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Recently, the Deep Learning (<jats:italic>DL<\/jats:italic>) models have significantly enhanced the capabilities of <jats:italic>SDTs<\/jats:italic>, particularly for tasks such as predictive maintenance, anomaly detection, and optimization. In many domains, including medicine, engineering, and education, <jats:italic>SDTs<\/jats:italic> use image data (image-based <jats:italic>SDTs<\/jats:italic>) to observe, learn, and control system behaviors. This paper focuses on various approaches and associated challenges in developing image-based <jats:italic>SDTs<\/jats:italic> by continually assimilating image data from physical systems. The paper also discusses the challenges in designing and implementing <jats:italic>DL<\/jats:italic> models for <jats:italic>SDTs<\/jats:italic>, including data acquisition, processing, and interpretation. In addition, insights into the future directions and opportunities for developing new image-based <jats:italic>DL<\/jats:italic> approaches to develop robust <jats:italic>SDTs<\/jats:italic> are provided. This includes the potential for using generative models for data augmentation, developing multi-modal <jats:italic>DL<\/jats:italic> models, and exploring the integration of <jats:italic>DL<\/jats:italic> models with other technologies, including Fifth Generation (<jats:italic>5\u00a0G<\/jats:italic>), edge computing, and the Internet of Things (<jats:italic>IoT<\/jats:italic>). In this paper, we describe the image-based <jats:italic>SDTs<\/jats:italic>, which enable broader adoption of the Digital Twins (<jats:italic>DTs<\/jats:italic>) paradigms across a broad spectrum of areas and the development of new methods to improve the abilities of <jats:italic>SDTs<\/jats:italic> in replicating, predicting, and optimizing the behavior of complex systems.<\/jats:p>","DOI":"10.1007\/s10462-024-11002-y","type":"journal-article","created":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T10:15:45Z","timestamp":1740392145000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Image-based deep learning for smart digital twins: a review"],"prefix":"10.1007","volume":"58","author":[{"given":"Md Ruman","family":"Islam","sequence":"first","affiliation":[]},{"given":"Mahadevan","family":"Subramaniam","sequence":"additional","affiliation":[]},{"given":"Pei-Chi","family":"Huang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,24]]},"reference":[{"key":"11002_CR1","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.procir.2020.02.146","volume":"91","author":"D Adamenko","year":"2020","unstructured":"Adamenko D, Kunnen S, Pluhnau R, Loibl A, Nagarajah A (2020) Review and comparison of the methods of designing the digital twin. 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