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1.
Cancer Research, Statistics, and Treatment ; 4(2):262-269, 2021.
Article in English | EMBASE | ID: covidwho-20233241

ABSTRACT

Background: Patients with cancer are at a higher risk of getting infected with the severe acute respiratory syndrome coronavirus 2 owing to their immunocompromised state. Providing care to these patients amidst the first wave of the coronavirus disease-2019 (COVID-19) pandemic was extremely challenging. Objective(s): This study was aimed at evaluating the clinical profile and disease-related outcomes of pediatric patients with hematological illnesses and cancer. Material(s) and Method(s): This retrospective study was conducted at a tertiary care center in North India during the first wave of the pandemic from March 2020 to December 2020. Children aged up to 18 years, who were treated for a hematological illness or malignancy or underwent hematopoietic stem cell transplantation (HSCT) and tested positive for COVID-19 regardless of symptoms were included in the study. Baseline demographic data related to the age, diagnosis, treatment status, and chemotherapy protocol used were collected. Outcomes including the cure rates, comorbidities, and sequelae were recorded. Result(s): A total of 650 tests for COVID-19 were performed for 181 children;22 patients were found to be COVID-19 positive. The most common diagnosis was acute leukemia (63.6%). None of the patients developed COVID-19 pneumonia. The majority of patients had asymptomatic infection and were managed at home. Among those with a symptomatic infection, the most common symptoms were fever and cough. A total of 3 (13.6%) patients needed oxygen therapy, one developed multisystem inflammatory syndrome of children leading to cardiogenic shock. Three patients required intensive care or respiratory support;all the patients had favorable clinical outcomes. The median time from the onset of COVID-19 to a negative result on the reverse transcription-polymerase chain reaction test was 21.3 days. Cancer treatment was modified in 15 patients (68.2%). Conclusion(s): Our results suggest that children with hemato-oncological illnesses rarely experience severe COVID-19 disease. The impact of the first wave of COVID-19 primarily manifested as disruptions in the logistic planning and administration of essential treatment to these children rather than COVID-19 sequelae.Copyright © 2021 Cancer Research, Statistics, and Treatment Published by Wolters Kluwer - Medknow.

2.
Cancer Research, Statistics, and Treatment ; 4(3):592, 2021.
Article in English | EMBASE | ID: covidwho-20233240
3.
Ieee Access ; 11:595-645, 2023.
Article in English | Web of Science | ID: covidwho-2311192

ABSTRACT

Biomedical image segmentation (BIS) task is challenging due to the variations in organ types, position, shape, size, scale, orientation, and image contrast. Conventional methods lack accurate and automated designs. Artificial intelligence (AI)-based UNet has recently dominated BIS. This is the first review of its kind that microscopically addressed UNet types by complexity, stratification of UNet by its components, addressing UNet in vascular vs. non-vascular framework, the key to segmentation challenge vs. UNet-based architecture, and finally interfacing the three facets of AI, the pruning, the explainable AI (XAI), and the AI-bias. PRISMA was used to select 267 UNet-based studies. Five classes were identified and labeled as conventional UNet, superior UNet, attention-channel UNet, hybrid UNet, and ensemble UNet. We discovered 81 variations of UNet by considering six kinds of components, namely encoder, decoder, skip connection, bridge network, loss function, and their combination. Vascular vs. non-vascular UNet architecture was compared. AP(ai)Bias 2.0-UNet was identified in these UNet classes based on (i) attributes of UNet architecture and its performance, (ii) explainable AI (XAI), and, (iii) pruning (compression). Five bias methods such as (i) ranking, (ii) radial, (iii) regional area, (iv) PROBAST, and (v) ROBINS-I were applied and compared using a Venn diagram. Vascular and non-vascular UNet systems dominated with sUNet classes with attention. Most of the studies suffered from a low interest in XAI and pruning strategies. None of the UNet models qualified to be bias-free. There is a need to move from paper-to-practice paradigms for clinical evaluation and settings.

