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Gupta S, Dubey AK, Singh R, Kalra MK, Abraham A, Kumari V, Laird JR, Al-Maini M, Gupta N, Singh I, Viskovic K, Saba L, Suri JS. Four Transformer-Based Deep Learning Classifiers Embedded with an Attention U-Net-Based Lung Segmenter and Layer-Wise Relevance Propagation-Based Heatmaps for COVID-19 X-ray Scans. Diagnostics (Basel) 2024; 14:1534. [PMID: 39061671 PMCID: PMC11275579 DOI: 10.3390/diagnostics14141534] [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: 05/04/2024] [Revised: 07/10/2024] [Accepted: 07/10/2024] [Indexed: 07/28/2024] Open
Abstract
Background: Diagnosing lung diseases accurately is crucial for proper treatment. Convolutional neural networks (CNNs) have advanced medical image processing, but challenges remain in their accurate explainability and reliability. This study combines U-Net with attention and Vision Transformers (ViTs) to enhance lung disease segmentation and classification. We hypothesize that Attention U-Net will enhance segmentation accuracy and that ViTs will improve classification performance. The explainability methodologies will shed light on model decision-making processes, aiding in clinical acceptance. Methodology: A comparative approach was used to evaluate deep learning models for segmenting and classifying lung illnesses using chest X-rays. The Attention U-Net model is used for segmentation, and architectures consisting of four CNNs and four ViTs were investigated for classification. Methods like Gradient-weighted Class Activation Mapping plus plus (Grad-CAM++) and Layer-wise Relevance Propagation (LRP) provide explainability by identifying crucial areas influencing model decisions. Results: The results support the conclusion that ViTs are outstanding in identifying lung disorders. Attention U-Net obtained a Dice Coefficient of 98.54% and a Jaccard Index of 97.12%. ViTs outperformed CNNs in classification tasks by 9.26%, reaching an accuracy of 98.52% with MobileViT. An 8.3% increase in accuracy was seen while moving from raw data classification to segmented image classification. Techniques like Grad-CAM++ and LRP provided insights into the decision-making processes of the models. Conclusions: This study highlights the benefits of integrating Attention U-Net and ViTs for analyzing lung diseases, demonstrating their importance in clinical settings. Emphasizing explainability clarifies deep learning processes, enhancing confidence in AI solutions and perhaps enhancing clinical acceptance for improved healthcare results.
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Affiliation(s)
- Siddharth Gupta
- Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi 110063, India;
| | - Arun K. Dubey
- Department of Information Technology, Bharati Vidyapeeth’s College of Engineering, New Delhi 110063, India; (A.K.D.); (N.G.)
| | - Rajesh Singh
- Department of Research and Innovation, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, India;
| | - Mannudeep K. Kalra
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA;
| | - Ajith Abraham
- Department of Computer Science, Bennett University, Greater Noida 201310, India;
| | - Vandana Kumari
- School of Computer Science and Engineering, Galgotias University, Greater Noida 201310, India;
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA 94574, USA;
| | - Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON M5G 1N8, Canada;
| | - Neha Gupta
- Department of Information Technology, Bharati Vidyapeeth’s College of Engineering, New Delhi 110063, India; (A.K.D.); (N.G.)
