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Advanced current technology can facilitate automating the process and help identifying those who are at higher risks of developing severe illness. This work explores and represents deep-learning-based schemes for predicting clinical outcomes in Covid-19 infected patients, using Visual Transformer and Convolutional Neural Networks (CNNs), fed with 3D data fusion of CT scan images and patients\u2019 clinical data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We report on the efficiency of Video Swin Transformers and several CNN models fed with fusion datasets and CT scans only vs. a set of conventional classifiers fed with patients\u2019 clinical data only. A relatively large clinical dataset from 380 Covid-19 diagnosed patients was used to train\/test the models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Results show that the 3D Video Swin Transformers fed with the fusion datasets of 64 sectional CT scans\u2009+\u200967 clinical labels outperformed all other approaches for predicting outcomes in Covid-19-infected patients amongst all techniques (i.e., TPR\u2009=\u20090.95, FPR\u2009=\u20090.40, F0.5 score\u2009=\u20090.82, AUC\u2009=\u20090.77, Kappa\u2009=\u20090.6).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>We demonstrate how the utility of our proposed novel 3D data fusion approach through concatenating CT scan images with patients\u2019 clinical data can remarkably improve the performance of the models in predicting Covid-19 infection outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Significance<\/jats:title>\n                    <jats:p>Findings indicate possibilities of predicting the severity of outcome using patients\u2019 CT images and clinical data collected at the time of admission to hospital.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12911-023-02344-8","type":"journal-article","created":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T09:02:44Z","timestamp":1700211764000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Visual transformer and deep CNN prediction of high-risk COVID-19 infected patients using fusion of CT images and clinical data"],"prefix":"10.1186","volume":"23","author":[{"given":"Sara Saberi Moghadam","family":"Tehrani","sequence":"first","affiliation":[]},{"given":"Maral","family":"Zarvani","sequence":"additional","affiliation":[]},{"given":"Paria","family":"Amiri","sequence":"additional","affiliation":[]},{"given":"Zahra","family":"Ghods","sequence":"additional","affiliation":[]},{"given":"Masoomeh","family":"Raoufi","sequence":"additional","affiliation":[]},{"given":"Seyed Amir Ahmad","family":"Safavi-Naini","sequence":"additional","affiliation":[]},{"given":"Amirali","family":"Soheili","sequence":"additional","affiliation":[]},{"given":"Mohammad","family":"Gharib","sequence":"additional","affiliation":[]},{"given":"Hamid","family":"Abbasi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,17]]},"reference":[{"issue":"7798","key":"2344_CR1","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1038\/s41586-020-2008-3","volume":"579","author":"F Wu","year":"2020","unstructured":"Wu F, Zhao S, Yu B, Chen Y, Wang W, Song Z, Hu Y, Tao Z, Tian J, Pei Y. 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All patients have signed and submitted their consent to participate in the research and their data privacy has been fully considered [37]. Informed consent was obtained from all subjects. All methods were carried out in accordance with relevant guidelines and regulations (Declaration of Helsinki).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"N\/a.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"265"}}