4.
Pediatric Hematology Oncology Journal ; 7(3):96-102, 2022.
Article in English | EMBASE | ID: covidwho-1926834

ABSTRACT

Introduction: Indian subcontinent witnessed first wave of COVID-19 around March 2020 and second wave in April 2021. The mutant delta variant was ≈2.5 times more transmissible and led to the severe second wave. We compared the impact of two waves on pediatric hematology and oncology patients at our tertiary care centre that was at heart of managing COVID-19. Methods: Children between 0 and 18 years, who were treated for a haematological illness, malignancy or stem cell transplant with confirmed COVID-19 infection or who developed multisystem inflammatory syndrome in children were included. Results: A total of 48 (22-first, 26-second wave) children were evaluated. Despite better understanding of disease and standardised management algorithms, we found a trend towards younger age, increased requirements of oxygen, severe pneumonia and other post-covid complications in admitted patients during the second wave. We observed early RTPCR negativity in second wave. Invasive aspergillosis, disseminated candidiasis, reactivation of tuberculosis, HLH and MISC were the main complications. No child died of COVID-19. Conclusion: The second wave hit pediatric hematology and oncology patients harder than the first wave.COVID-19 infection in these patients may lead to significant morbidity and complications that interfere with treatment of their primary illnesses. They need close monitoring for development of life threatening infections. Early recognition and prompt therapy can optimise outcomes.

5.
IEEE Transactions on Instrumentation and Measurement ; : 1-1, 2022.
Article in English | Scopus | ID: covidwho-1909267

ABSTRACT

Coronavirus 2019 (COVID-19) has led to a global pandemic infecting 224 million people and has caused 4.6 million deaths. Nearly 80 Artificial Intelligence (AI) articles have been published on COVID-19 diagnosis. The first systematic review on the Deep Learning (DL)-based paradigm for COVID-19 diagnosis was recently published by Suri et al. [IEEE J Biomed Health Inform. 2021]. The above study used AtheroPoint’s “AP(ai)Bias 1.0”using 10 AI attributes in the DL framework. The proposed study uses “AP(ai)Bias 2.0”as part of the three quantitative paradigms for Risk-of-Bias quantification by using the best 40 dedicated Hybrid DL (HDL) studies and utilizing 39 AI attributes. In the first method, the radial-bias map (RBM) was computed for each AI study, followed by the computation of bias value. In the second method, the regional-bias area (RBA) was computed by the area difference between the best and the worst AI performing attributes. In the third method, ranking-bias score (RBS) was computed, where AI-based cumulative scores were computed for all the 40 studies. These studies were ranked, and the cutoff was determined, categorizing the HDL studies into three bins: low, moderate, and high. Using the Venn diagram, these three quantitative methods were benchmarked against the two qualitative non-randomized-based AI trial methods (ROBINS-I and PROBAST). Using the analytically derived moderate-high and low-moderate cutoff of 2.9 and 3.6, respectively, we observed 40%, 27.5%, 17.5%, 10%, and 20% of studies were low-biased for RBM, RBA, RBS, ROBINS-I, and PROBAST, respectively. We present an eight-point recommendation for AP(ai)Bias 2.0 minimization. IEEE

6.
INDIAN JOURNAL OF CRITICAL CARE MEDICINE ; 26(6), 2022.
Article in English | Web of Science | ID: covidwho-1904487

ABSTRACT

Children with malignancies are facing new challenges in post-COVID-19 era. We report an interesting case of a child on treatment for acute lymphoblastic leukemia having a very protracted course of illness with complications not often seen with standard therapy. It intends to make pediatric oncologists and intensive care specialists wary of potential newer complications.

7.
Indian Journal of Forensic Medicine and Toxicology ; 16(1):781-786, 2022.
Article in English | EMBASE | ID: covidwho-1727484

ABSTRACT

The development of communication technologies has a dramatic influence on culture. The internet, cell phones and e-mail are different domains, and if health care professionals want to join this space, they will be careful to do so. Telemedicine has medical-legal implications for aspects of identification, licensing, insurance, protection, privacy and confidentiality, as well as other risks related to online healthcare communication. The International Advisory Group of the World Health Organization (WHO), which met in Geneva in 1997, identified telemedicine as providing healthcare services, where distance is a critical factor, to health care providers, who use the information and communications technologies to exchange relevant information for the diagnosis, treatment and prevention of diseases and injuries, and to continue to do so. In the context of the COVID-19 Pandemic Lockdown, the Indian Government has authorized telemedicine legal status. The Governing board, established by the Government at a meeting of the Indian Medical Council with the Ministry of Health and Family Welfare’s approval, published a notice dated 25 March 2020 (‘Amendment’) modifying Guidelines of the Indian Medical Council 2002 on telemedicine in India. The amendment introduced ‘Telemedicine consultation’ to the law. A basic knowledge of how medical negligence compensation is calculated and adjudicated in the judicial courts of India. The paper concludes with an assessment of the rules. This paper will seek to determine whether binding arbitration is the best possible solution to resolving malpractice disputes, or whether traditional litigation, while costly, is the safest choice. To do this, the paper will examine both the advantages and the disadvantages associated with using arbitration as opposed to litigation.

