Building BPJS Image, Public Trust and Intention to Use JKN Mobile Services During the Covid Pandemic

Purpose: to analyze the ability of the National Health Insurance mobile service quality to build BPJS brand image and public trust to increase intention to use online services during the Covid period. The background of this research is based on the phenomenon in the form of complaints on the quality of online services and research gaps on the effect of service quality on the intention to use online services. Brand image and trust are offered as a mediation for gaps in previous research results. Design/ methodology/approach: The type of research is quantitative, using a pre-existing measurement scale related to mobile service quality, brand image, trust and intention. Involving a sample of 140 BPJS users during the Covid pandemic. It is difficult to identify the population size, the sample size is determined by the formulation of a constant value of 5 multiplied by 28 indicators. The technique of selecting respondents was carried out by means of non-probability random sampling. PLS SEM model as an analysis tool. Findings: The results of this study indicate that the direct relationship of mobile service quality on brand image, trust and intention shows significant positive results. Furthermore, the influence of brand image on trust shows significant results. The influence of brand image and trust on intention is also found to be significantly positive. Practical/implications: although management policies encourage customers to use mobile services more, the public still considers the trustworthy image of BPJS to develop their intention to use mobile application services. The government must remain consistent in ensuring that the quality of mobile service is not compromised because the implications for BPJS image and public trust are at stake. Through the person in charge at BPJS, the government must continue to consistently evaluate and improve the system and educate the public regarding this BPJS health mobile service system. Originality/value: This research offers new insights, filling gaps in studies on national health insurance mobile services during the Covid-19 Pandemic

Implementation of Image Quality Improvement Methods and Lung Segmentation on Chest X-Ray Images Using U-Net Architectural Modifications

COVID-19 is an infectious disease that causes acute respiratory distress syndrome due to the SARS-CoV-2 virus. Rapid and accurate screening and early diagnosis of patients play an essential role in controlling outbreaks and reducing the spread of this disease. This disease can be diagnosed by manually reading CXR images, but it is time-consuming and prone to errors. For this reason, this research proposes an automatic medical image segmentation system using a combination of U-Net architecture with Batch Normalization to obtain more accurate and fast results. The method used in this study consists of pre-processing using the CLAHE method and morphology opening, CXR image segmentation using a combination of U-Net-4 Convolution Block architecture with Batch Normalization, then evaluated using performance measures such as accuracy, sensitivity, specificity, F1-score, and IoU. The results showed that the U-Net architecture modified with Batch Normalization had successfully segmented CXR images, as seen from all performance measurement values above 94%.

lungEcho - Resource Constrained Lung Ultrasound Video Analysis Tool for Faster Triaging and Active Learning

Lung ultrasound (LUS) is possibly the only medical imaging modality which could be used for continuous and periodic monitoring of the lung. This is extremely useful in tracking the lung manifestations either during the onset of lung infection or to track the effect of vaccination on lung as in pandemics such as COVID-19. There have been many attempts in automating the classification of the severity of lung involvement into various classes or automatic segmentation of various LUS landmarks and manifestations. However, all these approaches are based on training static machine learning models which require a significantly large clinically annotated dataset and are computationally heavy and are most of the time non-real time. In this work, a real-time light weight active learning-based approach is presented for faster triaging in COVID-19 subjects in resource constrained settings. The tool, based on the you look only once (YOLO) network, has the capability of providing the quality of images based on the identification of various LUS landmarks, artefacts and manifestations. This tool also predict the severity of lung infection and make use of the possibility of active learning based on the feedback from clinicians or on the image quality. The capability of this tool to summarize the significant frames which are having high severity of infection and high image quality will be helpful for clinicians to discern things more easily. The results show that the proposed object detection tool has a mean average precision (mAP) of 66% at an Intersection over Union (IoU) threshold of 0.5 for the prediction of LUS landmarks with initial training on less than 1000 images. The 14MB lightweight YOLOv5s network achieves 123 FPS while running on a Quadro P4000 GPU. The tool is available for usage and analysis upon request from the authors and details can be found online.

