Introducing the GEV Activation Function for Highly Unbalanced Data to Develop COVID-19 Diagnostic Models

Fast and accurate diagnosis is essential for the efficient and effective control of the COVID-19 pandemic that is currently disrupting the whole world. Despite the prevalence of the COVID-19 outbreak, relatively few diagnostic images are openly available to develop automatic diagnosis algorithms. Traditional deep learning methods often struggle when data is highly unbalanced with many cases in one class and only a few cases in another; new methods must be developed to overcome this challenge. We propose a novel activation function based on the generalized extreme value (GEV) distribution from extreme value theory, which improves performance over the traditional sigmoid activation function when one class significantly outweighs the other. We demonstrate the proposed activation function on a publicly available dataset and externally validate on a dataset consisting of 1,909 healthy chest X-rays and 84 COVID-19 X-rays. The proposed method achieves an improved area under the receiver operating characteristic (DeLong's p-value < 0.05) compared to the sigmoid activation. Our method is also demonstrated on a dataset of healthy and pneumonia vs. COVID-19 X-rays and a set of computerized tomography images, achieving improved sensitivity. The proposed GEV activation function significantly improves upon the previously used sigmoid activation for binary classification. This new paradigm is expected to play a significant role in the fight against COVID-19 and other diseases, with relatively few training cases available

Development and external validation of a mixed-effects deep learning model to diagnose COVID-19 from CT imaging

BackgroundThe automatic analysis of medical images has the potential improve diagnostic accuracy while reducing the strain on clinicians. Current methods analyzing 3D-like imaging data, such as computerized tomography imaging, often treat each image slice as individual slices. This may not be able to appropriately model the relationship between slices.MethodsOur proposed method utilizes a mixed-effects model within the deep learning framework to model the relationship between slices. We externally validated this method on a data set taken from a different country and compared our results against other proposed methods. We evaluated the discrimination, calibration, and clinical usefulness of our model using a range of measures. Finally, we carried out a sensitivity analysis to demonstrate our methods robustness to noise and missing data.ResultsIn the external geographic validation set our model showed excellent performance with an AUROC of 0.930 (95%CI: 0.914, 0.947), with a sensitivity and specificity, PPV, and NPV of 0.778 (0.720, 0.828), 0.882 (0.853, 0.908), 0.744 (0.686, 0.797), and 0.900 (0.872, 0.924) at the 0.5 probability cut-off point. Our model also maintained good calibration in the external validation dataset, while other methods showed poor calibration.ConclusionDeep learning can reduce stress on healthcare systems by automatically screening CT imaging for COVID-19. Our method showed improved generalizability in external validation compared to previous published methods. However, deep learning models must be robustly assessed using various performance measures and externally validated in each setting. In addition, best practice guidelines for developing and reporting predictive models are vital for the safe adoption of such models

Optimization of the Cell Structure for Radiation-Hardened Power MOSFETs

Power MOSFETs specially designed for space power systems are expected to simultaneously meet the requirements of electrical performance and radiation hardness. Radiation-hardened (rad-hard) power MOSFET design can be achieved via cell structure optimization. This paper conducts an investigation of the cell geometrical parameters with major impacts on radiation hardness, and a rad-hard power MOSFET is designed and fabricated. The experimental results validate the devices&#8217; total ionizing dose (TID) and single event effects (SEE) hardness to suitably satisfy most space power system requirements while maintaining acceptable electrical performance

Successful Ultrasound-Guided Methotrexate Intervention in the Treatment of Heterotopic Interstitial Pregnancy: A Case Report and Literature Review

Purpose: This study aims to share the experience of minimally invasive ultrasound-guided methotrexate intervention in the treatment of heterotopic interstitial pregnancy (HIP) with good pregnancy outcomes, and to review the treatment, pregnancy outcomes, and impact on the future fertility of HIP patients. Methods: The paper describes the medical history, clinical manifestations, treatment history, and clinical prognosis of a 31-year-old woman with HIP, and reviews cases of HIP from 1992 to 2021 published in the PubMed database. Results: The patient was diagnosed with HIP by transvaginal ultrasound (TVUS) at 8 weeks after assisted reproductive technology. The interstitial gestational sac was inactivated by ultrasound-guided methotrexate injection. The intrauterine pregnancy was successfully delivered at 38 weeks of gestation. Twenty-five HIP cases in 24 studies published on PubMed from 1992 to 2021 were reviewed. Combined with our case, there were 26 cases in total. According to these studies, 84.6% (22/26) of these cases were conceived by in vitro fertilization embryo transfer, 57.7% (15/26) had tubal disorders, and 23.1% (6/26) had a history of ectopic pregnancy; 53.8% (14/26) of the patients presented with abdominal pain and 19.2% (5/26) had vaginal bleeding. All cases were confirmed by TVUS. In total, 76.9% (20/26) of intrauterine pregnancies had a good prognosis (surgery vs. ultrasound interventional therapy 1:1). All fetuses were born without abnormalities. Conclusions: The diagnosis and treatment of HIP remain challenging. Diagnosis mainly relies on TVUS. Interventional ultrasound therapy and surgery are equally safe and effective. Early treatment of concomitant heterotopic pregnancy is associated with high survival of the intrauterine pregnancy

