Effectiveness of limited airway ultrasound education for medical students: a pilot study

Objective The point-of-care ultrasound of the airway (POCUS-A) is a useful examination method but there are currently no educational programs for medical students regarding it. We designed a POCUS-A training curriculum for medical students to improve three cognitive and psychomotor learning domains: knowledge of POCUS-A, image acquisition, and image interpretation. Methods Two hours of training were provided to 52 medical students in their emergency medicine (EM) rotation. Students were evaluated for cognitive and psychomotor skills before and immediately after the training. The validity measures were established with the help of six specialists and eight EM residents. A survey was administered following the curriculum. Results Cognitive skill significantly improved after the training (38.7±12.4 vs. 91.2±7.7) and there was no significant difference between medical students and EM residents in posttest scores (91.2±7.7 vs. 90.8±4.6). The success rate of overall POCUS-A performance was 95.8%. The students were confident to perform POCUS-A on an actual patient and strongly agreed to incorporate POCUS-A training in their medical school curriculum. Conclusion Cognitive and psychomotor skills of POCUS-A among medical students can be improved via a limited curriculum on EM rotation

Association between decreased ipsilateral renal function and aggressive behavior in renal cell carcinoma

Background To assess prognostic value of pre-operative ipsilateral split renal function (SRF) on disease-free survival (DFS) and its association with aggressive pathological features in renal cell carcinoma (RCC) patients.  Methods We examined patients registered in SNUG-RCC-Nx who underwent partial or radical nephrectomy at Seoul National University Hospital between January 1, 2010 and December 31, 2020. Patients with the following criteria were excluded from the study. 1) non-kidney origin cancer or benign renal tumor, 2) no pre-operative Tc 99 m-DTPA renal scan, 3) single kidney status or previous partial or radical nephrectomy, and 4) bilateral renal mass. Finally, 1,078 patients were included. Results Among 1,078 patients, 899 (83.4%) showed maintained ipsilateral SRF on DTPA renal scan; 179 patients (16.6%) showed decreased SRF. The decreased SRF group showed significantly large tumor size (maintained vs. decreased SRF; 3.31 ± 2.15 vs. 6.85 ± 3.25, p < 0.001), high Fuhrman grade (grade 3–4) (41.7% vs. 55.6%, p < 0.001), and high T stage (T stage 3–4) (9.0% vs. 20.1%, p < 0.001). Pathological invasive features, including invasion of the renal capsule, perirenal fat, renal sinus fat, vein, and collecting duct system, were associated with low SRF of the ipsilateral kidney. Univariate Cox regression analysis identified higher SSIGN (The stage, size, grade, and necrosis) score and decreased ipsilateral SRF as significant risk factors, while multivariate analysis showed SSIGN (5–7) (hazard ratio [HR] 11.9, p < 0.001) and SSIGN (8–10) (HR 69.2, p < 0.001) were significantly associated with shortened DFS, while decreased ipsilateral SRF (HR 1.75, p = 0.065) showed borderline significance. Kaplan–Meier analysis showed that decreased ipsilateral SRF (< 45%) group had shorter DFS than the other group (median DFS: 90.3 months vs. not reached, p < 0.001). Conclusions Among unilateral RCC patients, those with low ipsilateral SRF showed poor prognosis with pathologically invasive features. Our novel approach may facilitate risk stratification in RCC patients, helping formulate a treatment strategy

Imaging-based clusters in current smokers of the COPD cohort associate with clinical characteristics: the SubPopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS)