| | - Inder Singh
- Department of Radiology and Ultrasound, University Hospital for Infectious Diseases, 10000 Zagreb, Croatia;
| | - Klaudija Viskovic
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09100 Cagliari, Italy;
| | - Luca Saba
- Department of ECE, Idaho State University, Pocatello, ID 83209, USA;
| | - Jasjit S. Suri
- Department of ECE, Idaho State University, Pocatello, ID 83209, USA;
- Stroke Diagnostics and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA
- Department of Computer Engineering, Graphic Era (Deemed to be University), Dehradun 248002, India
- Department of Computer Science & Engineering, Symbiosis Institute of Technology, Nagpur Campus 440008, Symbiosis International (Deemed University), Pune 412115, India
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2
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Saba L, Maindarkar M, Johri AM, Mantella L, Laird JR, Khanna NN, Paraskevas KI, Ruzsa Z, Kalra MK, Fernandes JFE, Chaturvedi S, Nicolaides A, Rathore V, Singh N, Isenovic ER, Viswanathan V, Fouda MM, Suri JS. UltraAIGenomics: Artificial Intelligence-Based Cardiovascular Disease Risk Assessment by Fusion of Ultrasound-Based Radiomics and Genomics Features for Preventive, Personalized and Precision Medicine: A Narrative Review. Rev Cardiovasc Med 2024; 25:184. [PMID: 39076491 PMCID: PMC11267214 DOI: 10.31083/j.rcm2505184] [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: 09/25/2023] [Revised: 02/24/2024] [Accepted: 03/05/2024] [Indexed: 07/31/2024] Open
Abstract
Cardiovascular disease (CVD) diagnosis and treatment are challenging since symptoms appear late in the disease's progression. Despite clinical risk scores, cardiac event prediction is inadequate, and many at-risk patients are not adequately categorised by conventional risk factors alone. Integrating genomic-based biomarkers (GBBM), specifically those found in plasma and/or serum samples, along with novel non-invasive radiomic-based biomarkers (RBBM) such as plaque area and plaque burden can improve the overall specificity of CVD risk. This review proposes two hypotheses: (i) RBBM and GBBM biomarkers have a strong correlation and can be used to detect the severity of CVD and stroke precisely, and (ii) introduces a proposed artificial intelligence (AI)-based preventive, precision, and personalized ( aiP 3 ) CVD/Stroke risk model. The PRISMA search selected 246 studies for the CVD/Stroke risk. It showed that using the RBBM and GBBM biomarkers, deep learning (DL) modelscould be used for CVD/Stroke risk stratification in the aiP 3 framework. Furthermore, we present a concise overview of platelet function, complete blood count (CBC), and diagnostic methods. As part of the AI paradigm, we discuss explainability, pruning, bias, and benchmarking against previous studies and their potential impacts. The review proposes the integration of RBBM and GBBM, an innovative solution streamlined in the DL paradigm for predicting CVD/Stroke risk in the aiP 3 framework. The combination of RBBM and GBBM introduces a powerful CVD/Stroke risk assessment paradigm. aiP 3 model signifies a promising advancement in CVD/Stroke risk assessment.
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Affiliation(s)
- Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria, 40138 Cagliari, Italy
| | - Mahesh Maindarkar
- School of Bioengineering Sciences and Research, MIT Art, Design and Technology University, 412021 Pune, India
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
| | - Amer M. Johri
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada
| | - Laura Mantella
- Department of Medicine, Division of Cardiology, University of Toronto, Toronto, ON M5S 1A1, Canada
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA 94574, USA
| | - Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, 110001 New Delhi, India
| | | | - Zoltan Ruzsa
- Invasive Cardiology Division, University of Szeged, 6720 Szeged, Hungary
| | - Manudeep K. Kalra
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
| | | | - Seemant Chaturvedi
- Department of Neurology & Stroke Program, University of Maryland, Baltimore, MD 20742, USA
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre and University of Nicosia Medical School, 2368 Agios Dometios, Cyprus
| | - Vijay Rathore
- Nephrology Department, Kaiser Permanente, Sacramento, CA 95823, USA
| | - Narpinder Singh
- Department of Food Science and Technology, Graphic Era Deemed to be University, Dehradun, 248002 Uttarakhand, India
| | - Esma R. Isenovic
- Department of Radiobiology and Molecular Genetics, National Institute of The Republic of Serbia, University of Belgrade, 11000 Belgrade, Serbia
| | | | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA
| | - Jasjit S. Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
- Department of Computer Engineering, Graphic Era Deemed to be University, Dehradun, 248002 Uttarakhand, India
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3
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Hinterwimmer F, Serena RS, Wilhelm N, Breden S, Consalvo S, Seidl F, Juestel D, Burgkart RHH, Woertler K, von Eisenhart-Rothe R, Neumann J, Rueckert D. Recommender-based bone tumour classification with radiographs-a link to the past. Eur Radiol 2024:10.1007/s00330-024-10672-0. [PMID: 38488971 DOI: 10.1007/s00330-024-10672-0] [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: 10/20/2023] [Revised: 01/16/2024] [Accepted: 02/05/2024] [Indexed: 03/17/2024]
Abstract
