New International Guidelines and Consensus on the Use of Lung Ultrasound

Journal of Ultrasound in Medicine, Journal Year: 2022, Volume and Issue: 42(2), P. 309 - 344

Published: Aug. 22, 2022

Following the innovations and new discoveries of last 10 years in field lung ultrasound (LUS), a multidisciplinary panel international LUS experts from six countries different fields (clinical technical) reviewed updated original consensus for point-of-care LUS, dated 2012. As result, total 20 statements have been produced. Each statement is complemented by guidelines future developments proposals. The are furthermore classified based on their nature as technical (5), clinical (11), educational (3), safety (1) statements.

Language: Английский

---

Automated detection and forecasting of COVID-19 using deep learning techniques: A review

Neurocomputing, Journal Year: 2024, Volume and Issue: 577, P. 127317 - 127317

Published: Jan. 26, 2024

Language: Английский

---

Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review

arXiv (Cornell University), Journal Year: 2020, Volume and Issue: unknown

Published: Jan. 1, 2020

Coronavirus, or COVID-19, is a hazardous disease that has endangered the health of many people around world by directly affecting lungs. COVID-19 medium-sized, coated virus with single-stranded RNA, and also one largest RNA genomes approximately 120 nm. The X-Ray computed tomography (CT) imaging modalities are widely used to obtain fast accurate medical diagnosis. Identifying from these images extremely challenging as it time-consuming prone human errors. Hence, artificial intelligence (AI) methodologies can be consistent high performance. Among AI methods, deep learning (DL) networks have gained popularity recently compared conventional machine (ML). Unlike ML, all stages feature extraction, selection, classification accomplished automatically in DL models. In this paper, complete survey studies on application techniques for diagnostic segmentation lungs discussed, concentrating works CT images. Additionally, review papers forecasting coronavirus prevalence different parts presented. Lastly, challenges faced detection using directions future research discussed.

Language: Английский

---

Artificial Intelligence and Internet of Things (AI-IoT) Technologies in Response to COVID-19 Pandemic: A Systematic Review

IEEE Access, Journal Year: 2022, Volume and Issue: 10, P. 62613 - 62660

Published: Jan. 1, 2022

The origin of the COVID-19 pandemic has given overture to redirection, as well innovation many digital technologies. Even after progression vaccination efforts across globe, total eradication this is still a distant future due evolution new variants. To proactively deal with pandemic, health care service providers and caretaker organizations require technologies, alongside improvements in existing related Internet Things (IoT), Artificial Intelligence (AI), Machine Learning terms infrastructure, efficiency, privacy, security. This paper provides an overview current theoretical application prospects IoT, AI, cloud computing, edge deep learning techniques, blockchain social networks, robots, machines, security techniques. In consideration these intersection we reviewed technologies within broad umbrella AI-IoT most concise classification scheme. review, illustrated that technological applications innovations have impacted field healthcare. essential found for healthcare were fog computing learning, blockchain. Furthermore, highlighted several aspects their impact novel methodology using techniques from image processing, machine differential system modeling.

Language: Английский

---

Detection of COVID-19 in smartphone-based breathing recordings: A pre-screening deep learning tool

PLoS ONE, Journal Year: 2022, Volume and Issue: 17(1), P. e0262448 - e0262448

Published: Jan. 13, 2022

This study was sought to investigate the feasibility of using smartphone-based breathing sounds within a deep learning framework discriminate between COVID-19, including asymptomatic, and healthy subjects. A total 480 (240 shallow 240 deep) were obtained from publicly available database named Coswara. These recorded by 120 COVID-19 subjects via smartphone microphone through website application. proposed herein that relies on hand-crafted features extracted original recordings mel-frequency cepstral coefficients (MFCC) as well deep-activated learned combination convolutional neural network bi-directional long short-term memory units (CNN-BiLSTM). The statistical analysis patient profiles has shown significant difference (p-value: 0.041) for ischemic heart disease Analysis normal distribution combined MFCC values showed tended have is skewed more towards right side zero mean (shallow: 0.59±1.74, deep: 0.65±4.35, p-value: <0.001). In addition, approach had an overall discrimination accuracy 94.58% 92.08% recordings, respectively. Furthermore, it detected successfully with maximum sensitivity 94.21%, specificity 94.96%, area under receiver operating characteristic (AUROC) curves 0.90. Among participants, asymptomatic (18 subjects) 100.00% 88.89% recordings. paves way utilizing purpose detection. observations found in this promising suggest effective pre-screening tool alongside current reverse-transcription polymerase chain reaction (RT-PCR) assay. It can be considered early, rapid, easily distributed, time-efficient, almost no-cost diagnosis technique complying social distancing restrictions during pandemic.

