Lung-DT: An AI-Powered Digital Twin Framework for Thoracic Health Monitoring and Diagnosis
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...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-02-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/24/3/958 |
| _version_ | 1827354533868601344 |
|---|---|
| author | Roberta Avanzato Francesco Beritelli Alfio Lombardo Carmelo Ricci |
| author_facet | Roberta Avanzato Francesco Beritelli Alfio Lombardo Carmelo Ricci |
| author_sort | Roberta Avanzato |
| collection | DOAJ |
| description | 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’s respiratory health. Using the YOLOv8 neural network, the Lung-DT system accurately classifies chest X-rays into five distinct categories of lung diseases, including “normal”, “covid”, “lung_opacity”, “pneumonia”, and “tuberculosis”. 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’s 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. |
| first_indexed | 2024-03-08T03:48:58Z |
| format | Article |
| id | doaj.art-96d2df05ef07406782e153f17b5e6d2f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T03:48:58Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-96d2df05ef07406782e153f17b5e6d2f2024-02-09T15:22:24ZengMDPI AGSensors1424-82202024-02-0124395810.3390/s24030958Lung-DT: An AI-Powered Digital Twin Framework for Thoracic Health Monitoring and DiagnosisRoberta Avanzato0Francesco Beritelli1Alfio Lombardo2Carmelo Ricci3Department of Electrical, Electronic and Computer Engineering, University of Catania, Viale Andrea Doria, 95125 Catania, ItalyDepartment of Electrical, Electronic and Computer Engineering, University of Catania, Viale Andrea Doria, 95125 Catania, ItalyDepartment of Electrical, Electronic and Computer Engineering, University of Catania, Viale Andrea Doria, 95125 Catania, ItalyDepartment of Electrical, Electronic and Computer Engineering, University of Catania, Viale Andrea Doria, 95125 Catania, ItalyThe 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’s respiratory health. Using the YOLOv8 neural network, the Lung-DT system accurately classifies chest X-rays into five distinct categories of lung diseases, including “normal”, “covid”, “lung_opacity”, “pneumonia”, and “tuberculosis”. 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’s 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.https://www.mdpi.com/1424-8220/24/3/958Digital TwinIoT sensorsimage processinglung healthcaresmart healthcareconvolutional neural network |
| spellingShingle | Roberta Avanzato Francesco Beritelli Alfio Lombardo Carmelo Ricci Lung-DT: An AI-Powered Digital Twin Framework for Thoracic Health Monitoring and Diagnosis Sensors Digital Twin IoT sensors image processing lung healthcare smart healthcare convolutional neural network |
| title | Lung-DT: An AI-Powered Digital Twin Framework for Thoracic Health Monitoring and Diagnosis |
| title_full | Lung-DT: An AI-Powered Digital Twin Framework for Thoracic Health Monitoring and Diagnosis |
| title_fullStr | Lung-DT: An AI-Powered Digital Twin Framework for Thoracic Health Monitoring and Diagnosis |
| title_full_unstemmed | Lung-DT: An AI-Powered Digital Twin Framework for Thoracic Health Monitoring and Diagnosis |
| title_short | Lung-DT: An AI-Powered Digital Twin Framework for Thoracic Health Monitoring and Diagnosis |
| title_sort | lung dt an ai powered digital twin framework for thoracic health monitoring and diagnosis |
| topic | Digital Twin IoT sensors image processing lung healthcare smart healthcare convolutional neural network |
| url | https://www.mdpi.com/1424-8220/24/3/958 |
| work_keys_str_mv | AT robertaavanzato lungdtanaipowereddigitaltwinframeworkforthoracichealthmonitoringanddiagnosis AT francescoberitelli lungdtanaipowereddigitaltwinframeworkforthoracichealthmonitoringanddiagnosis AT alfiolombardo lungdtanaipowereddigitaltwinframeworkforthoracichealthmonitoringanddiagnosis AT carmeloricci lungdtanaipowereddigitaltwinframeworkforthoracichealthmonitoringanddiagnosis |