{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T02:36:07Z","timestamp":1781663767168,"version":"3.54.5"},"reference-count":30,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The integration of artificial intelligence (AI) with Digital Twins (DTs) has emerged as a promising approach to revolutionize healthcare, particularly in terms of diagnosis and management of thoracic disorders. This study proposes a comprehensive framework, named Lung-DT, which leverages IoT sensors and AI algorithms to establish the digital representation of a patient\u2019s respiratory health. Using the YOLOv8 neural network, the Lung-DT system accurately classifies chest X-rays into five distinct categories of lung diseases, including \u201cnormal\u201d, \u201ccovid\u201d, \u201clung_opacity\u201d, \u201cpneumonia\u201d, and \u201ctuberculosis\u201d. The performance of the system was evaluated employing a chest X-ray dataset available in the literature, demonstrating average accuracy of 96.8%, precision of 92%, recall of 97%, and F1-score of 94%. The proposed Lung-DT framework offers several advantages over conventional diagnostic methods. Firstly, it enables real-time monitoring of lung health through continuous data acquisition from IoT sensors, facilitating early diagnosis and intervention. Secondly, the AI-powered classification module provides automated and objective assessments of chest X-rays, reducing dependence on subjective human interpretation. Thirdly, the twin digital representation of the patient\u2019s respiratory health allows for comprehensive analysis and correlation of multiple data streams, providing valuable insights as to personalized treatment plans. The integration of IoT sensors, AI algorithms, and DT technology within the Lung-DT system demonstrates a significant step towards improving thoracic healthcare. By enabling continuous monitoring, automated diagnosis, and comprehensive data analysis, the Lung-DT framework has enormous potential to enhance patient outcomes, reduce healthcare costs, and optimize resource allocation.<\/jats:p>","DOI":"10.3390\/s24030958","type":"journal-article","created":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T09:43:22Z","timestamp":1706780602000},"page":"958","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Lung-DT: An AI-Powered Digital Twin Framework for Thoracic Health Monitoring and Diagnosis"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2160-1863","authenticated-orcid":false,"given":"Roberta","family":"Avanzato","sequence":"first","affiliation":[{"name":"Department of Electrical, Electronic and Computer Engineering, University of Catania, Viale Andrea Doria, 95125 Catania, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1606-7608","authenticated-orcid":false,"given":"Francesco","family":"Beritelli","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic and Computer Engineering, University of Catania, Viale Andrea Doria, 95125 Catania, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alfio","family":"Lombardo","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic and Computer Engineering, University of Catania, Viale Andrea Doria, 95125 Catania, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carmelo","family":"Ricci","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic and Computer Engineering, University of Catania, Viale Andrea Doria, 95125 Catania, Italy"},{"name":"National Inter-University Consortium for Telecommunications (CNIT), RU of Catania, 43124 Parma, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., and Summers, R.M. (2017, January 21\u201326). ChestX-ray8: Hospital-Scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.369"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","article-title":"Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning","volume":"172","author":"Kermany","year":"2018","journal-title":"Cell"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Avanzato, R., Beritelli, F., Lombardo, A., and Ricci, C. (2023). Heart DT: Monitoring and Preventing Cardiac Pathologies Using AI and IoT Sensors. Future Internet, 15.","DOI":"10.3390\/fi15070223"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"151972","DOI":"10.1109\/ACCESS.2021.3125324","article-title":"Self-supervised deep convolutional neural network for chest X-ray classification","volume":"9","author":"Gazda","year":"2021","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Hussein, F., Mughaid, A., AlZu\u2019bi, S., El-Salhi, S.M., Abuhaija, B., Abualigah, L., and Gandomi, A.H. (2022). Hybrid clahe-cnn deep neural networks for classifying lung diseases from X-ray acquisitions. Electronics, 11.","DOI":"10.3390\/electronics11193075"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Avanzato, R., and Beritelli, F. (2023, January 7\u20139). Thorax Disease Classification based on the Convolutional Network SqueezeNet. Proceedings of the 12th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, Dortmund, Germany.","DOI":"10.1109\/IDAACS58523.2023.10348691"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"114361","DOI":"10.1016\/j.eswa.2020.114361","article-title":"A new modular neural network approach with fuzzy response integration for lung disease classification based on multiple objective feature optimization in chest X-ray images","volume":"168","author":"Melin","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Mabrouk, A., D\u00edaz Redondo, R.P., Dahou, A., Abd Elaziz, M., and Kayed, M. (2022). Pneumonia detection on chest X-ray images using ensemble of deep convolutional neural networks. Appl. Sci., 12.","DOI":"10.3390\/app12136448"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Shamrat, F.J.M., Azam, S., Karim, A., Islam, R., Tasnim, Z., Ghosh, P., and De Boer, F. (2022). LungNet22: A fine-tuned model for multiclass classification and prediction of lung disease using X-ray images. J. Pers. Med., 12.","DOI":"10.3390\/jpm12050680"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Fan, R., and Bu, S. (2022). Transfer-learning-based approach for the diagnosis of lung diseases from chest X-ray images. Entropy, 24.","DOI":"10.3390\/e24030313"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"923","DOI":"10.1016\/j.aej.2022.10.053","article-title":"A deep learning architecture for multi-class lung diseases classification using chest X-ray (CXR) images","volume":"64","author":"Alshmrani","year":"2023","journal-title":"Alex. Eng. J."