9.
12th International Workshop on Machine Learning in Medical Imaging, MLMI 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 ; 12966 LNCS:467-476, 2021.
Article in English | Scopus | ID: covidwho-1469663

ABSTRACT

The pandemic of coronavirus disease 2019 (COVID-19) has severely impacted the world. Several studies suggest an increased risk for COVID-19 patients with underlying cardiovascular diseases (CVD). However, it is challenging to quantify such risk factors and integrate them into patient condition evaluation. This paper presents machine learning methods to assess CVD risk scores from chest computed tomography together with laboratory data, demographics, and deep learning extracted lung imaging features to increase the outcome prediction accuracy for COVID-19 patients. The experimental results demonstrate an overall increase in prediction performance when the CVD severity score was added to the feature set. The machine learning methods obtained their best performance when all categories of the features were used for the patient outcome prediction. With the best attained area under the curve of 0.888, the presented research may assist physicians in clinical decision-making process on managing COVID-19 patients. © 2021, Springer Nature Switzerland AG.

10.
Clin Radiol ; 76(5): 392.e1-392.e9, 2021 05.
Article in English | MEDLINE | ID: covidwho-1101168

ABSTRACT

AIM: To assess differences in qualitative and quantitative parameters of pulmonary perfusion from dual-energy computed tomography (CT) pulmonary angiography (DECT-PA) in patients with COVID-19 pneumonia with and without pulmonary embolism (PE). MATERIALS AND METHODS: This retrospective institutional review board-approved study included 74 patients (mean age 61±18 years, male:female 34:40) with COVID-19 pneumonia in two countries (one with 68 patients, and the other with six patients) who underwent DECT-PA on either dual-source (DS) or single-source (SS) multidetector CT machines. Images from DS-DECT-PA were processed to obtain virtual mono-energetic 40 keV (Mono40), material decomposition iodine (MDI) images and quantitative perfusion statistics (QPS). Two thoracic radiologists determined CT severity scores based on type and extent of pulmonary opacities, assessed presence of PE, and pulmonary parenchymal perfusion on MDI images. The QPS were calculated from the CT Lung Isolation prototype (Siemens). The correlated clinical outcomes included duration of hospital stay, intubation, SpO2 and death. The significance of association was determined by receiver operating characteristics and analysis of variance. RESULTS: One-fifth (20.2%, 15/74 patients) had pulmonary arterial filling defects; most filling defects were occlusive (28/44) located in the segmental and sub-segmental arteries. The parenchymal opacities were more extensive and denser (CT severity score 24±4) in patients with arterial filling defects than without filling defects (20±8; p=0.028). Ground-glass opacities demonstrated increased iodine distribution; mixed and consolidative opacities had reduced iodine on DS-DECT-PA but increased or heterogeneous iodine content on SS-DECT-PA. QPS were significantly lower in patients with low SpO2 (p=0.003), intubation (p=0.006), and pulmonary arterial filling defects (p=0.007). CONCLUSION: DECT-PA QPS correlated with clinical outcomes in COVID-19 patients.


Subject(s)
COVID-19/complications , COVID-19/diagnostic imaging , Computed Tomography Angiography/methods , Lung/diagnostic imaging , Pulmonary Artery/diagnostic imaging , Pulmonary Embolism/diagnostic imaging , Adult , Aged , Aged, 80 and over , COVID-19/therapy , Contrast Media , Female , Hospital Mortality , Humans , Iodine , Length of Stay , Lung/blood supply , Male , Middle Aged , Pulmonary Circulation , Pulmonary Embolism/etiology , Respiration, Artificial , Retrospective Studies , SARS-CoV-2 , Severity of Illness Index , Young Adult
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