SARS-CoV2 pandemic exposed the limitations of artificial intelligence based medical imaging systems. Earlier in the pandemic, the absence of sufficient training data prevented effective deep learning (DL) solutions for the diagnosis of COVID-19 based on X-Ray data. Here, addressing the lacunae in existing literature and algorithms with the paucity of initial training data; we describe CovBaseAI, an explainable tool using an ensemble of three DL models and an expert decision system (EDS) for COVID-Pneumonia diagnosis, trained entirely on pre-COVID-19 datasets. The performance and explainability of CovBaseAI was primarily validated on two independent datasets. Firstly, 1401 randomly selected CxR from an Indian quarantine center to assess effectiveness in excluding radiological COVID-Pneumonia requiring higher care. Second, curated dataset; 434 RT-PCR positive cases and 471 non-COVID/Normal historical scans, to assess performance in advanced medical settings. CovBaseAI had an accuracy of 87% with a negative predictive value of 98% in the quarantine-center data. However, sensitivity was 0.66–0.90 taking RT-PCR/radiologist opinion as ground truth. This work provides new insights on the usage of EDS with DL methods and the ability of algorithms to confidently predict COVID-Pneumonia while reinforcing the established learning; that benchmarking based on RT-PCR may not serve as reliable ground truth in radiological diagnosis. Such tools can pave the path for multi-modal high throughput detection of COVID-Pneumonia in screening and referral.
This paper reviews the first NTIRE challenge on video super-resolution (restoration of rich details in lowresolution video frames) with focus on proposed solutions and results. A new REalistic and Diverse Scenes dataset (REDS) was employed. The challenge was divided into 2 tracks. Track 1 employed standard bicubic downscaling setup while Track 2 had realistic dynamic motion blurs. Each competition had 124 and 104 registered participants. There were total 14 teams in the final testing phase. They gauge the state-of-the-art in video super-resolution.
Background and objectivesRecent randomized controlled trials (RCTs) have indicated potential therapeutic benefits with high-dose dexamethasone (HDD) or tocilizumab (TCZ) plus standard care in moderate to severe coronavirus disease 2019 (COVID-19) with acute respiratory distress syndrome (ARDS). No study has compared these two against each other. We aimed to compare the efficacy and safety of HDD against TCZ in moderate to severe COVID-ARDS.
MethodsPatients admitted with moderate to severe COVID-19 ARDS with clinical worsening within 48 hours of standard care were randomly assigned to receive either HDD or TCZ plus standard care. The primary outcome was ventilator-free days (VFDs) at 28 days. The main secondary outcomes were 28-day all-cause mortality and the incidence of adverse events. Our initial plan was to perform an interim analysis of the first 42 patients.
ResultsVFDs were significantly lower in the HDD arm (median difference: 28 days; 95% confidence interval (CI): 19.35-36.65; Cohen's d = 1.14; p < 0.001). We stopped the trial at the first interim analysis due to high 28-day mortality in the HDD arm (relative risk (RR) of death: 6.5; p = 0.007; NNT (harm) = 1.91). The incidence of secondary infections was also significantly high in the HDD arm (RR: 5.5; p = 0.015; NNT (harm) = 2.33).
ConclusionsIn our study population, HDD was associated with a very high rate of mortality and adverse events. We would not recommend HDD to mitigate the cytokine storm in moderate to severe COVID-19 ARDS. TCZ appears to be a much better and safer alternative.
Segmentation is an important step for developing any optical character recognition (OCR) system, which has to be redesigned for each script having, non-uniform nature/property. It is used to decompose the image into its sub-units, which act as a basis for character recognition. Brahmi is a non-cursive ancient script, in which characters are not attached to each other and have some spacing between them. This study analyses various segmentation methods for different scripts to develop the best suitable segmentation method for Brahmi. MATLAB software was used for segmentation purpose in the experiment. The sample data belongs to Brahmi script-based ‘Rumandei inscription’. In this paper, we discuss a segmentation methodology for distinct components, namely text lines, words and characters of Rumandei inscription, written in Brahmi script. For segmenting distinct components of inscription different approach were used like horizontal projection profile, vertical projection profile and Relative minima approach. This is fundamental research on an inscription based on Brahmi script, which acts as a foundation for developing a segmentation module of an OCR solution/system of similar scripts in future. Information search and retrieval is an important activity of a library. So, to ensure this support for digitised documents written in ancient script, their character recognition is mandatory through the OCR system.
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