Framework for biometric iris recognition in video, by deep learning and quality assessment of the iris-pupil region

In the current world scenario the influence of the COVID19 pandemic has reached universal proportions affecting almost all countries. In this sense, the need has arisen to wear gloves or to reduce direct contact with objects (such as sensors for capturing fingerprints or palm prints) as a sanitary measure to protect against the virus. In this new reality, it is necessary to have a biometric identification method that allows safe and rapid recognition of people at borders, or in quarantine controls, or in access to places of high biological risk, among others. In this scenario, iris biometric recognition has reached increasing relevance. This biometric modality avoids all the aforementioned inconveniences with proven high efficiency. However, there are still problems associated with the iris capturing and segmentation in real time that could affect the effectiveness of a System of this nature and that it is necessary to take into account. This work presents a framework for real time iris detection and segmentation in video as part of a biometric recognition system. Our proposal focuses on the stages of image capture, iris detection and segmentation in RGB video frames under controlled conditions (conditions of border and access controls, where people collaborate in the recognition process). The proposed framework is based on the direct detection of the iris-pupil region using the YOLO network, the evaluation of its quality and the semantic segmentation of iris by a Fully Convolutional Network. (FCN). The proposal of an evaluation step of the quality of the iris-pupil region reduce the passage to the system of images with problems of out of focus, blurring, occlusions, light changing and pose of the subject. For the evaluation of image quality, we propose a measure that combines parameters defined in ISO/IEC 19794-6 2005 and others derived from the systematization of the knowledge of the specialized literature. The experiments carried out in four different reference databases and an own video data set demonstrates the feasibility of its application under controlled conditions of border and access controls. The achieved results exceed or equal state-of-the-art methods under these working conditions.

Tourist Behavior in the Cruise Industry Post-COVID-19: An Examination of Service Quality, Corporate Image, and Intentions to Pay and Revisit

The COVID-19 outbreak has had detrimental consequences on the cruise industry due to the suspension of commercial cruise trips, and these effects remain apparent in Saudi Arabia. The offered service quality (SQ) in the post-COVID-19 era seems to be a critical element for improving customer experiences and satisfaction, enhancing destination attractiveness, increasing revenue, and maintaining repeat business. The current study aimed to assess the impact of service quality on tourists' satisfaction and corporate image as well as the intention to pay for cruise trips and revisit the destination among 315 tourists in Saudi Arabia. Service quality was measured using five subscales of the SERVQUAL scale, including reliability, tangibles, responsiveness, assurance, and empathy. Tourists' satisfaction was significantly influenced by four domains of SQ, whereas the intention to pay more, intention to revisit the destination, and corporate image were significantly predicted by ≤3 domains of SQ. The study's findings can help the cruise industry to improve its offerings and create more personalized and engaging experiences that meet the changing needs of customers in the recovery period after the COVID-19 outbreak.

Efficient intelligent compression and recognition system-based vision computing for computed tomography COVID images

Computed tomography (CT) image-based medical recognition is extensively used for COVID recognition as it improves recognition and scanning rate. A method for intelligent compression and recognition system-based vision computing for CT COVID (ICRS-VC-COVID) was developed. The proposed system first preprocesses lung CT COVID images. Segmentation is then used to split the image into two regions: nonregion of interest (NROI) with fractal lossy compression and region of interest with context tree weighting lossless. Subsequently, a fast discrete curvelet transform (FDCT) is applied. Finally, vector quantization is implemented through the encoder, channel, and decoder. Two experiments were conducted to test the proposed ICRS-VC-COVID. The first evaluated the segmentation compression, FDCT, wavelet transform, and discrete curvelet transform (DCT). The second evaluated the FDCT, wavelet transform, and DCT with segmentation. It demonstrates a significant improvement in performance parameters, such as mean square error, peak signal-to-noise ratio, and compression ratio. At similar computational complexity, the proposed ICRS-VC-COVID is superior to some existing techniques. Moreover, at the same bit rate, it significantly improves the quality of the image. Thus, the proposed method can enable lung CT COVID images to be applied for disease recognition with low computational power and space. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. [DOI: 10.1117/1.JEI.32.2.021404] © 2023 SPIE. All rights reserved.