Background Classification of COPD is usually based on the severity of airflow, which may not sensitively differentiate subpopulations. Using a multiscale imaging-based cluster analysis (MICA), we aim to identify subpopulations for current smokers with COPD. Methods Among the SPIROMICS subjects, we analyzed computed tomography images at total lung capacity (TLC) and residual volume (RV) of 284 current smokers. Functional variables were derived from registration of TLC and RV images, e.g. functional small airways disease (fSAD%). Structural variables were assessed at TLC images, e.g. emphysema and airway wall thickness and diameter. We employed an unsupervised method for clustering. Results Four clusters were identified. Cluster 1 had relatively normal airway structures; Cluster 2 had an increase of fSAD% and wall thickness; Cluster 3 exhibited a further increase of fSAD% but a decrease of wall thickness and airway diameter; Cluster 4 had a significant increase of fSAD% and emphysema. Clinically, Cluster 1 showed normal FEV1/FVC and low exacerbations. Cluster 4 showed relatively low FEV1/FVC and high exacerbations. While Cluster 2 and Cluster 3 showed similar exacerbations, Cluster 2 had the highest BMI among all clusters. Conclusions Association of imaging-based clusters with existing clinical metrics suggests the sensitivity of MICA in differentiating subpopulations

Imaging-based clusters in former smokers of the COPD cohort associate with clinical characteristics: the SubPopulations and intermediate outcome measures in COPD study (SPIROMICS)

Background: Quantitative computed tomographic (QCT) imaging-based metrics enable to quantify smoking induced disease alterations and to identify imaging-based clusters for current smokers. We aimed to derive clinically meaningful sub-groups of former smokers using dimensional reduction and clustering methods to develop a new way of COPD phenotyping. Methods: An imaging-based cluster analysis was performed for 406 former smokers with a comprehensive set of imaging metrics including 75 imaging-based metrics. They consisted of structural and functional variables at 10 segmental and 5 lobar locations. The structural variables included lung shape, branching angle, airway-circularity, airway-wall-thickness, airway diameter; the functional variables included regional ventilation, emphysema percentage, functional small airway disease percentage, Jacobian (volume change), anisotropic deformation index (directional preference in volume change), and tissue fractions at inspiration and expiration. Results: We derived four distinct imaging-based clusters as possible phenotypes with the sizes of 100, 80, 141, and 85, respectively. Cluster 1 subjects were asymptomatic and showed relatively normal airway structure and lung function except airway wall thickening and moderate emphysema. Cluster 2 subjects populated with obese females showed an increase of tissue fraction at inspiration, minimal emphysema, and the lowest progression rate of emphysema. Cluster 3 subjects populated with older males showed small airway narrowing and a decreased tissue fraction at expiration, both indicating air-trapping. Cluster 4 subjects populated with lean males were likely to be severe COPD subjects showing the highest progression rate of emphysema. Conclusions: QCT imaging-based metrics for former smokers allow for the derivation of statistically stable clusters associated with unique clinical characteristics. This approach helps better categorization of COPD sub-populations; suggesting possible quantitative structural and functional phenotypes.NIH [U01-HL114494, R01-HL112986, S10-RR022421]; Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education [NRF-2017R1D1A1B03034157]; Korea Ministry of Environment (MOE) [RE201806039]; NIH/NHLBI [HHSN268200900013C, HHSN268200900014C, HHSN268200900015C, HHSN268200900016C, HHSN268200900017C, HHSN268200900018C, HHSN268200900019C, HHSN268200900020C]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

DLMFCOS: Efficient Dual-Path Lightweight Module for Fully Convolutional Object Detection

Recent advances in convolutional neural network (CNN)-based object detection have a trade-off between accuracy and computational cost in various industrial tasks and essential consideration. However, the fully convolutional one-stage detector (FCOS) demonstrates low accuracy compared with its computational costs owing to the loss of low-level information. Therefore, we propose a module called a dual-path lightweight module (DLM) that efficiently utilizes low-level information. In addition, we propose a DLMFCOS based on DLM to achieve an optimal trade-off between computational cost and detection accuracy. Our network minimizes feature loss by extracting spatial and channel information in parallel and implementing a bottom-up feature pyramid network that improves low-level information detection. Additionally, the structure of the detection head is improved to minimize the computational cost. The proposed method was trained and evaluated by fine-tuning parameters through experiments and using public datasets PASCAL VOC 07 and MS COCO 2017 datasets. The average precision (AP) metric is used for our quantitative evaluation matrix for detection performance, and our model achieves an average 1.5% accuracy improvement at about 33.85% lower computational cost on each dataset than the conventional method. Finally, the efficiency of the proposed method is verified by comparing the proposed method with the conventional method through an ablation study

