A 15-Gbps BiCMOS XNOR gate for fast recognition of COVID-19 in binarized neural networks

The COVID-19 pandemic is spreading around the world causing more than 177 million cases and over 3.8 million deaths according to the European Centre for Disease Prevention and Control. The virus has devastating effects on economies, health, and well-being of worldwide population. Due to the high increase in daily cases, the available number of COVID-19 test kits in under-developed countries is scarce. Hence, it is vital to implement an effective screening method of patients using chest radiography since the equipment already exists. With the presence of automatic detection systems, any abnormalities in chest radiography that characterizes COVID-19 can be identified. Several artificial-intelligence algorithms have been proposed to detect the virus. However, neural networks training is considered to be time-consuming. Since computations in training neural networks are spent on floating-point multiplications, high computational power is required. Multipliers consume the most space and power among all arithmetic operators in deep neural networks. This paper proposes a 15 Gbps high-speed bipolar-complementary-metal-oxide-semiconductor (BiCMOS) exclusive-nor (XNOR) gate to replace multipliers in binarized neural networks. The proposed gate can be implemented on BiCMOS-based field-programmable gate arrays (FPGAs). This will significantly improve the response time in identifying chest abnormalities in CT scans and X-rays.

Retrieving a lost needle in the descending aorta during hemi-arch replacement

A 73-year-old man diagnosed with moderate aortic insufficiency and dilatation of the aortic root and ascending aorta underwent a modified Bentall procedure and hemi-arch aortic replacement. During open distal anastomosis of the ascending aorta, the surgical needle was lost. Because of circulatory arrest, the operation was continued; before closing the chest, radiography and a transesophageal echo were located in the needle in the descending aorta. It was retrieved using a snare catheter via the graft branch under fluoroscopic guidance. Thus, locating the needle in the descending aorta and leaving the graft branch uncut led to its removal without a new incision.

Chest radiography in children aged 2–59 months enrolled in the Innovative Treatments in Pneumonia (ITIP) project in Lilongwe Malawi: a secondary analysis

Background Pneumonia is the leading infectious cause of death in children aged under 5 years in low- and middle-income countries (LMICs). World Health Organization (WHO) pneumonia diagnosis guidelines rely on non-specific clinical features. We explore chest radiography (CXR) findings among select children in the Innovative Treatments in Pneumonia (ITIP) project in Malawi in relation to clinical outcomes. Methods When clinically indicated, CXRs were obtained from ITIP-enrolled children aged 2 to 59 months with community-acquired pneumonia hospitalized with treatment failure or relapse. ITIP1 (fast-breathing pneumonia) and ITIP2 (chest-indrawing pneumonia) trials enrolled children with non-severe pneumonia while ITIP3 enrolled children excluded from ITIP1 and ITIP2 with severe pneumonia and/or selected comorbidities. A panel of trained pediatricians classified the CXRs using the standardized WHO CXR research methodology. We analyzed the relationship between CXR classifications, enrollee characteristics, and outcomes. Results Between March 2016 and June 2018, of 114 CXRs obtained, 83 met analysis criteria with 62.7% (52/83) classified as having significant pathology per WHO standardized interpretation. ITIP3 (92.3%; 12/13) children had a higher proportion of CXRs with significant pathology compared to ITIP1 (57.1%, 12/21) and ITIP2 (57.1%, 28/49) (p-value = 0.008). The predominant pathological CXR reading was “other infiltrates only” in ITIP1 (83.3%, 10/12) and ITIP2 (71.4%, 20/28), while in ITIP3 it was “primary endpoint pneumonia”(66.7%, 8/12,; p-value = 0.008). The percent of CXRs with significant pathology among children clinically cured (60.6%, 40/66) vs those not clinically cured (70.6%, 12/17) at Day 14 was not significantly different (p-value = 0.58). Conclusions In this secondary analysis we observed that ITIP3 children with severe pneumonia and/or selected comorbidities had a higher frequency of CXRs with significant pathology, although these radiographic findings had limited relationship to Day 14 outcomes. The proportion of CXRs with “primary endpoint pneumonia” was low. These findings add to existing data that additional diagnostics and prognostics are important for improving the care of children with pneumonia in LMICs. Trial registration ITIP1, ITIP2, and ITIP3 were registered with ClinicalTrials.gov (NCT02760420, NCT02678195, and NCT02960919, respectively).

