older age groups.

Introduction: Complicated outpatient procedures are associated with excessive paperwork and long waiting times. We aimed to shorten queuing times and improve visiting satisfaction.Methods: We developed an artificial intelligence (AI)-assisted program named Smart-doctor. A randomized controlled trial was conducted at Shanghai Children’s Medical Center. Participants were randomly divided into an AI-assisted and conventional group. Smart-doctor was used as a medical assistant in the AI-assisted group. At the end of the visit, an e-medical satisfaction questionnaire was asked to be done. The primary outcome was the queuing time, while secondary outcomes included the consulting time, test time, total time, and satisfaction score. Wilcoxon rank sum test, multiple linear regression and ordinal regression were also used.Results: We enrolled 740 eligible patients (114 withdrew, response rate: 84.59%). The median queuing time was 8.78 (interquartile range [IQR] 3.97,33.88) minutes for the AI-assisted group versus 21.81 (IQR 6.66,73.10) minutes for the conventional group (p < 0.01), and the AI-assisted group had a shorter consulting time (0.35 [IQR 0.18, 0.99] vs. 2.68 [IQR 1.82, 3.80] minutes, p < 0.01), and total time (40.20 [IQR 26.40, 73.80] vs. 110.40 [IQR 68.40, 164.40] minutes, p < 0.01). The overall satisfaction score was increased by 17.53% (p < 0.01) in the AI-assisted group. In addition, multiple linear regression and ordinal regression showed that the queuing time and satisfaction were mainly affected by group (p < 0.01), and missing the turn (p < 0.01).Conclusions: Using AI to simplify the outpatient service procedure can shorten the queuing time of patients and improve visit satisfaction.

Secundum atrial septal defect (ASD) is one of the most common congenital heart diseases (CHDs). This study aims to evaluate the feasibility and accuracy of automatic detection of ASD in children based on color Doppler echocardiographic images using convolutional neural networks. In this study, we propose a fully automatic detection system for ASD, which includes three stages. The first stage is used to identify four target echocardiographic views (that is, the subcostal view focusing on the atrium septum, the apical four-chamber view, the low parasternal four-chamber view, and the parasternal short-axis view). These four echocardiographic views are most useful for the diagnosis of ASD clinically. The second stage aims to segment the target cardiac structure and detect candidates for ASD. The third stage is to infer the final detection by utilizing the segmentation and detection results of the second stage. The proposed ASD detection system was developed and validated using a training set of 4,031 cases containing 370,057 echocardiographic images and an independent test set of 229 cases containing 203,619 images, of which 105 cases with ASD and 124 cases with intact atrial septum. Experimental results showed that the proposed ASD detection system achieved accuracy, recall, precision, specificity, and F1 score of 0.8833, 0.8545, 0.8577, 0.9136, and 0.8546, respectively on the image-level averages of the four most clinically useful echocardiographic views. The proposed system can automatically and accurately identify ASD, laying a good foundation for the subsequent artificial intelligence diagnosis of CHDs.

This study aimed to evaluate the relationship between the daily dietary intake of riboflavin (DDIR) and impaired lung function associated with dibutyl phthalate (DBP) exposure. Data of 4631 adults in this national cross-sectional survey were included. Urinary mono-benzyl phthalate (MBP) was used to evaluate the level of DBP exposure. The ln-transformed urinary creatinine-corrected MBP (ln(MBP/UCr)) level was used in the statistical models. High DDIR was defined as the DDIR ≥1.8 mg per day. The results of lung function impairment and high monocytes were significantly higher in the highest MBP group compared with the lowest MBP group. A significant interaction between ln(MBP/UCr) and DDIR (P_interaction = 0.029) was detected for the risk of lung function impairment. The risk of lung function impairment (OR_{quartiles4 vs. 1} 1.85, 95% CI, 1.27–2.71; P_trend = 0.018) and high neutrophils (OR_{quartiles4 vs. 1} 1.45, 95% CI, 1.06–1.97; P_trend = 0.018) was significantly higher in the highest vs. the lowest quartile of MBP in participants with low/normal DDIR but not in in participants with high DDIR. The results of this study showed that high DDIR was associated with less lung function impairment related with DBP exposure, and the inhibiting of the neutrophil recruitment might be the potential mechanism.