Asphalt Pavement Potholes Localization and Segmentation using Deep RetinaNet and Conditional Random Fields

The main aspect of maintaining the roads and highways' durability and long life is to detect potholes and restore them. A huge number of accidents occur on the roads and highways due to the pothole. It also causes financial loss to vehicle owners by damaging the wheel and flat tire.  For the strategies of the road management system and ITS (Intelligent Transportation System) service, it is one of the major tasks to quickly and precisely detect the potholes. To solve this problem, we have proposed a deep learning methodology to automatically detect and segment the pothole region within the asphalt pavement images. The detection of the pothole is a challenging task because of the arbitrary shape and complex structure of the pothole. In our proposed methodology, to accurately detect the pothole region, we used RetinaNet that creates the bounding box around the multiple regions. For the segmentation we used Conditional Random Field that segments the detected pothole regions obtained from RetinaNet. There are three steps in our methodology, image preprocessing, Pothole region localization, and Pothole segmentation. Our proposed methodology results show that potholes in the images were correctly localized with the best accuracy of 93.04%. Conditional Random Fields (CRF) also show good results. Full Tex

Asphalt Pavement Potholes Localization and Segmentation using Deep RetinaNet and Conditional Random Fields

The main aspect of maintaining the roads and highways' durability and long life is to detect potholes and restore them. A huge number of accidents occur on the roads and highways due to the pothole. It also causes financial loss to vehicle owners by damaging the wheel and flat tire.  For the strategies of the road management system and ITS (Intelligent Transportation System) service, it is one of the major tasks to quickly and precisely detect the potholes. To solve this problem, we have proposed a deep learning methodology to automatically detect and segment the pothole region within the asphalt pavement images. The detection of the pothole is a challenging task because of the arbitrary shape and complex structure of the pothole. In our proposed methodology, to accurately detect the pothole region, we used RetinaNet that creates the bounding box around the multiple regions. For the segmentation we used Conditional Random Field that segments the detected pothole regions obtained from RetinaNet. There are three steps in our methodology, image preprocessing, Pothole region localization, and Pothole segmentation. Our proposed methodology results show that potholes in the images were correctly localized with the best accuracy of 93.04%. Conditional Random Fields (CRF) also show good results. Full Tex

Comparing the effects of muscle energy technique and mulligan mobilization with movements on pain, range of motion, and disability in adhesive capsulitis

Objective: To compare the effect of muscle energy technique and Mulligan mobilisation with movement on pain, range of motion and disability in patients of adhesive capsulitis. Methods: The single-blind, randomised controlled study was conducted at the Physiotherapy Department of Mayo Hospital, Lahore, Pakistan, from July to December, 2018, and comprised patients of either gender aged 30-70 years with adhesive capsulitis stage 2. The subjects were randomised using the lottery method into Mulligan mobilisation with movement group A, and the muscle energy technique grouo B. Conventional treatment, including hot packs and exercises like pulley rope exercise, wall climbing, and shoulder wheel, were part of both the groups. Each technique was applied five times per set, 2 sets per session 3 days a week for three weeks. Baseline and post-intervention readings were recorded for pain, range of motion and disability Using numeric pain rating scale, goniometer, and shoulder pain and disability index. Data was analysed using SPSS 23. Results: Of the 70 individuals assessed, 64(91.4%) were included; 32(50%) in each of the two groups. The mean age in group A was 49.93±6.69 years, while in group B it was 49.17±8.92 years. Group A showed significantly better result compared to group B (p<0.05). Conclusion: Muscle energy technique and Mulligan mobilisation with movement were both found to be effective, but the latter was significantly better compared to the former. Clinical Trial Number: IRCT20200611047734N2 (https://www.irct.ir/trial/48851) Continuous..

Outcome of angiotensin receptor-neprilysin inhibitor on anxiety and depression in heart failure with reduced ejection fraction vs. heart failure with preserved ejection fraction

Objective: In patients with heart failure (HF), anxiety and depression are commonly observed and confer an adverse outcome. The first-in-class member of angiotensin receptor-neprilysin inhibitor (ARNI), sacubitril/valsartan has been demonstrated to improve functional class and decrease mortality in patients with heart failure with reduced ejection fraction (HFrEF) and reduce the readmission of heart failure with preserved ejection fraction (HFpEF). However, its effects on anxiety and depression levels remain unknown.Methods: Sacubitril/valsartan was started on 764 symptomatic patients with HFrEF and HFpEF who were receiving guideline-directed medical therapy (GDMT) with an angiotensin-converting enzyme inhibitor (ACEI) or angiotensin receptor blocker (ARB). Patients were evaluated using Hamilton’s depression rating scale (HDRS) and the hospital anxiety and depression scale (HADS) for their levels of depression and anxiety before and after treatment at a six-month follow-up.Results: A significant reduction in HADS and HDRS scores was observed in patients with HFrEF (9.7 ± 1.3 to 6.4 ± 0.7, p = 0.032 and 19.2 ± 2.2 to 8.9 ± 1.6, p < 0.001, respectively) compared with HFpEF (p = 0.161 and 0.273, respectively). The six-minute walk test (6-MWT) significantly increased HFrEF from 195 ± 68 to 321 ± 97 (p < 0.001). There was an overall improvement in the functional class of all patients.Conclusion: Patients with HFrEF have the additional advantage of using sacubitril/valsartan in the form of decreased anxiety and depression symptoms in addition to an improvement in functional class. However, patients with HFpEF did not exhibit significant improvement in their psychological scores

Tricuspid insufficiency after cardiac-implantable electronic device placement

Objective Device-related estimates of incidence and significance of tricuspid regurgitation (TR) is mainly based on case reports and small observational studies. We sought to determine whether right-heart device implantation increased the risk of TR in this interventional study. Methods All patients who underwent permanent pacemaker (PPM) or other device implantation were assessed for degree of TR at one year. The data collected was analyzed on IBM SPSS version 26. Descriptive statistics were applied for qualitative variables. Mean and standard deviation were applied for quantitative variables. Regression analysis and paired t-tests were applied for the degree of change and predictors of TR. Results Out of 165 participants, 73.94% were male. The mean age of the participants was 59.86 ± 12.03 years. Dual-chamber pacemaker (DDDR) was the most common device implanted (78.18%) causing significant TR and drop in left ventricular ejection fraction as compared to other devices (p-value < 0.05). The paired t-test for changes in ejection fraction (LVEF) and TR were also significant (p-value < 0.05). A regression model predicted significant TR to depend on baseline LVEF (p-value < 0.05). Conclusion Device-related worsening of TR is related to mechanical mechanisms. It is significantly associated with DDDR pacemakers after a 1-year follow-up