Radiological follow-up of adults hospitalised with pneumonia and SARS-CoV-2 infection, in Bristol UK, during the COVID19 pandemic

For patients with pneumonia and COVID19 repeating chest radiography is recommend in current British Thoracic Society (BTS) guidelines. Over two distinct time periods during the COVID19 pandemic (Aug-Dec 2020, Jun-Aug 2021) we undertook an audit of 829 patients hospitalised with infective radiological change (pneumonia=481, COVID19=348). 654/829 patients (79%) required radiological follow-up under BTS guideline criteria. 414/654 (63%) were planned, 322/654 (49%) occurred and, of patients receiving radiological follow-up, most occurred within BTS timelines (86%). Further audits should be conducted to ensure BTS guidelines adherence, to avoid delay in diagnosing underlying malignancy or chronic lung disease.

Deep Learning-Based Four-Region Lung Segmentation in Chest Radiography for COVID-19 Diagnosis

Imaging plays an important role in assessing the severity of COVID-19 pneumonia. Recent COVID-19 research indicates that the disease progress propagates from the bottom of the lungs to the top. However, chest radiography (CXR) cannot directly provide a quantitative metric of radiographic opacities, and existing AI-assisted CXR analysis methods do not quantify the regional severity. In this paper, to assist the regional analysis, we developed a fully automated framework using deep learning-based four-region segmentation and detection models to assist the quantification of COVID-19 pneumonia. Specifically, a segmentation model is first applied to separate left and right lungs, and then a detection network of the carina and left hilum is used to separate upper and lower lungs. To improve the segmentation performance, an ensemble strategy with five models is exploited. We evaluated the clinical relevance of the proposed method compared with the radiographic assessment of the quality of lung edema (RALE) annotated by physicians. Mean intensities of segmented four regions indicate a positive correlation to the regional extent and density scores of pulmonary opacities based on the RALE. Therefore, the proposed method can accurately assist the quantification of regional pulmonary opacities of COVID-19 pneumonia patients.

The efficacy of Andrographis paniculata (Burm.f.) Wall. ex Nees crude extract in hospitalised mild COVID-19 patients: a retrospective cohort study

Background: Andrographis paniculata (AP) crude extract has been widely used in Thailand to treat mild COVID-19 infection since early 2020; however, supporting evidence was lacking. Purpose: To evaluate the efficacy of AP compared with standard treatment among hospitalised mild COVID-19 patients. Study design: Single-centre retrospective cohort study Methods: We collected data between March 2020 and August 2021 from COVID-19 patients admitted to one hospital in Thailand. Patients whose infection was confirmed by Real-Time Polymerase Chain Reaction (RT-PCR) and had normal chest radiography were included, whereas those receiving favipiravir or had unclear chest X-rays at admission were excluded. Participants were categorised as either AP or standard of care and followed for pneumonia confirmed by chest radiography. Multiple logistic regression was used to analyse the main results controlling for age, sex, history of having diabetes, hypertension, receiving statins, and antihypertensive drugs. Results: 605 out of 1,054 patients were included in the analysis. Of these, 59 patients (9.8%) developed pneumonia during the median follow-up of 7 days. The incidence rates of pneumonia were 13.93 (95%CI 10.09, 19.23) and 12.47 (95%CI 8.21, 18.94) per 1,000 person-days in AP and standard of care group, respectively. Compared to the standard of care group, the odds ratios of having pneumonia in the AP group were 1.24 (95%CI 0.71, 2.16; unadjusted model) and 1.42 (95%CI 0.79, 2.55; fully adjusted model). All sensitivity analyses produced consistent findings with the main results. Conclusion: We do not have sufficient evidence to show the efficacy of AP in mild COVID-19 infection. Interestingly, we observed the potentially harmful signal of using AP. While waiting for insights from ongoing trials, the use of AP in this condition should be done with caution.