Analysis of the effects of store image, trust and perceived quality on consumption habits of textile products in the COVID-19 pandemic

Consumer clothing presents behaviors defined by pre-established trends and patterns in contemporary societies, and in general the consumption of textile products follows this trend. However, as a result of the COVID-19 pandemic and the restrictions perpetuated as a consequence of it, the consumption of textile products has been affected throughout the world. Under this premise, the objective of this research is to analyze the effect of store images, trust and perceived quality on the habits of the textile consumer in the context of the COVID-19 pandemic, for which, firstly, a review of the literature was carried out regarding the variables of the habits of the textile consumer and their relationship with the store image, trust and perceived quality, for which documents from academic search engines were taken into account, such as Scopus, Web of Science, ResearchGate and Google Scholar. On the other hand, a survey was conducted among textile consumers in Ecuador. The measurement tool was completed by 500 participants. In this way, the survey was conducted virtually through Google Forms and through the use of IBM SPSS software. The sampling technique consisted of convenience sampling. For the specific case of this investigation, it was decided to opt for the use of 500 valid questionnaires. This allowed one to propose a model of structural equations based on constructs associated with reference investigations. The main results of this research confirmed that there is a positive impact of the image of the trusted establishment on the product, as well as a positive impact on the general perceived quality of consumption habits (comparison) and on the effect of the quality of perceived service in consumption habits (planning).

A comparative study of medical image enhancement algorithms and quality assessment metrics on COVID-19 CT images.

Medical imaging can help doctors in better diagnosis of several conditions. During the present COVID-19 pandemic, timely detection of novel coronavirus is crucial, which can help in curing the disease at an early stage. Image enhancement techniques can improve the visual appearance of COVID-19 CT scans and speed-up the process of diagnosis. In this study, we analyze some state-of-the-art image enhancement techniques for their suitability in enhancing the CT scans of COVID-19 patients. Six quantitative metrics, Entropy, SSIM, AMBE, PSNR, EME, and EMEE, are used to evaluate the enhanced images. Two experienced radiologists were involved in the study to evaluate the performance of the enhancement techniques and the quantitative metrics used to assess them.

Telepresence Video Quality Assessment

Video conferencing, which includes both video and audio content, has contributed to dramatic increases in Internet traffic, as the COVID-19 pandemic forced millions of people to work and learn from home. Global Internet traffic of video conferencing has dramatically increased Because of this, efficient and accurate video quality tools are needed to monitor and perceptually optimize telepresence traffic streamed via Zoom, Webex, Meet, etc.. However, existing models are limited in their prediction capabilities on multi-modal, live streaming telepresence content. Here we address the significant challenges of Telepresence Video Quality Assessment (TVQA) in several ways. First, we mitigated the dearth of subjectively labeled data by collecting similar to 2k telepresence videos from different countries, on which we crowdsourced similar to 80k subjective quality labels. Using this new resource, we created a first-of-a-kind online video quality prediction framework for live streaming, using a multi-modal learning framework with separate pathways to compute visual and audio quality predictions. Our all-in-one model is able to provide accurate quality predictions at the patch, frame, clip, and audiovisual levels. Our model achieves state-of-the-art performance on both existing quality databases and our new TVQA database, at a considerably lower computational expense, making it an attractive solution for mobile and embedded systems.

Automatic Detection of Sports Injuries Based on Multimedia Intelligent 3D Images

With the concept of quality education put forward, students' sports activities have received extensive attention from society. As a result, sports injuries among students during sports have also aroused widespread concern, so it is an irreversible trend to detect sports injuries. The development of multimedia intelligent 3D image technology also provides technical support for sports injury detection, which makes it possible to automatically detect sports injuries. In this paper, an automatic detection system for sports injuries was designed based on multimedia intelligent three-dimensional image technology, and the related content was evaluated. In the investigation of the parts of students' sports injuries, it was concluded that the injury rate of the students' ankle joints was the highest;in the investigation of the types of sports injuries among students, it was concluded that students were more likely to suffer from joint sprains;in the project investigation of students' sports injuries, it was concluded that students were more prone to sports injuries in ball games with a large amount of exercise;in the investigation of the causes of students' sports injuries, it was concluded that the main reasons for students' sports injuries were physical insufficiency and a bad venue environment;in terms of the performance evaluation of the sports injury detection system, it was concluded that the accuracy, effectiveness, authenticity, and efficiency of the sports injury automatic detection system based on multimedia intelligent three-dimensional image processing technology had been improved to different degrees compared with the traditional sports injury detection methods. Therefore, the detection efficiency of the sports injury automatic detection system proposed in this paper was improved by 5.7% compared with the traditional sports injury detection method.

