{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T21:45:03Z","timestamp":1778708703676,"version":"3.51.4"},"reference-count":34,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,16]],"date-time":"2022-01-16T00:00:00Z","timestamp":1642291200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007615","name":"Imam Abdulrahman Bin Faisal University","doi-asserted-by":"publisher","award":["Covid19-2020-051-CSIT"],"award-info":[{"award-number":["Covid19-2020-051-CSIT"]}],"id":[{"id":"10.13039\/501100007615","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The coronavirus pandemic (COVID-19) is disrupting the entire world; its rapid global spread threatens to affect millions of people. Accurate and timely diagnosis of COVID-19 is essential to control the spread and alleviate risk. Due to the promising results achieved by integrating machine learning (ML), particularly deep learning (DL), in automating the multiple disease diagnosis process. In the current study, a model based on deep learning was proposed for the automated diagnosis of COVID-19 using chest X-ray images (CXR) and clinical data of the patient. The aim of this study is to investigate the effects of integrating clinical patient data with the CXR for automated COVID-19 diagnosis. The proposed model used data collected from King Fahad University Hospital, Dammam, KSA, which consists of 270 patient records. The experiments were carried out first with clinical data, second with the CXR, and finally with clinical data and CXR. The fusion technique was used to combine the clinical features and features extracted from images. The study found that integrating clinical data with the CXR improves diagnostic accuracy. Using the clinical data and the CXR, the model achieved an accuracy of 0.970, a recall of 0.986, a precision of 0.978, and an F-score of 0.982. Further validation was performed by comparing the performance of the proposed system with the diagnosis of an expert. Additionally, the results have shown that the proposed system can be used as a tool that can help the doctors in COVID-19 diagnosis.<\/jats:p>","DOI":"10.3390\/s22020669","type":"journal-article","created":{"date-parts":[[2022,1,16]],"date-time":"2022-01-16T20:45:21Z","timestamp":1642365921000},"page":"669","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Using a Deep Learning Model to Explore the Impact of Clinical Data on COVID-19 Diagnosis Using Chest X-ray"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1002-6178","authenticated-orcid":false,"given":"Irfan Ullah","family":"Khan","sequence":"first","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia"}]},{"given":"Nida","family":"Aslam","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia"}]},{"given":"Talha","family":"Anwar","sequence":"additional","affiliation":[{"name":"School of Computing, National University of Computer and Emerging Sciences, Islamabad 44000, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1376-7652","authenticated-orcid":false,"given":"Hind S.","family":"Alsaif","sequence":"additional","affiliation":[{"name":"Radiology Department, King Fahd Hospital of the University, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1085-7660","authenticated-orcid":false,"given":"Sara Mhd. Bachar","family":"Chrouf","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia"}]},{"given":"Norah A.","family":"Alzahrani","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia"},{"name":"National Center for Artificial Intelligence (NCAI), Saudi Data and Artificial Intelligence Authority (SDAIA), Riyadh 12391, Saudi Arabia"}]},{"given":"Fatimah Ahmed","family":"Alamoudi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia"}]},{"given":"Mariam Moataz Aly","family":"Kamaleldin","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9937-7291","authenticated-orcid":false,"given":"Khaled Bassam","family":"Awary","sequence":"additional","affiliation":[{"name":"Radiology Department, King Fahd Hospital of the University, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,16]]},"reference":[{"key":"ref_1","unstructured":"(2021, October 07). COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). Available online: https:\/\/coronavirus.jhu.edu\/map.html."},{"key":"ref_2","unstructured":"(2021, October 07). A New Strain of Coronavirus: What You Should Know. Available online: https:\/\/www.hopkinsmedicine.org\/health\/conditions-and-diseases\/coronavirus\/a-new-strain-of-coronavirus-what-you-should-know."