ABSTRACT
Pneumonia is a very common and fatal disease, which needs to be identified at the initial stages in order to prevent a patient having this disease from more damage and help him/her in saving his/her life. Various techniques are used for the diagnosis of pneumonia including chest X-ray, CT scan, blood culture, sputum culture, fluid sample, bronchoscopy, and pulse oximetry. Medical image analysis plays a vital role in the diagnosis of various diseases like MERS, COVID-19, pneumonia, etc. and is considered to be one of the auspicious research areas. To analyze chest X-ray images accurately, there is a need for an expert radiologist who possesses expertise and experience in the desired domain. According to the World Health Organization (WHO) report, about 2/3 people in the world still do not have access to the radiologist, in order to diagnose their disease. This study proposes a DL framework to diagnose pneumonia disease in an efficient and effective manner. Various Deep Convolutional Neural Network (DCNN) transfer learning techniques such as AlexNet, SqueezeNet, VGG16, VGG19, and Inception-V3 are utilized for extracting useful features from the chest X-ray images. In this study, several machine learning (ML) classifiers are utilized. The proposed system has been trained and tested on chest X-ray and CT images dataset. In order to examine the stability and effectiveness of the proposed system, different performance measures have been utilized. The proposed system is intended to be beneficial and supportive for medical doctors to accurately and efficiently diagnose pneumonia disease. © 2021 Yar Muhammad et al.
ABSTRACT
INTRODUCTION: A critical question facing transplant programs is whether, when, and how to safely accept living kidney donors (LKDs) who have recovered from COVID-19 infection. The purpose of the study is to understand current practices related to accepting these LKDs. METHODS: We surveyed US transplant programs from 3 September through 3 November 2020. Center level and participant level responses were analyzed. RESULTS: A total of 174 respondents from 115 unique centers responded, representing 59% of US LKD programs and 72.4% of 2019 and 72.5% of 2020 LKD volume (Organ Procurement and Transplantation Network-OPTN 2021). In all, 48.6% of responding centers had received inquiries from such LKDs, whereas 44.3% were currently evaluating. A total of 98 donors were in the evaluation phase, whereas 27.8% centers had approved 42 such donors to proceed with donation. A total of 50.8% of participants preferred to wait >3 months, and 91% would wait at least 1 month from onset of infection to LD surgery. The most common reason to exclude LDs was evidence of COVID-19-related AKI (59.8%) even if resolved, followed by COVID-19-related pneumonia (28.7%) and hospitalization (21.3%). The most common concern in accepting such donors was kidney health postdonation (59.2%), followed by risk of transmission to the recipient (55.7%), donor perioperative pulmonary risk (41.4%), and donor pulmonary risk in the future (29.9%). CONCLUSION: Practice patterns for acceptance of COVID-19-recovered LKDs showed considerable variability. Ongoing research and consensus building are needed to guide optimal practices to ensure safety of accepting such donors. Long-term close follow-up of such donors is warranted.