Impact of the COVID-19 Pandemic on Patient Satisfaction After Total Joint Arthroplasty.

The coronavirus disease 2019 (COVID-19) pandemic impacted the inpatient experience before and after total joint arthroplasty (TJA). This study aimed to examine how these changes affected patient satisfaction following TJA as recorded by Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) postdischarge surveys and comments at 2 large academic institutions. A retrospective review identified patients who completed HCAHPS surveys following primary and revision TJA at 2 academic institutions: 1 in a predominately rural southern state (Institution A) and 1 in a northeastern metropolitan city (Institution B). Patients were grouped by discharge date: pre-COVID-19 (April 1, 2019, to October 31, 2019) or COVID-19 affected (April 1, 2020, to October 31, 2020). Differences in demographics, survey responses, and comment sentiments and themes were collected and evaluated. The number of HCAHPS surveys completed increased between periods at Institution A but decreased at Institution B (Institution A, 61 vs 103; Institution B, 524 vs 296). Rates of top-box survey responses remained the same across the 2 periods. The number of comments decreased at Institution B (1977 vs 1012) but increased at Institution A (55 vs 88). During the COVID-19-affected period, there was a significant increase in the negative comment rate from Institution B (11.6% vs 14.8%, P=.013) and a significant decrease in the positive comment rate from Institution A (70.9% vs 44.3%, P<.001). There was an increase in negative patient sentiments following TJA during the COVID-19 pandemic as seen in qualitative comments but not quantitative responses. This suggests that certain aspects of the TJA patient experience were impacted by COVID-19. [Orthopedics. 2023;46(2):e105-e110.].

Understanding search autocompletes from the perspectives of English and Spanish speakers during the early months of the COVID-19 pandemic.

The purpose of the study was to explore differences in Google search autocompletes between English and Spanish-speaking users during the first wave of the coronavirus disease 2019 (COVID-19) pandemic. Twenty-nine individuals who were in areas with shelter-in-place state orders participated in a virtual focus group meeting to understand the algorithm bias of COVID-19 Google autocompletes. The three focus group meetings lasted for 90-120 minutes. A codebook was created and transcripts were coded using NVivo qualitative software with a 95% intercoder reliability between two coders. Thematic analysis was used to analyze the data. Among the 29 participants, six self-identified as White, seven as Black/African American, five as American Indian or Alaska Native, four as Asian Indian, and three as Native Hawaiian or Pacific Islander. In terms of ethnicity, 21 participants identified as Hispanic/Latino. The themes that emerged from the study were: (1) autocompletes evoked fear and stress; (2) skepticism and hesitation towards autocomplete search; (3) familiarity with COVID-19 information impacts outlook on autocomplete search; (4) autocompletes can promote preselection of searches; and (5) lesser choice of autocomplete results for Spanish-speaking searchers. Spanish speakers expressed concerns and hesitation due to social factors and lack of information about COVID-19.

Challenges in Accessing and Delivering Maternal and Child Health Services during the COVID-19 Pandemic: A Cross-Sectional Rapid Survey from Six States of India.

BACKGROUND/OBJECTIVES: Globally, the COVID-19 pandemic and its prevention and control policies have impacted maternal and child health (MCH) services. This study documents the challenges faced by patients in accessing MCH services, and the experiences of health care providers in delivering those services during the COVID-19 outbreak, explicitly focusing on the lockdown period in India. METHODS: A cross-sectional study (rapid survey) was conducted in 18 districts from 6 states of India during March to June, 2020. The sample size included 540 MCH patients, 18 gynaecologists, 18 paediatricians, 18 district immunisation officers and 108 frontline health workers. Bivariate analysis and multivariable analysis were used to assess the association between sociodemographic characteristics, and challenges faced by the patients. RESULTS: More than one-third of patients (n = 212; 39%) reported that accessing MCH services was a challenge during the lockdown period, with major challenges being transportation-related difficulties (n = 99; 46%) unavailability of hospital-based services (n = 54; 23%) and interrupted outreach health services (n = 39; 18.4%). The supply-side challenges mainly included lack of infrastructural preparedness for outbreak situations, and a shortage of human resources. CONCLUSIONS/RECOMMENDATIONS: A holistic approach is required that focuses on both preparedness and response to the outbreak, as well reassignment and reinforcement of health care professionals to continue catering to and maintaining essential MCH services during the pandemic.

