1
|
Bourazana A, Xanthopoulos A, Briasoulis A, Magouliotis D, Spiliopoulos K, Athanasiou T, Vassilopoulos G, Skoularigis J, Triposkiadis F. Artificial Intelligence in Heart Failure: Friend or Foe? Life (Basel) 2024; 14:145. [PMID: 38276274 PMCID: PMC10817517 DOI: 10.3390/life14010145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 01/08/2024] [Accepted: 01/17/2024] [Indexed: 01/27/2024] Open
Abstract
In recent times, there have been notable changes in cardiovascular medicine, propelled by the swift advancements in artificial intelligence (AI). The present work provides an overview of the current applications and challenges of AI in the field of heart failure. It emphasizes the "garbage in, garbage out" issue, where AI systems can produce inaccurate results with skewed data. The discussion covers issues in heart failure diagnostic algorithms, particularly discrepancies between existing models. Concerns about the reliance on the left ventricular ejection fraction (LVEF) for classification and treatment are highlighted, showcasing differences in current scientific perceptions. This review also delves into challenges in implementing AI, including variable considerations and biases in training data. It underscores the limitations of current AI models in real-world scenarios and the difficulty in interpreting their predictions, contributing to limited physician trust in AI-based models. The overarching suggestion is that AI can be a valuable tool in clinicians' hands for treating heart failure patients, as far as existing medical inaccuracies have been addressed before integrating AI into these frameworks.
Collapse
Affiliation(s)
- Angeliki Bourazana
- Department of Cardiology, University Hospital of Larissa, 41110 Larissa, Greece
| | - Andrew Xanthopoulos
- Department of Cardiology, University Hospital of Larissa, 41110 Larissa, Greece
| | - Alexandros Briasoulis
- Division of Cardiovascular Medicine, Section of Heart Failure and Transplantation, University of Iowa, Iowa City, IA 52242, USA
| | - Dimitrios Magouliotis
- Department of Cardiothoracic Surgery, University of Thessaly, 41110 Larissa, Greece; (D.M.); (K.S.)
| | - Kyriakos Spiliopoulos
- Department of Cardiothoracic Surgery, University of Thessaly, 41110 Larissa, Greece; (D.M.); (K.S.)
| | - Thanos Athanasiou
- Department of Surgery and Cancer, Imperial College London, St Mary’s Hospital, London W2 1NY, UK
| | - George Vassilopoulos
- Department of Hematology, University Hospital of Larissa, University of Thessaly Medical School, 41110 Larissa, Greece
| | - John Skoularigis
- Department of Cardiology, University Hospital of Larissa, 41110 Larissa, Greece
| | | |
Collapse
|
2
|
Di Bidino R, Piaggio D, Andellini M, Merino-Barbancho B, Lopez-Perez L, Zhu T, Raza Z, Ni M, Morrison A, Borsci S, Fico G, Pecchia L, Iadanza E. Scoping Meta-Review of Methods Used to Assess Artificial Intelligence-Based Medical Devices for Heart Failure. Bioengineering (Basel) 2023; 10:1109. [PMID: 37892839 PMCID: PMC10604154 DOI: 10.3390/bioengineering10101109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/13/2023] [Accepted: 09/17/2023] [Indexed: 10/29/2023] Open
Abstract
Artificial intelligence and machine learning (AI/ML) are playing increasingly important roles, permeating the field of medical devices (MDs). This rapid progress has not yet been matched by the Health Technology Assessment (HTA) process, which still needs to define a common methodology for assessing AI/ML-based MDs. To collect existing evidence from the literature about the methods used to assess AI-based MDs, with a specific focus on those used for the management of heart failure (HF), the International Federation of Medical and Biological Engineering (IFMBE) conducted a scoping meta-review. This manuscript presents the results of this search, which covered the period from January 1974 to October 2022. After careful independent screening, 21 reviews, mainly conducted in North America and Europe, were retained and included. Among the findings were that deep learning is the most commonly utilised method and that electronic health records and registries are among the most prevalent sources of data for AI/ML algorithms. Out of the 21 included reviews, 19 focused on risk prediction and/or the early diagnosis of HF. Furthermore, 10 reviews provided evidence of the impact on the incidence/progression of HF, and 13 on the length of stay. From an HTA perspective, the main areas requiring improvement are the quality assessment of studies on AI/ML (included in 11 out of 21 reviews) and their data sources, as well as the definition of the criteria used to assess the selection of the most appropriate AI/ML algorithm.
