ABSTRACT
The Corona Virus Disease 2019 (COVID-19) pandemic has taught us many valuable lessons regarding the importance of our physical and mental health. Even with so many technological advancements, we still lag in developing a system that can fully digitalize the medical data of each individual and make it readily accessible for both the patient and health worker at any point in time. Moreover, there are also no ways for the government to identify the legitimacy of a particular clinic. This study merges modern technology with traditional approaches, thereby highlighting a scenario where artificial intelligence (AI) merges with traditional Chinese medicine (TCM), proposing a way to advance the conventional approaches. The main objective of our research is to provide a one-stop platform for the government, doctors, nurses, and patients to access their data effortlessly. The proposed portal will also check the doctors' authenticity. Data is one of the most critical assets of an organization, so a breach of data can risk users' lives. Data security is of primary importance and must be prioritized. The proposed methodology is based on cloud computing technology which assures the security of the data and avoids any kind of breach. The study also accounts for the difficulties encountered in creating such an infrastructure in the cloud and overcomes the hurdles faced during the project, keeping enough room for possible future innovations. To summarize, this study focuses on the digitalization of medical data and suggests some possible ways to achieve it. Moreover, it also focuses on some related aspects like security and potential digitalization difficulties.Copyright © 2022 Digital Chinese Medicine
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SESSION TITLE: Lung Cancer Assessment and Risk Calculations SESSION TYPE: Rapid Fire Original Inv PRESENTED ON: 10/19/2022 11:15 am - 12:15 pm PURPOSE: Detection of pulmonary nodules is central to early diagnosis of lung cancer. Many types of observer error have been reported in literature. During the pandemic the radiology departments were stretched, and it is possible that the radiologists were primarily looking to either confirm or rule out COVID and missed nodules due to satisfaction of search and fatigue. This study was undertaken to estimate the magnitude of such errors. METHOD: 1312 consecutive chest CT scans interpreted in two specialist radiology units during the months of May-Jun 2021 (peak of India's 2nd covid wave) were used in this study. All scans were processed by qCT-Lung, a deep learning algorithm capable of flagging nodules. The radiology reports of the cases flagged by qCT were searched for findings suggestive of lung cancer. Cases for which nodules or mass were not mentioned in the report were re-read by an independent radiologist with qCT's assistance. The radiologist was aware of the context and was asked to mark flagged lesions as Yes/no for nodules. They also rated the nodule for malignancy potential in a positive directed five-point Likert scale if the flag was correct and gave an alternative finding if incorrect. RESULTS: 381 (29.0%) scans were flagged for nodules by qCT. 52 of these were also reported in radiology reports. Of the 329 scans re-read by radiologist, 65 (19.8 %) scans were reported as having nodules. These 65 scans had a total of 134 nodules. The median size (longest diameter) of these nodules was 14 mm (range: 7 - 33) and most (95) were solid nodules. The most common reason (94.5%) of false flag by qCT was ground-glass opacity with consolidation. 53 of 65 of the correctly identified scans were given malignancy rating of 1 or 2 (non-malignant or probably non-malignant). 10 were given a rating of 3 (could be malignant or non-malignant) and 2 were rated as probably malignant. Two cases of lung cancers were flagged by both qCT and reports. CONCLUSIONS: Nodules were not reported in 65 scans. All of them can't be considered as missed by radiologists. From the ratings provided by the radiologist doing re-reads - it is possible that previous radiologists could have not reported the nodule due to perceived malignancy risk. 12 scans had ratings that would have warranted follow-up action. False flags were mainly due to ground glass with consolidation. The occurrence of ground glass opacities in COVID could in part explain the high number of false flags in this study. CLINICAL IMPLICATIONS: All scans had nodules with size > 6 mm and 26 had multiple nodules making the patients eligible for follow-up as per Fleischner guideline. Radiologist report only clinically significant findings focussing mostly on indication(s) of the scan. Taking a conservative approach and reporting all nodules irrespective of perceived risk will help clinicians plan follow-up. Algorithms like qCT-Lung can help readers in identifying all nodules. DISCLOSURES: Employee relationship with Qure.ai Please note: 15/12/2020 Added 04/04/2022 by Vikash Challa, value=Salary Removed 04/04/2022 by Vikash Challa Employee relationship with Qure.ai Please note: Current employee Added 04/04/2022 by Vikash Challa, value=Salary Owner/Founder relationship with bodyScans Please note: Current co-founder Added 04/05/2022 by Era Dwivedi, value=Salary Employee relationship with Aarthi Scans and Labs Please note: Current Employee Added 04/04/2022 by Arunkumar Govindarajan, value=Salary Owner/Founder relationship with bodyScans Please note: Current Director Added 04/05/2022 by Arpit Kothari, value=Salary Employee relationship with Qure.ai Please note: Current employee Added 04/04/2022 by Souvik Mandal, value=Salary Employee relationship with Qure.ai Please note: Current employee Added 04/04/2022 by Ankit Modi, value=Salary Self relationship with Qure.ai Please note: Current employee Added 04/04/2022 by SAI NAREN V S, value=Salary Employee elationship with Qure AI Please note: Current Employee Added 04/04/2022 by Vanapalli Prakash, value=Salary Employee relationship with Qure.ai Please note: Current employee Added 04/07/2022 by Preetham Putha, value=Salary Employee relationship with Qure.ai Please note: 4 years Added 04/04/2022 by Bhargava Reddy, value=Salary Employee relationship with Qure.ai Please note: Current Employee Added 03/27/2022 by Saigopal Sathyamurthy, value=Salary Owner/Founder relationship with Qure.ai Please note: Current co-founder Added 04/04/2022 by Prashant Warier, value=Salary