Language: Английский

---

Review of Machine Learning in Lung Ultrasound in COVID-19 Pandemic

Journal of Imaging, Journal Year: 2022, Volume and Issue: 8(3), P. 65 - 65

Published: March 5, 2022

Ultrasound imaging of the lung has played an important role in managing patients with COVID-19-associated pneumonia and acute respiratory distress syndrome (ARDS). During COVID-19 pandemic, ultrasound (LUS) or point-of-care (POCUS) been a popular diagnostic tool due to its unique capability logistical advantages over chest X-ray CT. Pneumonia/ARDS is associated sonographic appearances pleural line irregularities B-line artefacts, which are caused by interstitial thickening inflammation, increase number severity. Artificial intelligence (AI), particularly machine learning, increasingly used as critical that assists clinicians LUS image reading decision making. We conducted systematic review from academic databases (PubMed Google Scholar) preprints on arXiv TechRxiv state-of-the-art learning technologies for images diagnosis. Openly accessible datasets listed. Various architectures have employed evaluate showed high performance. This paper will summarize current development AI management outlook emerging trends combining AI-based robotics, telehealth, other techniques.

Language: Английский

---

A survey on deep learning models for detection of COVID-19

Neural Computing and Applications, Journal Year: 2023, Volume and Issue: 35(23), P. 16945 - 16973

Published: May 27, 2023

Language: Английский

---

Clinical benefit of AI-assisted lung ultrasound in a resource-limited intensive care unit

Critical Care, Journal Year: 2023, Volume and Issue: 27(1)

Published: July 1, 2023

Interpreting point-of-care lung ultrasound (LUS) images from intensive care unit (ICU) patients can be challenging, especially in low- and middle- income countries (LMICs) where there is limited training available. Despite recent advances the use of Artificial Intelligence (AI) to automate many imaging analysis tasks, no AI-enabled LUS solutions have been proven clinically useful ICUs, specifically LMICs. Therefore, we developed an AI solution that assists practitioners assessed its usefulness a low resource ICU.This was three-phase prospective study. In first phase, performance four different clinical user groups interpreting clips assessed. second 57 non-expert clinicians with without aid bespoke tool for interpretation retrospective offline clips. third conducted study ICU 14 were asked carry out examinations 7 our interviewed regarding usability tool.The average accuracy beginners' 68.7% [95% CI 66.8-70.7%] compared 72.2% 70.0-75.6%] intermediate, 73.4% 62.2-87.8%] advanced users. Experts had 95.0% 88.2-100.0%], which significantly better than beginners, intermediate users (p < 0.001). When supported by retrospectively acquired clips, improved their 68.9% 65.6-73.9%] 82.9% 79.1-86.7%], real-time testing, baseline 68.1% 57.9-78.2%] 93.4% 89.0-97.8%], 0.001) when using tool. The time-to-interpret median 12.1 s (IQR 8.5-20.6) 5.0 3.5-8.8), clinicians' confidence level 3 4 tool.AI-assisted help LMIC improve features more accurately, quickly confidently.

Language: Английский

---

Penalized spatial-temporal sensor fusion for detecting and localizing bursts in water distribution systems

Information Fusion, Journal Year: 2025, Volume and Issue: 117, P. 102912 - 102912

Published: Jan. 2, 2025

Language: Английский

---

Medical Imaging-based Artificial Intelligence in Pneumonia: A Narrative Review

Neurocomputing, Journal Year: 2025, Volume and Issue: unknown, P. 129731 - 129731

Published: Feb. 1, 2025

Language: Английский

---