Collapse
Affiliation(s)
- Rossella Di Bidino
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS—The Graduate School of Health Economics and Management (ALTEMS), 00168 Rome, Italy
| | - Davide Piaggio
- School of Engineering, University of Warwick, Coventry CV4 7AL, UK; (D.P.); (M.A.); (Z.R.); (L.P.)
| | - Martina Andellini
- School of Engineering, University of Warwick, Coventry CV4 7AL, UK; (D.P.); (M.A.); (Z.R.); (L.P.)
| | - Beatriz Merino-Barbancho
- Life Supporting Technologies, Photonics Technology and Bioengineering Department, School of Telecommunication Engineering, Universidad Politécnica de Madrid, 28040 Madrid, Spain (L.L.-P.); (G.F.)
| | - Laura Lopez-Perez
- Life Supporting Technologies, Photonics Technology and Bioengineering Department, School of Telecommunication Engineering, Universidad Politécnica de Madrid, 28040 Madrid, Spain (L.L.-P.); (G.F.)
| | - Tianhui Zhu
- NIHR London In-Vitro Diagnostics Cooperative, Imperial College of London, London W2 1NY, UK
| | - Zeeshan Raza
- School of Engineering, University of Warwick, Coventry CV4 7AL, UK; (D.P.); (M.A.); (Z.R.); (L.P.)
| | - Melody Ni
- NIHR London In-Vitro Diagnostics Cooperative, Imperial College of London, London W2 1NY, UK
| | - Andra Morrison
- Canadian Agency for Drugs and Technologies in Health, Ottawa, ON K1S 5S8, Canada;
| | - Simone Borsci
- NIHR London In-Vitro Diagnostics Cooperative, Imperial College of London, London W2 1NY, UK
- Department of Learning, Data Analysis, and Technology, Cognition, Data and Education (CODE) Group, Faculty of Behavioural Management and Social Sciences, University of Twente, 7522 Enschede, The Netherlands
| | - Giuseppe Fico
- Life Supporting Technologies, Photonics Technology and Bioengineering Department, School of Telecommunication Engineering, Universidad Politécnica de Madrid, 28040 Madrid, Spain (L.L.-P.); (G.F.)
| | - Leandro Pecchia
- School of Engineering, University of Warwick, Coventry CV4 7AL, UK; (D.P.); (M.A.); (Z.R.); (L.P.)
- School of Engineering, University Campus Bio-Medico, 00128 Rome, Italy
- International Federation of Medical and Biological Engineering, B-1090 Brussels, Belgium
| | - Ernesto Iadanza
- International Federation of Medical and Biological Engineering, B-1090 Brussels, Belgium
- Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy
| |
Collapse
|
3
|
Cuevas-Chávez A, Hernández Y, Ortiz-Hernandez J, Sánchez-Jiménez E, Ochoa-Ruiz G, Pérez J, González-Serna G. A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases. Healthcare (Basel) 2023; 11:2240. [PMID: 37628438 PMCID: PMC10454027 DOI: 10.3390/healthcare11162240] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 07/21/2023] [Accepted: 07/24/2023] [Indexed: 08/27/2023] Open
Abstract
According to the Pan American Health Organization, cardiovascular disease is the leading cause of death worldwide, claiming an estimated 17.9 million lives each year. This paper presents a systematic review to highlight the use of IoT, IoMT, and machine learning to detect, predict, or monitor cardiovascular disease. We had a final sample of 164 high-impact journal papers, focusing on two categories: cardiovascular disease detection using IoT/IoMT technologies and cardiovascular disease using machine learning techniques. For the first category, we found 82 proposals, while for the second, we found 85 proposals. The research highlights list of IoT/IoMT technologies, machine learning techniques, datasets, and the most discussed cardiovascular diseases. Neural networks have been popularly used, achieving an accuracy of over 90%, followed by random forest, XGBoost, k-NN, and SVM. Based on the results, we conclude that IoT/IoMT technologies can predict cardiovascular diseases in real time, ensemble techniques obtained one of the best performances in the accuracy metric, and hypertension and arrhythmia were the most discussed diseases. Finally, we identified the lack of public data as one of the main obstacles for machine learning approaches for cardiovascular disease prediction.