ABSTRACT
SARS-CoV2 has encompassed showing symptoms from acute respiratory distress syndrome to minor symptoms like loss of smell, taste, fever, body ache. This paper, explains the brain activity observed, if loss of smell (Olfaction) persists. If the symptoms are treated as early biomarker will enable earlier diagnosis and preventative treatments of syndromes. The proposed framework suggests a portable, easy to deploy noninvasive method to detect olfactory dysfunctions at the COVID test center. The validation of the parameters under clinical expertise has laid a ground to predict and proper assess of olfactory deficits in a patient within 20 minutes. The selection of hyper parameters was done using RBF kernel. The test is steered using a simple neuro-imaging, non-invasive device gathering the EBG waves, essentially gamma waves received from the olfactory nerve present in the upper nostril. The results impress to establish a base, that a decreased sense of smell may be a pointer to patients in the initial stage of the syndrome. The statistical validator, Fisher's exact test is performed for data analyses taken from neuroimaging device. The statistical significance was defined as P <.05 for the anosmia (loss of smell). The P- value calculated through our experimental setup is 0.008 for anosmia proved as a significant factor for the detection of infection. © 2022 IEEE.
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SARS-COV-2, also known as COVID-19 pandemic, has escalated calamity in the entire world. Due to its contagious properties, the disease spreads swiftly from person to person via direct contact. More than 210 million people got infected worldwide with more than 18 million active patients as of August 29, 2021. In numerous places, the test process for COVID-19 detection takes longer than 2 days. Once the patient is affected by COVID-19, the obstruction in lungs causes difficulty in analyzing the presence of other lung diseases, such as variants of pneumonia. In this paper, we propose an enhancement technique via the acclaimed signal processing method called variational mode decomposition (VMD) aiding any X-ray image classification method for the detection of pneumonia using convolutional neural networks (CNN). The experiments were conducted on VGG-16 model loaded with ImageNet weights followed by numerous configurations of dense layers. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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The impact of covid-19 on the financial market is considered a ‘black swan event’, i.e., the occurrence of a highly unpredictable event with far-reaching consequences. Prediction of such events in prior is essential due to the financial risk associated. In this paper, we study critical slowing down as an early warning signal to forewarn such unpredictable and sudden transitions concerning the Indian stock market for the covid-19 period. This is the first study to predict covid-19 financial crisis based on critical slowing down theory. We analyze the evolution of first-order autocorrelation and standard deviation using the sliding window approach to predict any impending transition. We found that both the early warning measures could forewarn an impending transition for almost all the stock indices considered for the analysis. © 2022, Springer Nature Switzerland AG.
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The current pandemic of COVID-19, with its climbing number of cases and deaths, has us searching for tools for rapid, reliable, and affordable methods of detection on one hand, and novel, improved therapeutic strategies on the other. The currently employed RT-PCR method, despite its all-encompassing utility, has its shortcomings. Newer diagnostic tools, based on the Clustered Regularly Interspaced Short Palindromic Repeats/Cas(CRISPR-Cas) system, with its better diagnostic accuracy measures, have come up to fill that void. These assay platforms are expected to slowly take up the place of COVID-19 diagnostics. Further, the current therapeutic options focus mainly on counteracting the viral proteins and components and their entry into host cells. The CRISPR-based system, especially through the RNA-guided Cas13 approach, can identify the genomic characteristics of SARS-CoV-2 and provide a novel inhibition strategy for coronaviruses. In this mini-review, we have discussed the available and upcoming CRISPR-based diagnostic assays and the potential of the CRISPR/Cas system as a therapeutic or prevention strategy in COVID-19. CRISPR-Cas system shows promise in both diagnostics as well as therapeutics and may as well change the face of molecular diagnosis and precision medicine.
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This review presents a detailed overview of COVID-19 pandemic on dental prac-tice. Guidelines issued by authorities/agencies for COVID-19 and identical guidelines that are applicable for similar outbreaks in the past are discussed. The outbreak has caused unexpected disruption to dental practice and has changed the way dentistry will be foreseen in the near future. It is therefore imperative to study the nature, characteristics and transmission of COVID-19 during emergency dental procedures. In addition to this, management of infection control, precautionary measures during preoperative, intraoperative and postoperative dental care and contingency management when con-tacted by virus are of prime importance not only for dental practice but also for dental education. © 2020 Nova Science Publishers, Inc.