Non-Mucormycetes Causes of Fungal Rhino Sinusitis With Periocular Swelling in COVID-19 With Delta Variant.

OBJECTIVE:  To highlight fungi other than mucormycetes as causative agents of rhinosinusitis with periocular swelling in coronavirus (COVID-19) infection caused by Delta variant of SARS-CoV-2 virus and identify the presenting features, risk factors, intervention, and outcomes. METHODS AND ANALYSIS: A retrospective interventional study of 96 patients with fungal rhinosinusitis and periocular swelling was done in patients with concurrent or recovered COVID-19 infection with the Delta variant (B.1.617.2) of SARS-CoV-2 virus in India. All patients with mucormycetes infection were excluded. Clinical presentation, medical history, blood reports, and imaging were analyzed. Management was by intravenous (IV) liposomal amphotericin B and functional endoscopic sinus surgery (FESS) with paranasal sinus debridement. Limited orbital debridement with or without transcutaneous retrobulbar liposomal amphotericin B (TRAMB) was done in patients with orbital involvement. Postoperative antifungal therapy was decided on the basis of the causative fungi. RESULTS: Four cases of Aspergillus and one each of Fusarium, Curvularia, and Penicillium-associated fungal rhinosinusitis with periocular swelling were seen. Signs of orbital involvement on MRI were present in all four of them. Two of these showed partial third-nerve palsy while one case with aspergillosis suffered cavernous sinus thrombosis. Proptosis was not witnessed in any case. History of diabetes and use of steroids was seen in all patients. All patients had mild to moderate COVID-19 with oxygen supplementation needed in one. No mortality, acute vision loss, or exenteration took place. CONCLUSION:  Aspergillus, Fusarium, Curvularia, and Penicillium were non-mucormycetes causes of fungal rhinosinusitis with periocular swelling in COVID-19 infection with the Delta variant (B.1.617.2) of SARS COV-2 virus. Few cases showed orbital and intracranial involvement.

Non-Mucormycetes Causes of Fungal Rhino Sinusitis With Periocular Swelling in COVID-19 With Delta Variant

Objective: To highlight fungi other than mucormycetes as causative agents of rhinosinusitis with periocular swelling in coronavirus (COVID-19) infection caused by Delta variant of SARS-CoV-2 virus and identify the presenting features, risk factors, intervention, and outcomes. Methods and analysis: A retrospective interventional study of 96 patients with fungal rhinosinusitis and periocular swelling was done in patients with concurrent or recovered COVID-19 infection with the Delta variant (B.1.617.2) of SARS-CoV-2 virus in India. All patients with mucormycetes infection were excluded. Clinical presentation, medical history, blood reports, and imaging were analyzed. Management was by intravenous (IV) liposomal amphotericin B and functional endoscopic sinus surgery (FESS) with paranasal sinus debridement. Limited orbital debridement with or without transcutaneous retrobulbar liposomal amphotericin B (TRAMB) was done in patients with orbital involvement. Postoperative antifungal therapy was decided on the basis of the causative fungi. Results: Four cases of Aspergillus and one each of Fusarium, Curvularia, and Penicillium-associated fungal rhinosinusitis with periocular swelling were seen. Signs of orbital involvement on MRI were present in all four of them. Two of these showed partial third-nerve palsy while one case with aspergillosis suffered cavernous sinus thrombosis. Proptosis was not witnessed in any case. History of diabetes and use of steroids was seen in all patients. All patients had mild to moderate COVID-19 with oxygen supplementation needed in one. No mortality, acute vision loss, or exenteration took place. Conclusion: Aspergillus, Fusarium, Curvularia, and Penicillium were non-mucormycetes causes of fungal rhinosinusitis with periocular swelling in COVID-19 infection with the Delta variant (B.1.617.2) of SARS COV-2 virus. Few cases showed orbital and intracranial involvement.