},{"key":"ref_3","unstructured":"(2021, October 07). BBC-New Coronavirus Variant: What Do We Know?. Available online: https:\/\/www.bbc.com\/news\/health-55388846."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.cca.2020.03.009","article-title":"Positive rate of RT-PCR detection of SARS-CoV-2 infection in 4880 cases from one hospital in Wuhan, China, from Jan to Feb 2020","volume":"505","author":"Liu","year":"2020","journal-title":"Clin. Chim. Acta"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"262","DOI":"10.7326\/M20-1495","article-title":"Variation in False-Negative Rate of Reverse Transcriptase Polymerase Chain Reaction-Based SARS-CoV-2 Tests by Time Since Exposure","volume":"173","author":"Kucirka","year":"2020","journal-title":"Ann. Intern. Med."},{"key":"ref_6","unstructured":"World Health Organization (2020). Use of Chest Imaging in COVID-19, WHO. Available online: https:\/\/www.who.int\/publications\/i\/item\/use-of-chest-imaging-in-covid-19."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"110338","DOI":"10.1016\/j.chaos.2020.110338","article-title":"Applications of artificial intelligence in battling against COVID-19: A literature review","volume":"142","year":"2021","journal-title":"Chaos Solitons Fractals"},{"key":"ref_8","first-page":"851","article-title":"Deep learning in bioinformatics","volume":"18","author":"Min","year":"2017","journal-title":"Brief. Bioinform."},{"key":"ref_9","first-page":"100013","article-title":"Deep learning and its role in COVID-19 medical imaging","volume":"3\u20134","author":"Desai","year":"2020","journal-title":"Intell. Med."},{"key":"ref_10","first-page":"5587188","article-title":"Machine Learning-Based Model to Predict the Disease Severity and Outcome in COVID-19 Patients","volume":"2021","author":"Aljameel","year":"2021","journal-title":"Sci. Program."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"102327","DOI":"10.1109\/ACCESS.2021.3097559","article-title":"Cough Sound Detection and Diagnosis Using Artificial Intelligence Techniques: Challenges and Opportunities","volume":"9","author":"Alqudaihi","year":"2021","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Khan, I.U., Aslam, N., Aljabri, M., Aljameel, S.S., Kamaleldin, M.M.A., Alshamrani, F.M., and Chrouf, S.M.B. (2021). Computational intelligence-based model for mortality rate prediction in COVID-19 patients. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph18126429"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yaron, D., Keidar, D., Goldstein, E., Shachar, Y., Blass, A., Frank, O., Schipper, N., Shabshin, N., Grubstein, A., and Suhami, D. (2021, January 6\u201311). Point of care image analysis for COVID-19. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Toronto, ON, Canada.","DOI":"10.1109\/ICASSP39728.2021.9413687"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Cocconcelli, E., Biondini, D., Giraudo, C., Lococo, S., Bernardinello, N., Fichera, G., Barbiero, G., Castelli, G., Cavinato, S., and Ferrari, A. (2020). Clinical Features and Chest Imaging as Predictors of Intensity of Care in Patients with COVID-19. J. Clin. Med., 9.","DOI":"10.3390\/jcm9092990"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Cellina, M., Gibelli, D., Valenti Pittino, C., Toluian, T., Marino, P., and Oliva, G. (2020). Risk factors of fatal outcome in patients with COVID-19 pneumonia. Disaster Med. Public Health Prep., 1\u201330.","DOI":"10.1017\/dmp.2020.346"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1770","DOI":"10.1007\/s00330-020-07269-8","article-title":"Initial chest radiographs and artificial intelligence (AI) predict clinical outcomes in COVID-19 patients: Analysis of 697 Italian patients","volume":"31","author":"Mushtaq","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"E167","DOI":"10.1148\/radiol.2020203511","article-title":"DeepCOVID-XR: An artificial intelligence algorithm to detect COVID-19 on chest radiographs trained and tested on a large U.S. Clinical data set","volume":"299","author":"Wehbe","year":"2021","journal-title":"Radiology"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Asif, S., Wenhui, Y., Jin, H., and Jinhai, S. (2020, January 11\u201314). Classification of COVID-19 from Chest X-ray images using Deep Convolutional Neural Network. Proceedings of the 2020 IEEE 6th International Conference on Computer and Communications (ICCC), Chengdu, China.","DOI":"10.1109\/ICCC51575.2020.9344870"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Hasan