Collapse
Affiliation(s)
- Alejandra Cuevas-Chávez
- Computer Science Department, Tecnológico Nacional de México/Cenidet, Cuernavaca 62490, Mexico; (J.O.-H.); (E.S.-J.); (J.P.); (G.G.-S.)
| | - Yasmín Hernández
- Computer Science Department, Tecnológico Nacional de México/Cenidet, Cuernavaca 62490, Mexico; (J.O.-H.); (E.S.-J.); (J.P.); (G.G.-S.)
| | - Javier Ortiz-Hernandez
- Computer Science Department, Tecnológico Nacional de México/Cenidet, Cuernavaca 62490, Mexico; (J.O.-H.); (E.S.-J.); (J.P.); (G.G.-S.)
| | - Eduardo Sánchez-Jiménez
- Computer Science Department, Tecnológico Nacional de México/Cenidet, Cuernavaca 62490, Mexico; (J.O.-H.); (E.S.-J.); (J.P.); (G.G.-S.)
| | - Gilberto Ochoa-Ruiz
- School of Engineering and Sciences, Tecnologico de Monterrey, Av. Eugenio Garza Sada 2501, Monterrey 64849, Mexico;
| | - Joaquín Pérez
- Computer Science Department, Tecnológico Nacional de México/Cenidet, Cuernavaca 62490, Mexico; (J.O.-H.); (E.S.-J.); (J.P.); (G.G.-S.)
| | - Gabriel González-Serna
- Computer Science Department, Tecnológico Nacional de México/Cenidet, Cuernavaca 62490, Mexico; (J.O.-H.); (E.S.-J.); (J.P.); (G.G.-S.)
| |
Collapse
|
4
|
Clinical Modelling of RVHF Using Pre-Operative Variables: A Direct and Inverse Feature Extraction Technique. Diagnostics (Basel) 2022; 12:diagnostics12123061. [PMID: 36553067 PMCID: PMC9777038 DOI: 10.3390/diagnostics12123061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 11/23/2022] [Accepted: 12/02/2022] [Indexed: 12/12/2022] Open
Abstract
Right ventricular heart failure (RVHF) mostly occurs due to the failure of the left-side of the heart. RVHF is a serious disease that leads to swelling of the abdomen, ankles, liver, kidneys, and gastrointestinal (GI) tract. A total of 506 heart-failure subjects from the Faculty of Medicine, Cardiovascular Surgery Department, Ege University, Turkey, who suffered from a severe heart failure and are currently receiving support from a ventricular assistance device, were involved in the current study. Therefore, the current study explored the application of both the direct and inverse modelling approaches, based on the correlation analysis feature extraction performance of various pre-operative variables of the subjects, for the prediction of RVHF. The study equally employs both single and hybrid paradigms for the prediction of RVHF using different pre-operative variables. The visualized and quantitative performance of the direct and inverse modelling approach indicates the robust prediction performance of the hybrid paradigms over the single techniques in both the calibration and validation steps. Whereby, the quantitative performance of the hybrid techniques, based on the Nash-Sutcliffe coefficient (NC) metric, depicts its superiority over the single paradigms by up to 58.7%/75.5% and 80.3%/51% for the calibration/validation phases in the direct and inverse modelling approaches, respectively. Moreover, to the best knowledge of the authors, this is the first study to report the implementation of direct and inverse modelling on clinical data. The findings of the current study indicates the possibility of applying these novel hybridised paradigms for the prediction of RVHF using pre-operative variables.