Outcomes of Transcutaneous Retrobulbar Amphotericin B in Rhino-Orbital-Cerebral Mucormycosis Among Patients Recovering From COVID-19: A Preliminary Experience.

Background In this study, we aimed to assess the outcomes of transcutaneous retrobulbar injection of amphotericin B (TRAMB) in rhino-orbital-cerebral mucormycosis (ROCM) among patients recovering from coronavirus disease 2019 (COVID-19). Methodology This retrospective study was conducted at a tertiary care center in eastern India from May 29th to July 31st, 2021, and included post-COVID-19 patients admitted with stage 3 and 4a ROCM who underwent TRAMB. The details of the ophthalmic examination, laboratory investigations, and radiological examination were retrieved from patients records. Patients were given TRAMB (3.5 mg/mL) on alternate days till they underwent debulking surgery and resumed from the second postoperative day alternatively till the patients showed clinical stabilization or improvement. Results In total, 45 eyes of 41 patients were included in the study. The median number of injections given was six (minimum = 3; maximum = 10). Following was the distribution of number of injection needed in each eye: eight eyes (three injections), six eyes (four injections), seven eyes (five injections), three eyes (six injections), eight eyes (seven injections), 11 eyes (eight injections), and one eye had received nine and ten injections each. Overall, 21/32 (65.62%) eyes had improvement in proptosis whereas 9/32 (28.12%) had improvement in ptosis. Six patients had improvement in extraocular movement. In total, 25 eyes had no improvement whereas seven eyes had improvement in vision. Four eyes underwent exenteration. All nine patients with limited orbital disease had good improvement with fewer injections (median = 4). None of the patients undergoing TRAMB had an intracranial extension of disease. Moreover, 8.88% (4/45) of the eyes had post-TRAMB transient inflammation which resolved without any intervention. Finally, 3/41 of the patients died. Conclusions TRAMB can be considered as an useful therapeutic adjunct in managing ROCM. Further, it can halt the progression of the disease while awaiting definitive surgical intervention.

Impact of teleconsultation on visual and refractive outcomes in patients undergoing laser refractive surgery during COVID-19.

Purpose: To assess the role of remote teleconsultation (TC) follow-up care following a successful and uneventful laser vision correction. Methods: The study is a retrospective, comparative analysis of patients undergoing laser vision correction at tertiary care eye hospital in Southern India. The patients were divided into two groups. The first group included patients operated on before the coronavirus disease (COVID-19) pandemic and were followed up with physical consultations during their follow-up visit (Group 1). The second group comprised patients operated on during the pandemic and had at least one remote TC during their post-operative follow-up (Group 2). Results: A total of 1088 eyes of 564 patients and 717 eyes of 372 patients were included in Group 1 and 2, respectively. The mean number of visits for the patients from Group 2 during the COVID period (2.56 +/- 0.74 days) was significantly lesser (P < 0.0001) than that of Group 1 in the pre-COVID period (3.53 +/- 1.07 days). Close to 90% of the eyes achieved an uncorrected distance visual acuity (UDVA) of 20/20 in both groups (P = 0.925). 96.50% of the eyes in Group 1 and 98.18% of the eyes in Group 2 achieved UCVA 20/25 or better (P = 0.049). Eight eyes (0.73%) in Group 1 and one eye (0.14%) in Group 2 reported a loss of 2 or more lines. However, the results were not statistically significant (P = 0.156). None of the groups had any patients who had a sight-threatening complication. Conclusion: Remote TC following refractive surgery is safe and can be effectively integrated into routine refractive practice to reduce travel to the hospital for a physical consult.