Low-Dose Chest CT Protocols for Imaging COVID-19 Pneumonia: Technique Parameters and Radiation Dose.

Chest computed tomography (CT) plays a vital role in the early diagnosis, treatment, and follow-up of COVID-19 pneumonia during the pandemic. However, this raises concerns about excessive exposure to ionizing radiation. This study aimed to survey radiation doses in low-dose chest CT (LDCT) and ultra-low-dose chest CT (ULD) protocols used for imaging COVID-19 pneumonia relative to standard CT (STD) protocols so that the best possible practice and dose reduction techniques could be recommended. A total of 564 articles were identified by searching major scientific databases, including ISI Web of Science, Scopus, and PubMed. After evaluating the content and applying the inclusion criteria to technical factors and radiation dose metrics relevant to the LDCT protocols used for imaging COVID-19 patients, data from ten articles were extracted and analyzed. Technique factors that affect the application of LDCT and ULD are discussed, including tube current (mA), peak tube voltage (kVp), pitch factor, and iterative reconstruction (IR) algorithms. The CTDIvol values for the STD, LDCT, and ULD chest CT protocols ranged from 2.79-13.2 mGy, 0.90-4.40 mGy, and 0.20-0.28 mGy, respectively. The effective dose (ED) values for STD, LDCT, and ULD chest CT protocols ranged from 1.66-6.60 mSv, 0.50-0.80 mGy, and 0.39-0.64 mSv, respectively. Compared with the standard (STD), LDCT reduced the dose reduction by a factor of 2-4, whereas ULD reduced the dose reduction by a factor of 8-13. These dose reductions were achieved by applying scan parameters and techniques such as iterative reconstructions, ultra-long pitches, and fast spectral shaping with a tin filter. Using LDCT, the cumulative radiation dose of serial CT examinations during the acute period of COVID-19 may have been inferior or equivalent to that of conventional CT.

Contactless non-invasive method to identify abnormal tongue area using K-mean and problem identification in COVID-19 scenario

Due to the spread of COVID-19 all around the world, there is a need of automatic system for primary tongue ulcer cancerous cell detection since everyone do not go to hospital due to the panic and fear of virus spread. These diseases if avoided may spread soon. So, in such a situation, there is global need of improvement in disease sensing through remote devices using non-invasive methods. Automatic tongue analysis supports the examiner to identify the problem which can be finally verified using invasive methods. In automated tongue analysis image quality, segmentation of the affected region plays an important role for disease identification. This paper proposes mobile-based image sensing and sending the image to the examiner, if examiner finds an issue in the image, the examiner may guide the user to go for further treatment. For segmentation of abnormal area, K-mean clustering is used by varying its parameters.Copyright © 2022 Inderscience Enterprises Ltd.

Quality Assurance of Chest X-ray Images with a Combination of Deep Learning Methods

Background: Chest X-ray (CXR) imaging is the most common examination;however, no automatic quality assurance (QA) system using deep learning (DL) has been established for CXR. This study aimed to construct a DL-based QA system and assess its usefulness. Method: Datasets were created using over 23,000 images from Chest-14 and clinical images. The QA system consisted of three classification models and one regression model. The classification method was used for the correction of image orientation, left–right reversal, and estimating the patient's position, such as standing, sitting, and lying. The regression method was used for the correction of the image angle. ResNet-50, VGG-16, and the original convolutional neural network (CNN) were compared under five cross-fold evaluations. The overall accuracy of the QA system was tested using clinical images. The mean correction time of the QA system was measured. Result: ResNet-50 demonstrated higher performance in the classification. The original CNN was preferred in the regression. The orientation, angle, and left–right reversal of all images were fully corrected in all images. Moreover, patients' positions were estimated with 96% accuracy. The mean correction time was approximately 0.4 s. Conclusion: The DL-based QA system quickly and accurately corrected CXR images.

Application of chest low-dose CT screening of Corona Virus Disease 2019 with a third-generation dual-source scanner.