Collapse
|
5
|
Huang JD, Wang J, Ramsey E, Leavey G, Chico TJA, Condell J. Applying Artificial Intelligence to Wearable Sensor Data to Diagnose and Predict Cardiovascular Disease: A Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:8002. [PMID: 36298352 PMCID: PMC9610988 DOI: 10.3390/s22208002] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 10/06/2022] [Accepted: 10/13/2022] [Indexed: 06/06/2023]
Abstract
Cardiovascular disease (CVD) is the world's leading cause of mortality. There is significant interest in using Artificial Intelligence (AI) to analyse data from novel sensors such as wearables to provide an earlier and more accurate prediction and diagnosis of heart disease. Digital health technologies that fuse AI and sensing devices may help disease prevention and reduce the substantial morbidity and mortality caused by CVD worldwide. In this review, we identify and describe recent developments in the application of digital health for CVD, focusing on AI approaches for CVD detection, diagnosis, and prediction through AI models driven by data collected from wearables. We summarise the literature on the use of wearables and AI in cardiovascular disease diagnosis, followed by a detailed description of the dominant AI approaches applied for modelling and prediction using data acquired from sensors such as wearables. We discuss the AI algorithms and models and clinical applications and find that AI and machine-learning-based approaches are superior to traditional or conventional statistical methods for predicting cardiovascular events. However, further studies evaluating the applicability of such algorithms in the real world are needed. In addition, improvements in wearable device data accuracy and better management of their application are required. Lastly, we discuss the challenges that the introduction of such technologies into routine healthcare may face.
Collapse
Affiliation(s)
- Jian-Dong Huang
- School of Computing, Engineering and Intelligent Systems, Ulster University at Magee, Londonderry BT48 7JL, UK
| | - Jinling Wang
- School of Computing, Engineering and Intelligent Systems, Ulster University at Magee, Londonderry BT48 7JL, UK
| | - Elaine Ramsey
- Department of Global Business & Enterprise, Ulster University at Magee, Londonderry BT48 7JL, UK
| | - Gerard Leavey
- School of Psychology, Ulster University at Coleraine, Londonderry BT52 1SA, UK
| | - Timothy J. A. Chico
- Department of Infection, Immunity and Cardiovascular Disease, The Medical School, The University of Sheffield, Beech Hill Road, Sheffield S10 2RX, UK
| | - Joan Condell
- School of Computing, Engineering and Intelligent Systems, Ulster University at Magee, Londonderry BT48 7JL, UK
| |
Collapse
|
6
|
Junaid SB, Imam AA, Balogun AO, De Silva LC, Surakat YA, Kumar G, Abdulkarim M, Shuaibu AN, Garba A, Sahalu Y, Mohammed A, Mohammed TY, Abdulkadir BA, Abba AA, Kakumi NAI, Mahamad S. Recent Advancements in Emerging Technologies for Healthcare Management Systems: A Survey. Healthcare (Basel) 2022; 10:healthcare10101940. [PMID: 36292387 PMCID: PMC9601636 DOI: 10.3390/healthcare10101940] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 09/26/2022] [Accepted: 09/28/2022] [Indexed: 11/16/2022] Open
Abstract
In recent times, the growth of the Internet of Things (IoT), artificial intelligence (AI), and Blockchain technologies have quickly gained pace as a new study niche in numerous collegiate and industrial sectors, notably in the healthcare sector. Recent advancements in healthcare delivery have given many patients access to advanced personalized healthcare, which has improved their well-being. The subsequent phase in healthcare is to seamlessly consolidate these emerging technologies such as IoT-assisted wearable sensor devices, AI, and Blockchain collectively. Surprisingly, owing to the rapid use of smart wearable sensors, IoT and AI-enabled technology are shifting healthcare from a conventional hub-based system to a more personalized healthcare management system (HMS). However, implementing smart sensors, advanced IoT, AI, and Blockchain technologies synchronously in HMS remains a significant challenge. Prominent and reoccurring issues such as scarcity of cost-effective and accurate smart medical sensors, unstandardized IoT system architectures, heterogeneity of connected wearable devices, the multidimensionality of data generated, and high demand for interoperability are vivid problems affecting the advancement of HMS. Hence, this survey paper presents a detailed evaluation of the application of these emerging technologies (Smart Sensor, IoT, AI, Blockchain) in HMS to better understand the progress thus far. Specifically, current studies and findings on the deployment of these emerging technologies in healthcare are investigated, as well as key enabling factors, noteworthy use cases, and successful deployments. This survey also examined essential issues that are frequently encountered by IoT-assisted wearable sensor systems, AI, and Blockchain, as well as the critical concerns that must be addressed to enhance the application of these emerging technologies in the HMS.