Prior wavelet knowledge for multi-modal medical image segmentation using a lightweight neural network with attention guided features

Medical image segmentation plays a crucial role in diagnosing and staging diseases. It facilitates image analysis and quantification in multiple applications, but building the right appropriate solutions is essential and highly reliant on the features of different datasets and computational resources. Most existing approaches provide segmentation for a specific anatomical region of interest and are limited to multiple imaging modalities in a clinical setting due to their generalizability with high computational requirements. To mitigate these issues, we propose a robust and lightweight deep learning real-time segmentation network for multi-modality medical images called MISegNet. We incorporate discrete wavelet transform (DWT) of the input to extract salient features in the frequency domain. This mechanism allows the neurons’ receptive field to enlarge within the network. We propose a self-attention-based global context-aware (SGCA) module with varying dilation rates to enlarge the field of view and designate the importance of each scale that enhances the network’s ability to discriminate features. We build a residual shuffle attention (RSA) mechanism to improve the feature representation of the proposed model and formulate a new boundary-aware loss function called Farid End Point Error (FEPE) that correctly segments regions with ambiguous boundaries for edge detection. We confirm the versatility of the proposed model by performing experiments against eleven state-of-the-art segmentation methods on four datasets of different organs, including two publicly available datasets (i.e., ISBI2017, and COVID-19 CT) and two private datasets (i.e., ovary and liver ultrasound images). Experimental results prove that the MISegNet with 1.5M parameters, outperforms the state-of-the-art methods by 1.5%–7% (i.e., dice coefficient score) with a corresponding 23× decrease in the number of parameters and multiply-accumulate operations respectively compared to U-Net.

Clinico-Radiological-Pathologic Profile and Outcomes of Lung Transplant in Post-COVID-19 Phenotype: A Single Center Experience.

BACKGROUND: Lung transplantation (LTx) has come as hope for select patients with post-COVID acute respiratory distress syndrome (ARDS). It has a different phenotype with unique challenges. We aimed to bring out our experience with and outcomes of LTx for post-COVID ARDS. METHODS: This study is retrospective case series from a single center in India. All the patients with post-COVID end stage lung disease (ESLD) who underwent bilateral LTx between 1st May 2020 and 30th August 2021 were included. LTx was performed following no improvement with optimal medical management with adequate time provided for recovery. Information relating to demographics, comorbidities, pretransplant status, perioperative parameters, gross and histopathological findings of explanted lungs, posttransplant morbidity, and mortality were analyzed. RESULTS: This study included 23 patients. The median age of the patients in this study was 42 years and 20 participants were men (87%). The mean duration of intensive care unit stay was 15.83 ± 6.61 days. Mortality was observed among 8 participants (34.78%). Mean survival time was 34.54 weeks. Among the 8 patients who expired, the cause of death was sepsis for 6 patients (75.0%), neurologic cerebrovascular accident for 1 patient (12.5%), and cytomegalovirus for 1 patient (12.5%). All the deaths were reported in primary graft dysfunction grade 2 & 3 category. No rejections were observed on first and third month surveillance biopsies. CONCLUSIONS: LTx is the definitive option for survival in select patients with severe post-COVID-19-associated ESLD. This study brings out various challenges involved in such phenotypes and also observations in postoperative recovery.

A unified deep neuro-fuzzy approach for COVID-19 twitter sentiment classification

Covid-19 braces serious mental health crisis across the world. Since a vast majority of the population exploit social media platforms such as twitter to exchange information, rapid collecting and analyzing social media data to understand personal well-being and subsequently adopting adequate measures could avoid severe socio-economic damage. Sentiment analysis on twitter data is very useful to understand and identify the mental health issues. In this research, we proposed a unified deep neuro-fuzzy approach for Covid-19 twitter sentiment classification. Fuzzy logic has been a very powerful tool for twitter data analysis where approximate semantic and syntactic analysis is more relevant because correcting spelling and grammar in tweets are merely obnoxious. We conducted the experiment on three challenging COVID-19 twitter sentiment datasets. Experimental results demonstrate that fuzzy Sugeno integral based ensembled classifiers succeed over individual base classifiers. [ FROM AUTHOR] Copyright of Journal of Intelligent & Fuzzy Systems is the property of IOS Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Privacy Attitudes and COVID Symptom Tracking Apps: Understanding Active Boundary Management by Users.

