Coronavirus disease (COVID-19) has had a significant impact on global health since the start of the pandemic in 2019. As of June 2022, over 539 million cases have been confirmed worldwide with over 6.3 million deaths as a result. Artificial Intelligence (AI) solutions such as machine learning and deep learning have played a major part in this pandemic for the diagnosis and treatment of COVID-19. In this research, we review these modern tools deployed to solve a variety of complex problems. We explore research that focused on analyzing medical images using AI models for identification, classification, and tissue segmentation of the disease. We also explore prognostic models that were developed to predict health outcomes and optimize the allocation of scarce medical resources. Longitudinal studies were conducted to better understand COVID-19 and its effects on patients over a period of time. This comprehensive review of the different AI methods and modeling efforts will shed light on the role that AI has played and what path it intends to take in the fight against COVID-19.

/pdf/a-comprehensive-review-of-machine-learning-used-to-combat-7pltsvdf.pdf

A Comprehensive Review of Machine Learning Used to Combat COVID-19

3D visualization of hydraulic fractures using micro-seismic monitoring: Methodology and application

Background: The chest radiograph (CXR) is the most frequently performed radiological examination worldwide. The increasing volume of CXRs performed in hospitals causes reporting backlogs and increased waiting times for patients, potentially compromising timely clinical intervention and patient safety. Implementing computer-aided detection (CAD) artificial intelligence (AI) algorithms capable of accurate and rapid CXR reporting could help address such limitations. A novel use for AI reporting is the classification of CXRs as ‘abnormal’ or ‘normal’. This classification could help optimize resource allocation and aid radiologists in managing their time efficiently. Methods: qXR is a CE-marked computer-aided detection (CAD) software trained on over 4.4 million CXRs. In this retrospective cross-sectional pre-deployment study, we evaluated the performance of qXR in stratifying normal and abnormal CXRs. We analyzed 1040 CXRs from various referral sources, including general practices (GP), Accident and Emergency (A&E) departments, and inpatient (IP) and outpatient (OP) settings at East Kent Hospitals University NHS Foundation Trust. The ground truth for the CXRs was established by assessing the agreement between two senior radiologists. Results: The CAD software had a sensitivity of 99.7% and a specificity of 67.4%. The sub-group analysis showed no statistically significant difference in performance across healthcare settings, age, gender, and X-ray manufacturer. Conclusions: The study showed that qXR can accurately stratify CXRs as normal versus abnormal, potentially reducing reporting backlogs and resulting in early patient intervention, which may result in better patient outcomes.

Using Artificial Intelligence to Stratify Normal versus Abnormal Chest X-rays: External Validation of a Deep Learning Algorithm at East Kent Hospitals University NHS Foundation Trust

Artificial intelligence (AI) has been shown to solve several issues affecting COVID-19 diagnosis. This systematic review research explores the impact of AI in early COVID-19 screening, detection, and diagnosis. A comprehensive survey of AI in the COVID-19 literature, mainly in the context of screening and diagnosis, was observed by applying the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Data sources for the years 2020, 2021, and 2022 were retrieved from google scholar, web of science, Scopus, and PubMed, with target keywords relating to AI in COVID-19 screening and diagnosis. After a comprehensive review of these studies, the results found that AI contributed immensely to improving COVID-19 screening and diagnosis. Some proposed AI models were shown to have comparable (sometimes even better) clinical decision outcomes, compared to experienced radiologists in the screening/diagnosing of COVID-19. Additionally, AI has the capacity to reduce physician work burdens and fatigue and reduce the problems of several false positives, associated with the RT-PCR test (with lower sensitivity of 60–70%) and medical imaging analysis. Even though AI was found to be timesaving and cost-effective, with less clinical errors, it works optimally under the supervision of a physician or other specialists.

/pdf/the-capacity-of-artificial-intelligence-in-covid-19-response-309c9wqc.pdf

The Capacity of Artificial Intelligence in COVID-19 Response: A Review in Context of COVID-19 Screening and Diagnosis

