A Novel Method for COVID-19 Diagnosis Using Artificial Intelligence in Chest X-ray Images

Almalki, Yassir Edrees; Qayyum, Abdul; Irfan, Muhammad; Haider, Noman; Głowacz, Adam; Alshehri, Fahad; Alduraibi, Sharifa Khalid; Alshamrani, Khalaf; Basha, Mohammad Abd Alkhalik; Alduraibi, Alaa Khalid; Saeed, M.K.; Rahman, Saifur

doi:10.3390/healthcare9050522

Cited by 74 publications

(56 citation statements)

References 43 publications

(45 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed algorithm achieved a classification accuracy of 99% on the test dataset [ 22 ]. Another approach, COVID Inception-ResNet model (CoVIRNet), was proposed to diagnose COVID-19 patients using chest X-rays [ 23 ]. This approach used a combination of deep learning and machine learning models and achieved an accuracy of more than 95% [ 23 ].…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Novel Prediction Model for COVID‐19 in Saudi Arabia Based on an LSTM Algorithm

Alkhammash

Algethami

Alshahrani

2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The rapid emergence of the novel SARS-CoV-2 poses a challenge and has attracted worldwide attention. Artificial intelligence (AI) can be used to combat this pandemic and control the spread of the virus. In particular, deep learning-based time-series techniques are used to predict worldwide COVID-19 cases for short-term and medium-term dependencies using adaptive learning. This study aimed to predict daily COVID-19 cases and investigate the critical factors that increase the transmission rate of this outbreak by examining different influential factors. Furthermore, the study analyzed the effectiveness of COVID-19 prevention measures. A fully connected deep neural network, long short-term memory (LSTM), and transformer model were used as the AI models for the prediction of new COVID-19 cases. Initially, data preprocessing and feature extraction were performed using COVID-19 datasets from Saudi Arabia. The performance metrics for all models were computed, and the results were subjected to comparative analysis to detect the most reliable model. Additionally, statistical hypothesis analysis and correlation analysis were performed on the COVID-19 datasets by including features such as daily mobility, total cases, people fully vaccinated per hundred, weekly hospital admissions per million, intensive care unit patients, and new deaths per million. The results show that the LSTM algorithm had the highest accuracy of all the algorithms and an error of less than 2%. The findings of this study contribute to our understanding of COVID-19 containment. This study also provides insights into the prevention of future outbreaks.

show abstract

Section: Related Workmentioning

confidence: 99%

[Retracted] Novel Prediction Model for COVID‐19 in Saudi Arabia Based on an LSTM Algorithm

Alkhammash

Algethami

Alshahrani

2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…to assess COVID-19 diagnosis. 33 Another limitation is the lack of hospital information: we cannot capture ICU admission, ventilation or treatments administered during the admission, which clearly have in uence in the prognosis and outcomes of COVID-19.…”

Section: Discussionmentioning

confidence: 99%

Risk of Thrombotic Events and Other Complications in Anticoagulant Users Infected With SARS-CoV-2: An Observational Cohort Study in Primary Health Care in SIDIAP (Catalonia, Spain)

Giner-Soriano

Gómez-Lumbreras

Urgell

et al. 2021

Preprint

View full text Add to dashboard Cite

Background The risk of thromboembolic events and COVID-19 complications in anticoagulated patients once hospitalized has been widely analyzed. We aim to assess these outcomes in primary health care (PHC) patients chronically treated with oral anticoagulants (OAC) in comparison with non-treated. Methods Cohort study including adults with COVID-19 diagnosis in the PHC records in Catalonia, Spain; from March to June 2020. Patients were matched between exposed and non-exposed to OAC based on age and gender in a 1:2 design. Data source is the Information System for Research in Primary Care (SIDIAP). ResultsWe included 11,828 (33.3%) patients previously exposed to OAC, who were matched by age and sex to 23,656 (66.7%) non-exposed to OAC. Their mean age was 79.3 and 51% were women. Anticoagulated patients had a higher risk of hospital admission (OR 1.30, 95% CI 1.22-1.38), and of stroke and pulmonary embolism than non-anticoagulated (OR 2.5, 95% CI 2.04-3.06). The risk of pneumonia was not different between groups (OR 1.09, 95% CI 0.95-1.26). We found a lower risk of death in patients exposed to OAC (OR 0.88, 95% CI 0.83-0.93). ConclusionsOAC users in our study had more comorbidities and were older than non-users, well known risks for hospitalization being confirmed with our results. We also found in our study that OAC exposure was not associated to an increased risk in the mortality rate, although we cannot assess the effect of the interventions applied during hospital admission on the COVID-19 outcomes, as our database is a PHC database. EUPAS register: EUPAS37205

show abstract

“…Methods in apps processing with AI algorithms data images like X-ray, various sounds or ECG have regularly big datasets available that are essential to better training for deeper neural networks [ 48 , 49 , 50 , 51 ]. This is not situation for CAT.…”

Section: Methodsmentioning

confidence: 99%

Artificial Intelligence in Orthodontic Smart Application for Treatment Coaching and Its Impact on Clinical Performance of Patients Monitored with AI-TeleHealth System

2021

View full text Add to dashboard Cite

Background: Treatment of malocclusion with clear removable appliances like Invisalign® or Spark™, require considerable higher level of patient compliance when compared to conventional fixed braces. The clinical outcomes and treatment efficiency strongly depend on the patient’s discipline. Smart treatment coaching applications, like strojCHECK® are efficient for improving patient compliance. Purpose: To evaluate the impact of computerized personalized decision algorithms responding to observed and anticipated patient behavior implemented as an update of an existing clinical orthodontic application (app). Materials and Methods: Variables such as (1) patient app interaction, (2) patient app discipline and (3) clinical aligner tracking evaluated by artificial intelligence system (AI) system—Dental monitoring® were observed on the set of 86 patients. Two 60-day periods were evaluated; before and after the app was updated with decision tree processes. Results: All variables showed significant improvement after the update except for the manifestation of clinical non-tracking in men, evaluated by artificial intelligence from video scans. Conclusions: Implementation of application update including computerized decision processes can significantly enhance clinical performance of existing health care applications and improve patients’ compliance. Using the algorithm with decision tree architecture could create a baseline for further machine learning optimization.

show abstract

A Novel Method for COVID-19 Diagnosis Using Artificial Intelligence in Chest X-ray Images

Cited by 74 publications

References 43 publications

[Retracted] Novel Prediction Model for COVID‐19 in Saudi Arabia Based on an LSTM Algorithm

[Retracted] Novel Prediction Model for COVID‐19 in Saudi Arabia Based on an LSTM Algorithm

Risk of Thrombotic Events and Other Complications in Anticoagulant Users Infected With SARS-CoV-2: An Observational Cohort Study in Primary Health Care in SIDIAP (Catalonia, Spain)

Artificial Intelligence in Orthodontic Smart Application for Treatment Coaching and Its Impact on Clinical Performance of Patients Monitored with AI-TeleHealth System

Contact Info

Product

Resources

About