LNFCOS: Efficient Object Detection through Deep Learning Based on LNblock

In recent deep-learning-based real-time object detection methods, the trade-off between accuracy and computational cost is an important consideration. Therefore, based on the fully convolutional one-stage detector (FCOS), which is a one-stage object detection method, we propose a light next FCOS (LNFCOS) that achieves an optimal trade-off between computational cost and accuracy. In LNFCOS, the loss of low- and high-level information is minimized by combining the features of different scales through the proposed feature fusion module. Moreover, the light next block (LNblock) is proposed for efficient feature extraction. LNblock performs feature extraction with a low computational cost compared with standard convolutions, through sequential operation on a small amount of spatial and channel information. To define the optimal parameters of LNFCOS suggested through experiments and for a fair comparison, experiments and evaluations were conducted on the publicly available benchmark datasets MSCOCO and PASCAL VOC. Additionally, the average precision (AP) was used as an evaluation index for quantitative evaluation. LNFCOS achieved an optimal trade-off between computational cost and accuracy by achieving a detection accuracy of 79.3 AP and 37.2 AP on the MS COCO and PASCAL VOC datasets, respectively, with 36% lower computational cost than the FCOS

LNFCOS: Efficient Object Detection through Deep Learning Based on LNblock

In recent deep-learning-based real-time object detection methods, the trade-off between accuracy and computational cost is an important consideration. Therefore, based on the fully convolutional one-stage detector (FCOS), which is a one-stage object detection method, we propose a light next FCOS (LNFCOS) that achieves an optimal trade-off between computational cost and accuracy. In LNFCOS, the loss of low- and high-level information is minimized by combining the features of different scales through the proposed feature fusion module. Moreover, the light next block (LNblock) is proposed for efficient feature extraction. LNblock performs feature extraction with a low computational cost compared with standard convolutions, through sequential operation on a small amount of spatial and channel information. To define the optimal parameters of LNFCOS suggested through experiments and for a fair comparison, experiments and evaluations were conducted on the publicly available benchmark datasets MSCOCO and PASCAL VOC. Additionally, the average precision (AP) was used as an evaluation index for quantitative evaluation. LNFCOS achieved an optimal trade-off between computational cost and accuracy by achieving a detection accuracy of 79.3 AP and 37.2 AP on the MS COCO and PASCAL VOC datasets, respectively, with 36% lower computational cost than the FCOS

EAR-Net: Efficient Atrous Residual Network for Semantic Segmentation of Street Scenes Based on Deep Learning

Segmentation of street scenes is a key technology in the field of autonomous vehicles. However, conventional segmentation methods achieve low accuracy because of the complexity of street landscapes. Therefore, we propose an efficient atrous residual network (EAR-Net) to improve accuracy while maintaining computation costs. First, we performed feature extraction and restoration, utilizing depthwise separable convolution (DSConv) and interpolation. Compared with conventional methods, DSConv and interpolation significantly reduce computation costs while minimizing performance degradation. Second, we utilized residual learning and atrous spatial pyramid pooling (ASPP) to achieve high accuracy. Residual learning increases the ability to extract context information by preventing the problem of feature and gradient losses. In addition, ASPP extracts additional context information while maintaining the resolution of the feature map. Finally, to alleviate the class imbalance between the image background and objects and to improve learning efficiency, we utilized focal loss. We evaluated EAR-Net on the Cityscapes dataset, which is commonly used for street scene segmentation studies. Experimental results showed that the EAR-Net had better segmentation results and similar computation costs as the conventional methods. We also conducted an ablation study to analyze the contributions of the ASPP and DSConv in the EAR-Net