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Bhosale, Y.H., and Patnaik, K.S. (2023). PulDi-COVID: Chronic obstructive pulmonary (lung) diseases with COVID-19 classification using ensemble deep convolutional neural network from chest X-ray images to minimize severity and mortality rates. Biomed. Signal Process. Control, 81.","DOI":"10.1016\/j.bspc.2022.104445"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Mezina, A., and Burget, R. (2024). Detection of post-COVID-19-related pulmonary diseases in X-ray images using Vision Transformer-based neural network. Biomed. Signal Process. Control, 87.","DOI":"10.1016\/j.bspc.2023.105380"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"118650","DOI":"10.1016\/j.eswa.2022.118650","article-title":"Automated multi-class classification of lung diseases from CXR-images using pre-trained convolutional neural networks","volume":"211","author":"Karaddi","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Rajagopal, R., Karthick, R., Meenalochini, P., and Kalaichelvi, T. (2023). Deep Convolutional Spiking Neural Network optimized with Arithmetic optimization algorithm for lung disease detection using chest X-ray images. Biomed. Signal Process. Control, 79.","DOI":"10.1016\/j.bspc.2022.104197"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2774","DOI":"10.1109\/TEM.2021.3103334","article-title":"Lung-GANs: Unsupervised representation learning for lung disease classification using chest CT and X-ray images","volume":"70","author":"Yadav","year":"2021","journal-title":"IEEE Trans. Eng. Manag."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Sulaiman, A., Anand, V., Gupta, S., Asiri, Y., Elmagzoub, M., Reshan, M.S.A., and Shaikh, A. (2023). A Convolutional Neural Network Architecture for Segmentation of Lung Diseases Using Chest X-ray Images. Diagnostics, 13.","DOI":"10.3390\/diagnostics13091651"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.vrih.2022.03.002","article-title":"Integrating digital twins and deep learning for medical image analysis in the era of COVID-19","volume":"4","author":"Ahmed","year":"2022","journal-title":"Virtual Real. Intell. Hardw."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"20918","DOI":"10.1109\/JIOT.2022.3176300","article-title":"Digital-Twin-Enabled IoMT System for Surgical Simulation Using rAC-GAN","volume":"9","author":"Tai","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4009309","DOI":"10.1109\/TIM.2023.3298389","article-title":"Electrical Impedance Tomography Guided by Digital Twins and Deep Learning for Lung Monitoring","volume":"72","author":"Zhu","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3677","DOI":"10.1109\/JSAC.2023.3310096","article-title":"An Enhanced Vision Transformer Model in Digital Twins Powered Internet of Medical Things for Pneumonia Diagnosis","volume":"41","author":"Xing","year":"2023","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_22","unstructured":"(2023, December 10). Kaggle, Multiclass Chest X-ray Disease Dataset. Available online: https:\/\/www.kaggle.com\/datasets\/saifurrahmanshatil\/multiclass-chest-xray-disease-dataset."},{"key":"ref_23","unstructured":"(2023, December 10). Kaggle, Lungs Disease Dataset (4 Types). Available online: https:\/\/www.kaggle.com\/datasets\/omkarmanohardalvi\/lungs-disease-dataset-4-types."},{"key":"ref_24","unstructured":"(2023, December 10). Kaggle, Multi Classe Chest X-ray DATASET(VERSION 2). Available online: https:\/\/www.kaggle.com\/datasets\/sourov509\/multi-classe-chest-X-ray-datasetversion-2."},{"key":"ref_25","unstructured":"(2023, January 12). Kaggle, Tuberculosis Chest X-rays (Shenzhen). Available online: https:\/\/www.kaggle.com\/datasets\/raddar\/tuberculosis-chest-xrays-shenzhen\/data."},{"key":"ref_26","unstructured":"(2023, January 12). Kaggle, Chest X-rays Tuberculosis from India. Available online: https:\/\/www.kaggle.com\/datasets\/raddar\/chest-xrays-tuberculosis-from-india."},{"key":"ref_27","unstructured":"(2023, January 12). Kaggle, Balanced Augmented Covid CXR Dataset. Available online: https:\/\/www.kaggle.com\/datasets\/tr1gg3rtrash\/balanced-augmented-covid-cxr-dataset."},{"key":"ref_28","unstructured":"(2023, December 15). YOLOv8, Roboflow. Available online: https:\/\/blog.roboflow.com\/whats-new-in-yolov8\/."},{"key":"ref_29","first-page":"31","article-title":"YOLO-NPK: A Lightweight Deep Network for Lettuce Nutrient Deficiency Classification Based on Improved YOLOv8 Nano","volume":"58","author":"Sikati","year":"2023","journal-title":"Eng. Proc."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Inui, A., Mifune, Y., Nishimoto, H., Mukohara, S., Fukuda, S., Kato, T., Furukawa, T., Tanaka, S., Kusunose, M., and Takigami, S. (2023). Detection of elbow OCD in the ultrasound image by artificial intelligence using YOLOv8. Appl. Sci., 13.","DOI":"10.3390\/app13137623"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/3\/958\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:53:16Z","timestamp":1760104396000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/3\/958"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,1]]},"references-count":30,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["s24030958"],"URL":"https:\/\/doi.org\/10.3390\/s24030958","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,1]]}}}