Objective: To investigate the application value of third-generation dual-source CT(3-G DSCT) low-dose scan mode combined with iterative reconstruction technology in the screening of COVID-19 and to evaluate the radiation dose. Method(s): One hundred and twenty patients suspected of COVID-19 from December 2019 to February 2020 were retrospectively analysed and randomly divided into two groups (test group and conventional group, 60 patients in each). The parameters for test group included 3-G DSCT, Turbo Flash scan mode, CARE kV, with reference 90 kV, pitch 2.0, and ADMIRE algorithm, while those parameters for conventional group included the 128-slice CT, conventional spiral scan mode, 120 kV, pitch 1.2, and FBP algorithm. The CT values of aorta, spinal posterior muscle, and subcutaneous fat, the aortic noise, signal-to-noise ratio (SNR), and contrast noise ratio (CNR) were compared to evaluate the image quality between two groups. Two experienced doctors scored the image quality using a double-blind method, and compared the CT dose index volume (CTDIvol), dose-length product (DLP), and effective dose (E) of the two groups. Result(s): The CT value of the aorta and spinal posterior muscle and the aortic SNR in the test group were (45.38+/-4.77), (53.41+/-8.44) HU, and 2.82+/-0.59, and significantly higher than those in the conventional group [(39.68+/-6.26), (42.66+/-6.32) HU, 2.58+/-0.61, t=5.608, 7.897, 2.162, P<0.05]. The aortic noise, CNR and subjective scores between the two groups had no significant difference( P>0.05). The CTDIvol, DLP, and E in the test group were (3.09+/-1.02) mGy, (107.57+/-32.81) mGy*cm, (1.51+/-0.46) mSv, significantly lower than those in the conventional group [(7.00+/-1.80) mGy, (261.65+/-73.93) mGy*cm, (3.66+/-1.03) mSv;t=-14.680, -14.756, -14.756, P<0.05]. Conclusion(s): In the screening of COVID-19, using low-dose scanning mode of 3-G DSCT combined with iterative reconstruction technology would provide diagnostic quality images and meanwhile effectively reduce the radiation dose and improve the SNR of the image.Copyright © 2020 by the Chinese Medical Association.

An Effective COVID-19 CT Image Denoising Method Based on a Deep Convolutional Neural Network

Background: Faced with the global threat posed by SARS-CoV-2 (COVID-19), low-dose computed tomography (LDCT), as the primary diagnostic tool, is often accompanied by high levels of noise. This can easily interfere with the radiologist's assessment. Convolutional neural networks (CNN), as a method of deep learning, have been shown to have excellent effects in image denoising. Objective: The objective of the study was to use modified convolutional neural network algorithm to train the denoising model. The purpose was to make the model extract the highlighted features of the lesion region better and ensure its effectiveness in removing noise from COVID-19 lung CT images, preserving more important detail information of the images and reducing the adverse effects of denoising. Methods: We propose a CNN-based deformable convolutional denoising neural network (DCDNet). By combining deformable convolution methods with residual learning on the basis of CNN structure, more image detail features are retained in CT image denoising. Results: According to the noise reduction evaluation index of PSNR, SSIM and RMSE, DCDNet shows excellent denoising performance for COVID-19 CT images. From the visual effect of denoising, DCDNet can effectively remove image noise and preserve more detailed features of lung lesions. Conclusion: The experimental results indicate that the DCDNet-trained model is more suitable for image denoising of COVID-19 than traditional image denoising algorithms under the same training set. © 2023 Bentham Science Publishers.

The application value of low-dose CT scan in pregnant women with COVID-19.