Multiple symptom tracking applications (apps) were created during the early phase of the COVID-19 pandemic. While they provided crowdsourced information about the state of the pandemic in a scalable manner, they also posed significant privacy risks for individuals. The present study investigates the interplay between individual privacy attitudes and the adoption of symptom tracking apps. Using the communication privacy theory as a framework, it studies how users' privacy attitudes changed during the public health emergency compared to the pre-COVID times. Based on focus-group interviews (N=21), this paper reports significant changes in users' privacy attitudes toward such apps. Research participants shared various reasons for both increased acceptability (e.g., disease uncertainty, public good) and decreased acceptability (e.g., reduced utility due to changed lifestyle) during COVID. The results of this study can assist health informatics researchers and policy designers in creating more socially acceptable health apps in the future.

Intelligent Pandemic Surveillance via Privacy-Preserving Crowdsensing.

Intelligently responding to a pandemic like Covid-19 requires sophisticated models over accurate real-time data, which is typically lacking at the start, e.g., due to deficient population testing. In such times, crowdsensing of spatially tagged disease-related symptoms provides an alternative way of acquiring real-time insights about the pandemic. Existing crowdsensing systems aggregate and release data for pre-fixed regions, e.g., counties. However, the insights obtained from such aggregates do not provide useful information about smaller regions - e.g., neighborhoods where outbreaks typically occur - and the aggregate-and-release method is vulnerable to privacy attacks. Therefore, we propose a novel differentially private method to obtain accurate insights from crowdsensed data for any number of regions specified by the users (e.g., researchers and a policy makers) without compromising privacy of the data contributors. Our approach, which has been implemented and deployed, informs the development of the future privacy-preserving intelligent systems for longitudinal and spatial data analytics.

A deep learning approach for predicting severity of COVID-19 patients using a parsimonious set of laboratory markers.

The SARS-CoV-2 virus has caused tremendous healthcare burden worldwide. Our focus was to develop a practical and easy-to-deploy system to predict the severe manifestation of disease in patients with COVID-19 with an aim to assist clinicians in triage and treatment decisions. Our proposed predictive algorithm is a trained artificial intelligence-based network using 8,427 COVID-19 patient records from four healthcare systems. The model provides a severity risk score along with likelihoods of various clinical outcomes, namely ventilator use and mortality. The trained model using patient age and nine laboratory markers has the prediction accuracy with an area under the curve (AUC) of 0.78, 95% CI: 0.77-0.82, and the negative predictive value NPV of 0.86, 95% CI: 0.84-0.88 for the need to use a ventilator and has an accuracy with AUC of 0.85, 95% CI: 0.84-0.86, and the NPV of 0.94, 95% CI: 0.92-0.96 for predicting in-hospital 30-day mortality.

Lung transplantation for post-COVID-19 pulmonary fibrosis.

COVID-19 has affected over a billion people around the world, with over 2 million losing their lives (Worldometer). About 10% of patients infected with COVID-19 develop a serious illness, including respiratory failure, that require advanced life-supporting measures. Mortality among this subgroup exceeds 60%. We present a case of an otherwise healthy 34-year-old male who developed end-stage pulmonary fibrosis following COVID-19 infection. He achieved haemodynamic stability with mechanical ventilation and extracorporeal membrane oxygenation (ECMO) but did not show any sign of weaning off ECMO; however, he successfully underwent bilateral lung transplantation.

"COVID19 is_": The Perpetuation of Coronavirus Conspiracy Theories via Google Autocomplete.

As the impact of the COVID-19 pandemic grew in 2020, uncertainty surrounding its origins and nature led to widespread conspiracy-related theories (CRT). Use of technological platforms enabled the rapid and exponential dissemination of COVID-19 CRT. This study applies social contagion theory to examine how Google Autocomplete (GA) propagates and perpetuates these CRT. An in-house software program, Autocomplete Search Logging Tool (ASLT) captured a snapshot of GA COVID-19 related searches early in the pandemic (from March to May 2020) across 76 randomly-selected countries to gain insight into search behaviors around the world. Analysis identified 15 keywords relating to COVID-19 CRT predictions and demonstrate how searches across different countries received varying degrees of GA predictions. When grouped with similar keywords, two major categories were identified "Man-Made Biological Weapon" (42%, n = 2,111), and "Questioning Reality/Severity of COVID-19" (44%, n = 2,224). This investigation is also among the first to apply social contagion theory to autocomplete applications and can be used in future research to explain and perhaps mitigate the spread of CRT.

