ABSTRACT
Background and Objective: Bronchoscopy is a widely used diagnostic and therapeutic procedure for respiratory disorders such as infections and tumors. However, visualizing the bronchial tubes and lungs can be challenging due to the presence of various objects, such as mucus, blood, and foreign bodies. Accurately identifying the anatomical location of the bronchi can be quite challenging, especially for medical professionals who are new to the field. Deep learning-based object detection algorithms can assist doctors in analyzing images or videos of the bronchial tubes to identify key features such as the epiglottis, vocal cord, and right basal bronchus. This study aims to improve the accuracy of object detection in bronchoscopy images by integrating a YOLO-based algorithm with a CBAM attention mechanism. Methods: The CBAM attention module is implemented in the YOLO-V7 and YOLO-V8 object detection models to improve their object identification and classification capabilities in bronchoscopy images. Various YOLO-based object detection algorithms, such as YOLO-V5, YOLO-V7, and YOLO-V8 are compared on this dataset. Experiments are conducted to evaluate the performance of the proposed method and different algorithms. Results: The proposed method significantly improves the accuracy and reliability of object detection for bronchoscopy images. This approach demonstrates the potential benefits of incorporating an attention mechanism in medical imaging and the benefits of utilizing object detection algorithms in bronchoscopy. In the experiments, the YOLO-V8-based model achieved a mean Average Precision (mAP) of 87.09% on the given dataset with an Intersection over Union (IoU) threshold of 0.5. After incorporating the Convolutional Block Attention Module (CBAM) into the YOLO-V8 architecture, the proposed method achieved a significantly enhanced m A P 0.5 and m A P 0.5 : 0.95 of 88.27% and 55.39%, respectively. Conclusions: Our findings indicate that by incorporating a CBAM attention mechanism with a YOLO-based algorithm, there is a noticeable improvement in object detection performance in bronchoscopy images. This study provides valuable insights into enhancing the performance of attention mechanisms for object detection in medical imaging.
ABSTRACT
This study was dedicated to introducing a new method for predicting the Sauter mean diameter (SMD) buildup in the swirl cup airblast fuel injector. There have been considerable difficulties with predicting SMD mainly because of complicated flow characteristics in a spray. Therefore, the backpropagation (BP) neural network-based machine learning was applied for the prediction of SMD as a function of geometry, condition parameters, and axial distance such as primary swirl number, secondary swirl number, venturi angle, mass flow rate of fuel, and relative air pressure. SMD was measured by a phase Doppler particle analyzer (PDPA). The results show that the prediction accuracy of the trained BP neural network was excellent with a coefficient of determination (R2) score of 0.9599, root mean square error (RMSE) score of 1.4613, and overall relative error within 20%. Through sensitivity analysis, the relative air pressure drop and primary swirl number were the largest and smallest factors affecting the value of SMD, respectively. Finally, the prediction accuracy of the BP neural network model is far greater than the current prediction correlations. Moreover, for the predicting target in the present study, the BP neural network shows the advantages of a simple structure and short running time compared with PSO-BP and GRNN. All these prove that the BP neural network is a novel and effective way to predict the SMD of droplets generated by a swirl cup airblast fuel injector.
ABSTRACT
Background: Surgical masks (SMs) protect medical staff and reduce surgical site infections. Extended SM use may reduce oxygen concentrations in circulation, causing hypoxia, headache, and fatigue. However, no research has examined the effects of wearing SMs on oxygenation and physical discomfort of anesthesiologists. Methods: An electronic questionnaire was established and administered through WeChat, and a cross-sectional survey was conducted to determine SM use duration and related discomfort of operating room medical staff. Then, operating room anesthesiologists were enrolled in a single-arm study. Peripheral blood oxygen saturation (SpO2), heart rate, and respiratory rate were determined at different times before and after SM use. Shortness of breath, dizziness, and headache were subjectively assessed based on the visual analog scale (VAS) scores. Results: In total, 485 operating room medical staff completed the electronic questionnaire; 70.5% of them did not change SMs until after work, and 63.9% wore SMs continuously for more than 4 h. The proportion of anesthesiologists was the highest. After wearing masks for 4 h, the shortness of breath, fatigue, and dizziness/headache rates were 42.1, 34.6, and 30.9%, respectively. Compared with other medical staff, the proportion of subjective discomfort of anesthesiologists increased significantly with prolonged SM use from 1 to 4 h. Thirty-five anesthesiologists completed the study. There was no difference in anesthesiologist SpO2, heart rate, or respiratory rate within 2 h of wearing SMs. After more than 2 h, the variation appears to be statistically rather than clinically significant-SpO2 decreased (98.0 [1.0] vs. 97.0 [1.0], p < 0.05), respiratory rate increased (16.0 [3.0] vs. 17.0 [2.0], p < 0.01), and heart rate remained unchanged. As mask use duration increased, the VAS scores of shortness of breath, dizziness, and headache gradually increased. Conclusion: In healthy anesthesiologists, wearing SMs for more than 2 h can significantly decrease SpO2 and increase respiratory rates without affecting heart rates.
