Wednesday, October 16th 2024
(Milan Time) 14:20-17:20
(Beijing Time) 20:20-23:20
Tencent ID : 249-162-652
Session Chairs

Chair
Dedong MA
Chief Physician, Professor, Doctoral supervisor
Qilu Hospital of Shandong University (China)

Co-Chair
Peppino PAFFI
Chief of the ‘Anaesthesia and Resuscitation’
P.O. San Francesco di Nuoro Hospital, of ASL Nuoro (Italy)

Co-Chair
Gang HOU
Deputy Head of Section, Chief Physician, Doctoral supervisor
China-Japan Friendship Hospital (China)
Session Presentation
1.

Dr. Gang HOU
Deputy Head of Section, Chief Physician, Doctoral supervisor
China-Japan Friendship Hospital (China)
Title: Artificial Intelligence and Digital Technology in Respiratory Intervention
Abstract
Artificial Intelligence (AI) and digital technologies are advancing rapidly, and we are now using these new technologies in respiratory interventions. They can help us in diagnostic support, personalized treatment, monitoring and early warning, training and education, etc. These applications not only help to improve clinical outcomes, but also optimize resource allocation, promote early detection and intervention of respiratory diseases, and ultimately promote the overall improvement of healthcare services.
2.

Dr. Mingming DENG
Assistant Researcher, postdoctoral student
China-Japan Friendship Hospital (China)
Title: Digital-Twin Technique Applied to Risk Disclosure Reduced Peri-bronchoscopy Anxiety: A Multi-Center, Randomized, Controlled Trial
Abstract
Background:Informed consent is essential, yet providing detailed risk information often heightens preoperative anxiety in bronchoscopy patients. Conventional text-based explanations can be challenging to comprehend, highlighting the need for visual consent methods. To address this, we developed a digital-twin bronchoscopy simulator to visualize the procedure and assessed its effect on patient anxiety. Methods:In this prospective, multicenter, randomized controlled trial, we assessed preoperative anxiety using the modified Amsterdam Preoperative Anxiety and Information Scale (APAIS) and a 100mm Anxiety Visual Analogue Scale (VAS) before and after obtaining informed consent. Results:The study included 110 patients undergoing elective local anaesthesia for bronchoscopy. Baseline demographics were similar between the two groups. After using the personalised data-based bronchoscopy simulator, patients showed a significant reduction in anxiety as measured by APAIS (mean -5.29 vs 2.13) and VAS (mean -8.67 vs 8.47). Conclusion: Pre-operative risk disclosure via a digital-twin based bronchoscopy simulator yielded significantly lower pre-operative anxiety than conventional risk disclosure.
3.

Prof. Chunxue BAI
Director of Shanghai Institute of Respiratory Disease, Doctoral Supervisor,
Director of Lung Tumor Comprehensive Diagnosis and Treatment Center,
Zhongshan Hospital of Fudan University (China)
Title: Advances in Metacosmic Medicine
Abstract
Metaverse medicine is a rapidly growing field that applies digital technologies such as virtual reality (VR), augmented reality (AR), and blockchain to the healthcare industry. The development of meta-universe medicine has not only brought innovations in the way healthcare services are delivered, but has also reduced healthcare costs and improved accessibility and efficiency. With the continuous advancement of technology, healthcare services in the future will be more intelligent and humanized.
4.

Peppino PAFFI
Chief of the ‘Anaesthesia and Resuscitation’
P.O. San Francesco di Nuoro Hospital, of ASL Nuoro (Italy)
Title: To be announced
Abstract
To be announced
5.

Prof. Dedong Ma
Chief Physician, Professor, Doctoral supervisor
Qilu Hospital of Shandong University (China)
Title: Inhaled Drug Efficacy Prediction Based on Digital Twin
Abstract
With the continuous development of medical technology, inhaled medications have become an important means of treating respiratory diseases. A digital twin is a virtual copy of a physical entity or system in digital space, which realizes a true reflection of the real world state through real-time data acquisition and analysis. In the context of inhalation drug efficacy prediction, digital twins can help us simulate the behavior of drugs in the body and thus predict drug efficacy.
6.

Dr. Teng Grace ZHANG
Assistant Professor, Director of the Digital Health Laboratory,
The University of Hong Kong (China)
Title: GenAI Facilitated Intelligent Spine OPD
Abstract
With the rapid development of Artificial Intelligence (AI) technology, Generative Artificial Intelligence (GenAI) has shown great potential in healthcare, especially in Intelligent Spine Outpatient Departments (OPDs). GenAI is used to generate and analyze large amounts of medical data, including imaging data, medical records and patient feedback, and extract valuable information through algorithmic models. It can help doctors quickly diagnose spine-related diseases and also provide personalized treatment recommendations.
7.

Dr. Zhonghai LI
Chief Physician, Associate Professor, Postdoctoral Fellow
The First Hospital of Dalian Medical University (China)
Title: The Digital Twin: a Potential Solution for Personalized Diagnosis and Treatment of Musculoskeletal Diseases
Abstract
Digital twin technology offers a potential solution for personalized diagnosis and treatment of musculoskeletal disorders. By creating a digital twin model of a patient, physicians are able to gain a more comprehensive understanding of the patient’s physical condition and complex pathology. This technology utilizes advanced sensors and data analytics to collect and monitor a patient’s physiological data in real time, which in turn allows for a customized treatment plan for each patient. The digital twin not only improves the early detection of disease, but also optimizes treatment outcomes, helping to achieve more accurate and efficient healthcare and ultimately improving patients’ quality of life.
8.

Dr. Yi-Ching HSUEH
Clinical Research and Development Director ,
Phil Rivers Technology Co., Ltd (China)
Title: Transforming Drug Repurposing: AI-powered Digital Twins in Virtual Clinical Trials
Abstract
Artificial intelligence digital twins are revolutionizing the vast landscape of drug discovery and development, especially in virtual clinical trials. This innovative technology dramatically accelerates the search for drug repurposing by accurately simulating patient physiology and drug response. Instead of waiting for lengthy physical trials, digital twins can predict new drug efficacy in a virtual environment, opening up efficient pathways for marketed drugs to explore new therapeutic areas. This not only reduces R&D costs, but also brings potential therapeutic solutions to patients more quickly, opening up a new era of drug development.