Radio-frequency (RF) room temperature plasma treatment of sweet basil seeds (Ocimum basilicum L.) for germination potential enhancement by immaculation.

Ocimum basilicum L. is an antiviral and immunity boosting medicinal plant and culinary herb. Potential use of sweet basils in COVID 19 prevention and management is making its demand rise. This study is aimed at germination potential enhancement of sweet basil seeds. Reported study is evidenced with scientific data of radio-frequency cold plasma treatment using Ar + O2 feed gas. O. basilicum seeds, placed inside the rotating glass bottle, were directly exposed to RF (13.56 MHz) plasma produced in Ar + O2 feed gas. Seed treatment was done using RF source power (60 W, 150 W, 240 W), process pressure (0.2 mbar, 0.4 mbar, 0.6 mbar), and treatment time (5 min, 10 min, 15 min) at different combinations. Results show that, the most efficient treatment provide up to ∼89 % of the germination percentage which is an enhancement by 32.3 % from the control. SEM images revealed slight shrinkage in the seed size with eroded appearance over the seed. Enhancement of lipid peroxidation, show that oxidation of seed coat may propagate internally. Water imbibition analysis, of the treated seeds, was carried out for 2-12 hours. Further analysis of seed weight, on every one hour, after soaking shows enhanced water absorption capability except the treatment at 240 W, 0.6 mbar and 15 min. Plasma treatment enhanced carbohydrate content and protein content which is reported to be due to increased primary metabolism. Whereas, increased activity of secondary metabolism results in the enhancement of enzymatic (catalase) and non-enzymatic antioxidants (proline). Vital growth parameters, such as SVI I and SVI II, got amplified by 37 % and 133 % respectively after treatment. Ameliorative effects of plasma treatment are found highly significant with a positive and significant correlation value (p < 0.01) between germination percentages, SVI I, SVI II, carbohydrate, protein and proline show their interrelationship. Ar + O2 plasma treatment is found to bring forth significant changes in the O. basilicum seeds which eventually enhanced the germination potential and it could be a very promising technology for the medicinal crop.

Development of a framework of intervention strategies for point of care quality improvement at different levels of healthcare delivery system in India: initial lessons.

BACKGROUND: Inadequate quality of care has been identified as one of the most significant challenges to achieving universal health coverage in low-income and middle-income countries. To address this WHO-SEARO, the point of care quality improvement (POCQI) method has been developed. This paper describes developing a dynamic framework for the implementation of POCQI across India from 2015 to 2020. METHODS: A total of 10 intervention strategies were designed as per the needs of the local health settings. These strategies were implemented across 10 states of India, using a modification of the 'translating research in practice' framework. Healthcare professionals and administrators were trained in POCQI using a combination of onsite and online training methods followed by coaching and mentoring support. The implementation strategy changed to a fully digital community of practice platform during the active phase of the COVID-19 pandemic. Dashboard process, outcome indicators and crude cost of implementation were collected and analysed across the implementation sites. RESULTS: Three implementation frameworks were evolved over the study period. The combined population benefitting from these interventions was 103 million. A pool of QI teams from 131 facilities successfully undertook 165 QI projects supported by a pool of 240 mentors over the study period. A total of 21 QI resources and 6 publications in peer-reviewed journals were also developed. The average cost of implementing POCQI initiatives for a target population of one million was US$ 3219. A total of 100 online activities were conducted over 6 months by the digital community of practice. The framework has recently extended digitally across the South-East Asian region. CONCLUSION: The development of an implementation framework for POCQI is an essential requirement for the initiative's successful country-wide scale. The implementation plan should be flexible to the healthcare system's needs, target population and the implementing agency's capacity and amenable to multiple iterative changes.