Collapse
Affiliation(s)
| | - Abdullahi Abubakar Imam
- School of Digital Science, Universiti Brunei Darussalam, Brunei Darussalam, Jalan Tungku Link, Gadong BE1410, Brunei
- Correspondence: (A.A.I.); or (A.O.B.)
| | - Abdullateef Oluwagbemiga Balogun
- Department of Computer Science, University of Ilorin, Ilorin 1515, Nigeria
- Department of Computer and Information Science, Universiti Teknologi PETRONAS, Sri Iskandar 32610, Malaysia
- Correspondence: (A.A.I.); or (A.O.B.)
| | | | | | - Ganesh Kumar
- Department of Computer and Information Science, Universiti Teknologi PETRONAS, Sri Iskandar 32610, Malaysia
| | - Muhammad Abdulkarim
- Department of Computer Science, Ahmadu Bello University, Zaria 810211, Nigeria
| | - Aliyu Nuhu Shuaibu
- Department of Electrical Engineering, University of Jos, Bauchi Road, Jos 930105, Nigeria
| | - Aliyu Garba
- Department of Computer Science, Ahmadu Bello University, Zaria 810211, Nigeria
| | - Yusra Sahalu
- SEHA Abu Dhabi Health Services Co., Abu Dhabi 109090, United Arab Emirates
| | - Abdullahi Mohammed
- Department of Computer Science, Ahmadu Bello University, Zaria 810211, Nigeria
| | | | | | | | - Nana Aliyu Iliyasu Kakumi
- Patient Care Department, General Ward, Saudi German Hospital Cairo, Taha Hussein Rd, Huckstep, El Nozha, Cairo Governorate 4473303, Egypt
| | - Saipunidzam Mahamad
- Department of Computer and Information Science, Universiti Teknologi PETRONAS, Sri Iskandar 32610, Malaysia
| |
Collapse
|
7
|
Yu Z, Wang K, Wan Z, Xie S, Lv Z. Popular deep learning algorithms for disease prediction: a review. CLUSTER COMPUTING 2022; 26:1231-1251. [PMID: 36120180 PMCID: PMC9469816 DOI: 10.1007/s10586-022-03707-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 07/07/2022] [Accepted: 08/03/2022] [Indexed: 06/15/2023]
Abstract
Due to its automatic feature learning ability and high performance, deep learning has gradually become the mainstream of artificial intelligence in recent years, playing a role in many fields. Especially in the medical field, the accuracy rate of deep learning even exceeds that of doctors. This paper introduces several deep learning algorithms: Artificial Neural Network (NN), FM-Deep Learning, Convolutional NN and Recurrent NN, and expounds their theory, development history and applications in disease prediction; we analyze the defects in the current disease prediction field and give some current solutions; our paper expounds the two major trends in the future disease prediction and medical field-integrating Digital Twins and promoting precision medicine. This study can better inspire relevant researchers, so that they can use this article to understand related disease prediction algorithms and then make better related research.