Treatment experience for different risk groups of Kaposiform hemangioendothelioma

Peer reviewed: TrueBackgroundKaposiform hemangioendothelioma (KHE) is a rare vascular tumor with a high risk of mortality. Few studies with large samples of KHE have been reported. KHE may develop into the Kasabach–Merritt phenomenon (KMP), which is characterized by thrombocytopenia and consumptive coagulopathy. The features of severe symptomatic anemia and life-threatening low platelets make the management of KHE associated with KMP challenging.ObjectiveThe aim of this study was to examine the clinical characteristics of patients with KHE and discuss the treatment experience for different risk groups of KHE.MethodsThrough a retrospective review of 70 patients diagnosed with KHE between 2017 and 2022 in our center, we classify lesions into three clinicopathological stages based on the tumor involving depth, and divided the severity of KHE into three levels by estimating clinicopathological stages and severity of thrombocytopenia. Treatments of different severity groups were estimated with sufficient data.ResultsIn our cohort, 27% were neonates, and KHE lesion occurred at birth in 84% of patients. There was a slight male predominance (32 girls and 38 boys). Common clinical characteristics included associated coagulation disorder (100%), locally aggressive cutaneous blue–purple mass (89%), thrombocytopenia (78%), and local pain or joint dysfunction (20%). The lower extremities were the dominant location (35%), followed by the trunk (29%), the maxillofacial region and neck (24%), and the upper extremities (10%). Of the total cohort, 78% developed KMP; the median age at which thrombocytopenia occurred was 27.8 days. The median platelet count of patients who were associated with KMP was 24,000/µL in our cohort. Ninety-two percent of patients were given surgery treatment and 89% of these patients were given high-dose methylprednisolone (5-6 mg/kg daily) before surgery. In 55 patients with KMP, 36% were sensitive to high-dose corticosteroid therapy. Patients from the low-risk group (eight cases) underwent operation, all of whom recovered without recurrence after a maximum follow-up of 5 years. Out of 26 patients from the high-risk group, 25 underwent surgery treatment, with 1 case undergoing secondary surgery after recurrence and 1 case taking sirolimus. Out of 36 cases from the extremely high-risk group, 32 underwent surgery (including 2 cases who underwent external carotid artery ligation and catheterization), 3 of whom underwent secondary operation after recurrence, and the remaining 4 cases took medicine. The mean length of having sirolimus was 21 months; two cases stopped taking sirolimus due to severe pneumonia. Two cases died at 1 and 3 months after discharge.ConclusionsOur study describes the largest assessment of high-risk patients with KHE who have undergone an operation to date, with 5 years of follow-up to track recovery, which provides invaluable knowledge for the future treatment of patients with KHE and KMP from different risk groups: Early surgical intervention may be the most definitive treatment option for most patients with KHE; multimodality treatment is the best choice for the extremely high-risk group.</jats:sec

Bilateral adaptive graph convolutional network on CT based Covid-19 diagnosis with uncertainty-aware consensus-assisted multiple instance learning

Coronavirus disease (COVID-19) has caused a worldwide pandemic, putting millions of people’s health and lives in jeopardy. Detecting infected patients early on chest computed tomography (CT) is critical in combating COVID-19. Harnessing uncertainty-aware consensus-assisted multiple instance learning (UC-MIL), we propose to diagnose COVID-19 using a new bilateral adaptive graph-based (BA-GCN) model that can use both 2D and 3D discriminative information in 3D CT volumes with arbitrary number of slices. Given the importance of lung segmentation for this task, we have created the largest manual annotation dataset so far with 7,768 slices from COVID-19 patients, and have used it to train a 2D segmentation model to segment the lungs from individual slices and mask the lungs as the regions of interest for the subsequent analyses. We then used the UC-MIL model to estimate the uncertainty of each prediction and the consensus between multiple predictions on each CT slice to automatically select a fixed number of CT slices with reliable predictions for the subsequent model reasoning. Finally, we adaptively constructed a BA-GCN with vertices from different granularity levels (2D and 3D) to aggregate multi-level features for the final diagnosis with the benefits of the graph convolution network’s superiority to tackle cross-granularity relationships. Experimental results on three largest COVID-19 CT datasets demonstrated that our model can produce reliable and accurate COVID-19 predictions using CT volumes with any number of slices, which outperforms existing approaches in terms of learning and generalisation ability. To promote reproducible research, we have made the datasets, including the manual annotations and cleaned CT dataset, as well as the implementation code, available at https://doi.org/10.5281/zenodo.6361963