Autonomous Robotic Point-of-Care Ultrasound Imaging for Monitoring of COVID-19-Induced Pulmonary Diseases.

The COVID-19 pandemic has emerged as a serious global health crisis, with the predominant morbidity and mortality linked to pulmonary involvement. Point-of-Care ultrasound (POCUS) scanning, becoming one of the primary determinative methods for its diagnosis and staging, requires, however, close contact of healthcare workers with patients, therefore increasing the risk of infection. This work thus proposes an autonomous robotic solution that enables POCUS scanning of COVID-19 patients' lungs for diagnosis and staging. An algorithm was developed for approximating the optimal position of an ultrasound probe on a patient from prior CT scans to reach predefined lung infiltrates. In the absence of prior CT scans, a deep learning method was developed for predicting 3D landmark positions of a human ribcage given a torso surface model. The landmarks, combined with the surface model, are subsequently used for estimating optimal ultrasound probe position on the patient for imaging infiltrates. These algorithms, combined with a force-displacement profile collection methodology, enabled the system to successfully image all points of interest in a simulated experimental setup with an average accuracy of 20.6 ± 14.7 mm using prior CT scans, and 19.8 ± 16.9 mm using only ribcage landmark estimation. A study on a full torso ultrasound phantom showed that autonomously acquired ultrasound images were 100% interpretable when using force feedback with prior CT and 88% with landmark estimation, compared to 75 and 58% without force feedback, respectively. This demonstrates the preliminary feasibility of the system, and its potential for offering a solution to help mitigate the spread of COVID-19 in vulnerable environments.

LungINFseg: Segmenting COVID-19 Infected Regions in Lung CT Images Based on a Receptive-Field-Aware Deep Learning Framework.

COVID-19 is a fast-growing disease all over the world, but facilities in the hospitals are restricted. Due to unavailability of an appropriate vaccine or medicine, early identification of patients suspected to have COVID-19 plays an important role in limiting the extent of disease. Lung computed tomography (CT) imaging is an alternative to the RT-PCR test for diagnosing COVID-19. Manual segmentation of lung CT images is time consuming and has several challenges, such as the high disparities in texture, size, and location of infections. Patchy ground-glass and consolidations, along with pathological changes, limit the accuracy of the existing deep learning-based CT slices segmentation methods. To cope with these issues, in this paper we propose a fully automated and efficient deep learning-based method, called LungINFseg, to segment the COVID-19 infections in lung CT images. Specifically, we propose the receptive-field-aware (RFA) module that can enlarge the receptive field of the segmentation models and increase the learning ability of the model without information loss. RFA includes convolution layers to extract COVID-19 features, dilated convolution consolidated with learnable parallel-group convolution to enlarge the receptive field, frequency domain features obtained by discrete wavelet transform, which also enlarges the receptive field, and an attention mechanism to promote COVID-19-related features. Large receptive fields could help deep learning models to learn contextual information and COVID-19 infection-related features that yield accurate segmentation results. In our experiments, we used a total of 1800+ annotated CT slices to build and test LungINFseg. We also compared LungINFseg with 13 state-of-the-art deep learning-based segmentation methods to demonstrate its effectiveness. LungINFseg achieved a dice score of 80.34% and an intersection-over-union (IoU) score of 68.77%-higher than the ones of the other 13 segmentation methods. Specifically, the dice and IoU scores of LungINFseg were 10% better than those of the popular biomedical segmentation method U-Net.

Least Expensive Facial Mask Against Corona Virus for Short Airway Procedures Generating Aerosol.

With ongoing Corona-pandemic, the quality of personal protection equipment (PPE) across the globe is creating controversy. This article presents a novel design of a facial mask that seems suitable to deal with short airway procedures protecting the surgeon from aerosol infection. The concept, design advantages and limitations are discussed. In absence of good quality PPEs this is an excellent option to deal with airway emergencies.