Collapse
Affiliation(s)
- Zengchen Yu
- College of Computer Science and Technology, Qingdao University, Ningxia Road, Qingdao, 266071 China
| | - Ke Wang
- Psychiatric Department, Qingdao Municipal Hospital, Zhuhai Road, Qingdao, 266071 China
| | - Zhibo Wan
- College of Computer Science and Technology, Qingdao University, Ningxia Road, Qingdao, 266071 China
| | - Shuxuan Xie
- College of Computer Science and Technology, Qingdao University, Ningxia Road, Qingdao, 266071 China
| | - Zhihan Lv
- Department of Game Design, Faculty of Arts, Uppsala University, 75105 Uppsala, Sweden
| |
Collapse
|
8
|
Zhou H, Xu K, Ha N, Cheng Y, Ou R, Ma Q, Hu Y, Trinh V, Ren G, Li Z, Ou JZ. Reversible Room Temperature H 2 Gas Sensing Based on Self-Assembled Cobalt Oxysulfide. SENSORS (BASEL, SWITZERLAND) 2021; 22:303. [PMID: 35009847 PMCID: PMC8749549 DOI: 10.3390/s22010303] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 12/26/2021] [Accepted: 12/29/2021] [Indexed: 11/16/2022]
Abstract
Reversible H2 gas sensing at room temperature has been highly desirable given the booming of the Internet of Things (IoT), zero-emission vehicles, and fuel cell technologies. Conventional metal oxide-based semiconducting gas sensors have been considered as suitable candidates given their low-cost, high sensitivity, and long stability. However, the dominant sensing mechanism is based on the chemisorption of gas molecules which requires elevated temperatures to activate the catalytic reaction of target gas molecules with chemisorbed O, leaving the drawbacks of high-power consumption and poor selectivity. In this work, we introduce an alternative candidate of cobalt oxysulfide derived from the calcination of self-assembled cobalt sulfide micro-cages. It is found that the majority of S atoms are replaced by O in cobalt oxysulfide, transforming the crystal structure to tetragonal coordination and slightly expanding the optical bandgap energy. The H2 gas sensing performances of cobalt oxysulfide are fully reversible at room temperature, demonstrating peculiar p-type gas responses with a magnitude of 15% for 1% H2 and a high degree of selectivity over CH4, NO2, and CO2. Such excellent performances are possibly ascribed to the physisorption dominating the gas-matter interaction. This work demonstrates the great potentials of transition metal oxysulfide compounds for room-temperature fully reversible gas sensing.
Collapse
Affiliation(s)
- Hui Zhou
- Key Laboratory of Advanced Technologies of Materials, Ministry of Education, School of Materials Science and Engineering, Southwest Jiaotong University, Chengdu 610031, China; (H.Z.); (Y.C.); (Z.L.); (J.Z.O.)
| | - Kai Xu
- School of Engineering, RMIT University, Melbourne, VIC 3000, Australia; (N.H.); (R.O.); (Q.M.); (Y.H.); (V.T.); (G.R.)
| | - Nam Ha
- School of Engineering, RMIT University, Melbourne, VIC 3000, Australia; (N.H.); (R.O.); (Q.M.); (Y.H.); (V.T.); (G.R.)
| | - Yinfen Cheng
- Key Laboratory of Advanced Technologies of Materials, Ministry of Education, School of Materials Science and Engineering, Southwest Jiaotong University, Chengdu 610031, China; (H.Z.); (Y.C.); (Z.L.); (J.Z.O.)
| | - Rui Ou
- School of Engineering, RMIT University, Melbourne, VIC 3000, Australia; (N.H.); (R.O.); (Q.M.); (Y.H.); (V.T.); (G.R.)
| | - Qijie Ma
- School of Engineering, RMIT University, Melbourne, VIC 3000, Australia; (N.H.); (R.O.); (Q.M.); (Y.H.); (V.T.); (G.R.)
| | - Yihong Hu
- School of Engineering, RMIT University, Melbourne, VIC 3000, Australia; (N.H.); (R.O.); (Q.M.); (Y.H.); (V.T.); (G.R.)
| | - Vien Trinh
- School of Engineering, RMIT University, Melbourne, VIC 3000, Australia; (N.H.); (R.O.); (Q.M.); (Y.H.); (V.T.); (G.R.)
| | - Guanghui Ren
- School of Engineering, RMIT University, Melbourne, VIC 3000, Australia; (N.H.); (R.O.); (Q.M.); (Y.H.); (V.T.); (G.R.)
| | - Zhong Li
- Key Laboratory of Advanced Technologies of Materials, Ministry of Education, School of Materials Science and Engineering, Southwest Jiaotong University, Chengdu 610031, China; (H.Z.); (Y.C.); (Z.L.); (J.Z.O.)
| | - Jian Zhen Ou
- Key Laboratory of Advanced Technologies of Materials, Ministry of Education, School of Materials Science and Engineering, Southwest Jiaotong University, Chengdu 610031, China; (H.Z.); (Y.C.); (Z.L.); (J.Z.O.)
- School of Engineering, RMIT University, Melbourne, VIC 3000, Australia; (N.H.); (R.O.); (Q.M.); (Y.H.); (V.T.); (G.R.)
| |
Collapse
|