Objective: To explore the value of low-dose CT in pregnancy with COVID-19. Method(s): A retrospective analysis was performed on the clinical characteristics, laboratory tests, and chest CT findings of 12 pregnant women with COVID-19 diagnosed by nucleic acid testing in the Renmin Hospital of Wuhan University from January 20, 2020 to February 16, 2020. Two radiologists blinded to the reconstruction algorithm independently scored subjective image quality on a 5-point Likert scale. Image quality score >= 3 was acceptable in clinics. The CT radiation doses were recorded, including CT volume dose index (CTDIvol), dose length product (DLP), and effective radiation dose (E). Two radiologists observed the distribution, shape, density, and other characteristics of lung lesions, and they also decided whether hilar, mediastinal lymphadenopathy, and pleural changed. Result(s): A total of 12 pregnant women with COVID-19, 8 had cough, 4 had fever, 2 had chest tightness, and 1 had dyspnea and diarrhea each. The CT image quality score of all patients was 3-4, with an average of 3.46, which fully met the clinical diagnosis requirements. The CTDIvol value was 1.13-4.31 mGy, with an average of 3.02 mGy. The DLP value was 34.48-75.29 mGy*cm, with an average of 55.48 mGy*cm. The Evalue was 0.48-1.05 mSv, with an average of 0.78 mSv. In all cases, chest CT examination showed abnormal manifestations after clinical symptoms, including unilateral lung lesions in 5 cases and bilateral lung lesions in 7 cases, 1 case of ground-glass opacity, 1 case of solidification, 7 cases of ground-glass and consolidation, 1 case of strip opacity, ground-glass, and consolidation and strip cable shadow coexisted in 2 cases. Conclusion(s): The application of low-dose CT scan in pregnant women with COVID-19 is completely feasible. CT mainly manifested as bilateral lung patchy and flaky ground-glass opacity with consolidation. Active and effective treatment can help recover and improve prognosis.Copyright © 2020 by the Chinese Medical Association.

Evaluation of low-dose CT protocol of novel coronavirus pneumonia based on infection prevention and control.

Objective: To explore a low dose CT scanning method on novel coronavirus (COVID-19) pneumonia based on infection prevention and control. Method(s): A total of 140 patients with confirmed novel coronavirus pneumonia in Xiehe hospital from January 20, 2020 to February 28, 2020 were undertaken CT scan and divided into low dose group and conventional dose group. The patients in low dose group(120 kV, 31 mAs) consisted of mild type(51), severe type(15) and critically ill type(4);and those in conventional dose group(120 kv, adaptive milliampere second) consisted of mild type(48), severe type(17) and critically ill type(5). The effective radiation dose, SNR and CNR of CT scan were compared between two groups. A senior and a middle radiologist made the image subjective quality scores, respectively. Result(s): The effective dose in low dose group was lower than that of conventional dose group(t=-48.343, P<0.05). There was no significant difference in SNR and CNR between two groups(P>0.05). For severe and critically ill patients, the score in low dose group was significantly lower than that in conventional dose group(t=-2.781, P<0.05). There was no significant difference in scores between two groups for mild patients(P>0.05). Conclusion(s): Low-dose CT scanning could meet the image quality needs for patients with COVID-19 and meanwhile significantly reduce the radiation dose.Copyright © 2020 by the Chinese Medical Association.

A novel medical image enhancement technique based on hybrid method

Medical images are a specific type of image that can be used to diagnose disease in patients. Critical uses for medical images can be found in many different areas of medicine and healthcare technology. Generally, the medical images produced by these imaging methods have low contrast. As a result, such types of images need immediate and fast enhancement. This paper introduced a novel image enhancement methodology based on the Laplacian filter, contrast limited adaptive histogram equalization, and an adjustment algorithm. Two image datasets were used to test the proposed method: The DRIVE dataset, forty images from the COVID-19 Radiography Database, endometrioma-11, normal-brain-MRI-6, and simple-breast-cyst-2. In addition, we used the robust MATLAB package to evaluate our proposed algorithm's efficacy. The results are compared quantitatively, and their efficacy is assessed using four metrics: Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Contrast to Noise Ratio (CNR), and Entropy (Ent). The experiments show that the proposed method yields improved images of higher quality than those obtained from state-of-the-art techniques regarding MSE, CNR, PSNR, and Ent metrics.