Deep learning-based radiological image analysis could facilitate use of chest x-rays as a triaging tool for COVID-19 diagnosis in resource-limited settings. This study sought to determine whether a modified commercially available deep learning algorithm (M-qXR) could risk stratify patients with suspected COVID-19 infections.A dual track clinical validation study was designed to assess the clinical accuracy of M-qXR. The algorithm evaluated all Chest-X-rays (CXRs) performed during the study period for abnormal findings and assigned a COVID-19 risk score. Four independent radiologists served as radiological ground truth. The M-qXR algorithm output was compared against radiological ground truth and summary statistics for prediction accuracy were calculated. In addition, patients who underwent both PCR testing and CXR for suspected COVID-19 infection were included in a co-occurrence matrix to assess the sensitivity and specificity of the M-qXR algorithm.625 CXRs were included in the clinical validation study. 98% of total interpretations made by M-qXR agreed with ground truth (p = 0.25). M-qXR correctly identified the presence or absence of pulmonary opacities in 94% of CXR interpretations. M-qXR's sensitivity, specificity, PPV, and NPV for detecting pulmonary opacities were 94%, 95%, 99%, and 88% respectively. M-qXR correctly identified the presence or absence of pulmonary consolidation in 88% of CXR interpretations (p = 0.48). M-qXR's sensitivity, specificity, PPV, and NPV for detecting pulmonary consolidation were 91%, 84%, 89%, and 86% respectively. Furthermore, 113 PCR-confirmed COVID-19 cases were used to create a co-occurrence matrix between M-qXR's COVID-19 risk score and COVID-19 PCR test results. The PPV and NPV of a medium to high COVID-19 risk score assigned by M-qXR yielding a positive COVID-19 PCR test result was estimated to be 89.7% and 80.4% respectively.M-qXR was found to have comparable accuracy to radiological ground truth in detecting radiographic abnormalities on CXR suggestive of COVID-19. 

https://doi.org/10.1016/j.ibmed.2022.100049

Using artificial intelligence to risk stratify COVID-19 patients based on chest X-ray findings

Combining Surface and Downhole Microseismic Monitoring: A Multi-Well Completion in Oman

Microseismic mapping provides essential information for monitoring effective fluid flow and stimulated reservoir volume, as well as for decision making on fracture operations and well completions. This work presents the microseismic monitoring results of a multiwell completion of a field located in the Sultanate of Oman. Target depth is about 1300 m from ground level. The treatments of three wells were monitored by both a surface array and a downhole array. The surface monitoring array was built using a 50-m inline spacing and 100 m crossline spacing geometry and comprised 1000, 974, and 975 3C geophones when monitoring the TW1, TW2, and TW3 wells, respectively. The downhole monitoring was performed using a 12-level 3C-geophone accelerometer array using 30-m-long interconnects deployed in the nearby well, MW. Both surface and downhole seismic data cover the complete stimulation campaign of all three treatment wells. Surface- and downhole-acquired data benefit from a combined inversion. Surface-acquired horizontal locations reveal the same event geometry as downhole results but with a more focused linear cloud. Mapped events indicate a well-bound stimulation along the vertical dimension while exhibiting predominantly strike-slip mechanisms.

Multi-Well Hydraulic Fracture Monitoring in the Sultanate of Oman Using Surface and Downhole Microseismic Arrays


 
 
 To evaluate intervention of structured reporting after wrong-site surgery that occurred after localization of an incorrect breast biopsy marker.
 
 
 
 An IRB-exempt retrospective database review identified patients who underwent core-needle biopsy of a breast lesion from July 1, 2014 to July 1, 2020. They were divided into three cohorts: 2014 pre-intervention/pre-sentinel, 2017 pre-intervention/post-sentinel, and 2019 post-intervention of structured reports. One hundred reports per cohort were reviewed for documentation of marker and shape. Statistical analysis was performed with mixed-effects logistic regression model and chi-squared test with P < 0.05 considered significant.
 
 
 
 The 2014 cohort consisted of 100 patients with 122 biopsies. Twenty-seven (22.1%) were excluded: 5/122 (4.1%) lesion resolution, 22/122 (18.0%) no documentation whether marker was/was not placed. Of the 95 biopsies remaining, 4/95 (4.2%) had no marker placed, 62/95 (65.3%) reported marker only, and 29/95 (30.5%) reported marker and shape. In the 2017 cohort, 100 patients underwent 108 biopsies. Four/108 (3.7%) were excluded: lesion resolution. Of the 104, 10/104 (9.6%) had no marker placed, 22/104 (21.2%) reported marker only, and 72/104 (69.2%) reported marker and shape. In the 2019 cohort, 100 patients underwent 114 biopsies. Two/114 (1.8%) were excluded: lesion resolution. Of the 112, 3/112 (2.7%) had no marker placed, 3/112 (2.7%) reported marker only, and 106/112 (94.6%) reported marker and shape. The predicted probability of both marker placement and shape described were statistically greater for 2019 compared to the other cohorts (P < 0.05).
 
 
 
 Using structured reports facilitates and improves documentation of breast biopsy markers and may potentially reduce the risk of medical errors.


Structured Reporting: An Intervention to Improve Procedure Documentation in Breast Imaging

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Using artificial intelligence to risk stratify COVID-19 patients based on chest X-ray findings

Combining Surface and Downhole Microseismic Monitoring: A Multi-Well Completion in Oman

Multi-Well Hydraulic Fracture Monitoring in the Sultanate of Oman Using Surface and Downhole Microseismic Arrays

Structured Reporting: An Intervention to Improve Procedure Documentation in Breast Imaging