Jahid, M., Alom Shahin, M., and Ali Shikhar, M. (2021, January 27\u201328). Deep Learning based Detection and Segmentation of COVID-19 Pneumonia on Chest X-ray Image. Proceedings of the 2021 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD), Dhaka, Bangladesh.","DOI":"10.1109\/ICICT4SD50815.2021.9396878"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Khan, I.U., and Aslam, N. (2020). A deep-learning-based framework for automated diagnosis of COVID-19 using X-ray images. Information, 11.","DOI":"10.3390\/info11090419"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1177\/2472630320958376","article-title":"Detection of COVID-19 from Chest X-Ray Images Using Convolutional Neural Networks","volume":"25","author":"Sekeroglu","year":"2020","journal-title":"SLAS Technol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"E88","DOI":"10.1148\/radiol.2020202944","article-title":"Diagnosis of Coronavirus Disease 2019 Pneumonia by Using Chest Radiography: Value of Artificial Intelligence","volume":"298","author":"Zhang","year":"2021","journal-title":"Radiology"},{"key":"ref_23","first-page":"7","article-title":"COVID-19 mortality risk prediction using X-ray images","volume":"6","author":"Prada","year":"2021","journal-title":"Int. J. Interact. Multimed. Artif. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.media.2020.101794","article-title":"Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning","volume":"65","author":"Minaee","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Duran-Lopez, L., Dominguez-Morales, J.P., Corral-Jaime, J., Vicente-Diaz, S., and Linares-Barranco, A. (2020). COVID-XNet: A custom deep learning system to diagnose and locate COVID-19 in chest X-ray images. Appl. Sci., 10.","DOI":"10.3390\/app10165683"},{"key":"ref_26","unstructured":"de la Iglesia Vay\u00e1, M., Saborit, J.M., Montell, J.A., Pertusa, A., Bustos, A., Cazorla, M., Galant, J., Barber, X., Orozco-Beltr\u00e1n, D., and Garc\u00eda-Garc\u00eda, F. (2020). BIMCV COVID-19+: A Large Annotated Dataset of RX and CT Images from COVID-19 Patients. IEEE Dataport."},{"key":"ref_27","unstructured":"Cohen, J.P., Morrison, P., and Dao, L. (2020). COVID-19 image data collection. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"101797","DOI":"10.1016\/j.media.2020.101797","article-title":"PadChest: A large chest X-ray image dataset with multi-label annotated reports","volume":"66","author":"Bustos","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref_29","first-page":"100138","article-title":"COVID-19 detection in X-ray images using convolutional neural networks","volume":"6","author":"Serrano","year":"2021","journal-title":"Mach. Learn. Appl."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1007\/s13755-021-00166-4","article-title":"COVID-19 diagnosis from chest X-rays: Developing a simple, fast, and accurate neural network","volume":"9","author":"Nikolaou","year":"2021","journal-title":"Health Inf. Sci. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Baltazar, L.R., Manzanillo, M.G., Gaudillo, J., Viray, E.D., Domingo, M., Tiangco, B., and Albia, J. (2021). Artificial intelligence on COVID-19 pneumonia detection using chest xray images. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0257884"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"9654","DOI":"10.1007\/s00330-021-08050-1","article-title":"COVID-19 classification of X-ray images using deep neural networks","volume":"31","author":"Keidar","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"17532","DOI":"10.1038\/s41598-020-74539-2","article-title":"Automatic classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray image: Combination of data augmentation methods","volume":"10","author":"Nishio","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5121\/ijdkp.2015.5201","article-title":"A Review on Evaluation Metrics for Data Classification Evaluations","volume":"5","author":"Hossin","year":"2015","journal-title":"Int. J. Data Min. Knowl. Manag. Process"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/2\/669\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:39:41Z","timestamp":1760362781000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/2\/669"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,16]]},"references-count":34,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["s22020669"],"URL":"https:\/\/doi.org\/10.3390\/s22020669","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,16]]}}}