Reducing face-to-face visits for skin cancer assessment using patient-led teledermatology: a retrospective review of experience with the My Skin Selfie platform

Teledermatology has made massive progress throughout the COVID-19 pandemic, but significant debates are emerging about the correct way to use technology and deliver services in a nonpandemic future where all face-to-face (F2F) options will be available again. Some very fixed views are emerging, and it is important that future national guidelines are both evidence-based and pragmatic. Improvements in phone camera technology allow patients to take high-quality skin images. Adequate assessment of moles does require dermoscopy, but many other skin lesions can be accurately triaged without it, as was commonplace until relatively recently. There is now extensive literature confirming the ability to make safe and secure diagnoses of skin cancers using teledermatology. Debates around the optimal uses of teledermatology are now in progress. We report retrospective data from two pilot studies, for basal cell carcinoma (BCC) and 2-week wait (2WW), using patient-led skin images taken using the MySkinSelfie app and viewed on the MySkinSelfie web portal. The aim was to evaluate the number of F2F visits that had been prevented. In each pilot, patients were initially referred by their general practitioner in the usual way, without images. The BCC pilot was conducted prepandemic. Patients were sent a letter inviting them to submit images. Once images had been received, they were booked into a telephone clinic for assessment. In total, 288 patients were invited and 76 submitted images. Thirty-two (42%) needed further F2F review, 37 (49%) were booked for a surgical procedure, five (6%) were prescribed a cream and two (3%) lesions resolved. The 2WW pilot was conducted during the pandemic. Patients referred on a 2WW pathway were telephoned by administration staff and invited to submit images followed by a telephone consultation. In total, 1385 were invited and 704 submitted images. Two hundred and sixty-five (37 6%) needed further F2F review, 170 (24 1%) were booked for a surgical procedure, 219 (31 1%) were discharged and 50 (7 1%) received a cream. The agreement between diagnosis via digital images of nonpigmented skin lesions and a final diagnosis was 83%. Compared with a standard F2F model, 58% (BCC) and 62% (2WW) avoided a first F2F appointment, providing benefits for patients' travel time, infection risk and missed time at work for patients and carers. A larger prospective study is now needed to document image quality, diagnostic concordance and health economic effects with more precision.

CT Transit Time And Its Correlation To Cardiac Output Derived From Mr

Introduction: Pulmonary transit time (PTT), the time taken for contrast to travel from the left to right ventricle, can be used as a surrogate marker for cardiac output. There have been previous studies evaluating the prognostic significance of Magnetic Resonance (MR) and Computed Tomography (CT) PTT in heart failure patients. This study used dynamic CT images to determine the PTT and study its correlation with left and right ventricular ejection fraction and left and right cardiac output in COVID patients, with a known range of cardiac outputs. Method(s): 123 COVID-19 patients were retrospectively studied. A single contrast bolus timing scan was acquired with a 320-detector CT (Acquilion ONE, Canon). A single 2 mm slice was placed axially where left and right ventricle and descending aorta were visualised. Contrast administration and scan acquisition began at the same with 20 ml of Omnipaque with 40 ml saline flush at 5 ml/s. One image was acquired every second and the total scan time was 26 seconds. A circular ROI was placed in the centre left and right ventricle, the signal intensity was plotted over time for each of these regions. Matlab software was used to extract the peak contrast time between the right and left ventricles. MR cardiac images were acquired on a 3 T Prisma, which determined MR PTT, left and right ejection fraction (LVEF, RVEF) and left and right ventricle cardiac output (LVCO, RVCO). These values were already computed from a previous study where this data was taken from. Correlations were studied using the Pearson correlation method using Minitab software. Result(s): There was correlation between MR PTT and LVEF and RVEF, r = - 0.433 p<0.05 and r=-0.358 p<0.05 respectively. A correlation was also seen with CT PTT and LVEF (figure 1) and RVEF, r=-0.-345 p<0.05 and r=-0.2 p=0.029 respectively. A correlation was seen for MR PTT and LVCO and RVCO, r=-0.322 p<0.05 and r=-0.295 p<0.05 but not for CT PTT and LVCO and RVCO, r=-0.1 p=0.297 and r=-0.04 p=0.668 respectively. Conclusion(s): A correlation was seen between MR PTT and CT PTT for both LVEF and RVEF, but this was not seen for CT PTT and LVCO and RVCO. Further work is required to understand the limitations of the CT PTT and why it fails to correlate with these parameters. Limitations may include dynamic CT temporal resolution or due to poor image quality due to motion from breathing. Compared to previous studies there is agreement between the MR PTT and MR cardiac parameters. At this stage there is an indication that CT PTT could be a potential tool to estimate LVEF and RVEF. [Formula presented]Copyright © 2023