OBJECTIVES To develop an algorithm to link undiagnosed patients to previous patient histories based on radiographs, and simultaneous classification of multiple bone tumours to enable early and specific diagnosis. MATERIALS AND METHODS For this retrospective study, data from 2000 to 2021 were curated from our database by two orthopaedic surgeons, a radiologist and a data scientist. Patients with complete clinical and pre-therapy radiographic data were eligible. To ensure feasibility, the ten most frequent primary tumour entities, confirmed histologically or by tumour board decision, were included. We implemented a ResNet and transformer model to establish baseline results. Our method extracts image features using deep learning and then clusters the k most similar images to the target image using a hash-based nearest-neighbour recommender approach that performs simultaneous classification by majority voting. The results were evaluated with precision-at-k, accuracy, precision and recall. Discrete parameters were described by incidence and percentage ratios. For continuous parameters, based on a normality test, respective statistical measures were calculated. RESULTS Included were data from 809 patients (1792 radiographs; mean age 33.73 ± 18.65, range 3-89 years; 443 men), with Osteochondroma (28.31%) and Ewing sarcoma (1.11%) as the most and least common entities, respectively. The dataset was split into training (80%) and test subsets (20%). For k = 3, our model achieved the highest mean accuracy, precision and recall (92.86%, 92.86% and 34.08%), significantly outperforming state-of-the-art models (54.10%, 55.57%, 19.85% and 62.80%, 61.33%, 23.05%). CONCLUSION Our novel approach surpasses current models in tumour classification and links to past patient data, leveraging expert insights. CLINICAL RELEVANCE STATEMENT The proposed algorithm could serve as a vital support tool for clinicians and general practitioners with limited experience in bone tumour classification by identifying similar cases and classifying bone tumour entities. KEY POINTS • Addressed accurate bone tumour classification using radiographic features. • Model achieved 92.86%, 92.86% and 34.08% mean accuracy, precision and recall, respectively, significantly surpassing state-of-the-art models. • Enhanced diagnosis by integrating prior expert patient assessments.
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Affiliation(s)
- Florian Hinterwimmer
- Department of Orthopaedics and Sports Orthopaedics, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.
- Institute for AI and Informatics in Medicine, Technical University of Munich, Munich, Germany.
| | - Ricardo Smits Serena
- Department of Orthopaedics and Sports Orthopaedics, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Institute for AI and Informatics in Medicine, Technical University of Munich, Munich, Germany
| | - Nikolas Wilhelm
- Department of Orthopaedics and Sports Orthopaedics, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Sebastian Breden
- Department of Orthopaedics and Sports Orthopaedics, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Sarah Consalvo
- Department of Orthopaedics and Sports Orthopaedics, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Fritz Seidl
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, Germany
| | - Dominik Juestel
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, Germany
- Institute at Helmholtz: Institute of Computational Biology, Oberschleißheim, Germany
- Chair of Biological Imaging at the Central Institute for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Munich, Germany
| | - Rainer H H Burgkart
- Department of Orthopaedics and Sports Orthopaedics, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Klaus Woertler
- Musculoskeletal Radiology Section, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Ruediger von Eisenhart-Rothe
- Department of Orthopaedics and Sports Orthopaedics, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Jan Neumann
- Musculoskeletal Radiology Section, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Daniel Rueckert
- Institute for AI and Informatics in Medicine, Technical University of Munich, Munich, Germany
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4
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Khanna NN, Singh M, Maindarkar M, Kumar A, Johri AM, Mentella L, Laird JR, Paraskevas KI, Ruzsa Z, Singh N, Kalra MK, Fernandes JFE, Chaturvedi S, Nicolaides A, Rathore V, Singh I, Teji JS, Al-Maini M, Isenovic ER, Viswanathan V, Khanna P, Fouda MM, Saba L, Suri JS. Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review. J Korean Med Sci 2023; 38:e395. [PMID: 38013648 PMCID: PMC10681845 DOI: 10.3346/jkms.2023.38.e395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 10/15/2023] [Indexed: 11/29/2023] Open
Abstract
Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established. It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction. Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans.
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Affiliation(s)
- Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India
- Asia Pacific Vascular Society, New Delhi, India
| | - Manasvi Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
- Bennett University, Greater Noida, India
| | - Mahesh Maindarkar
- Asia Pacific Vascular Society, New Delhi, India
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
- School of Bioengineering Sciences and Research, Maharashtra Institute of Technology's Art, Design and Technology University, Pune, India
| | | | - Amer M Johri
- Department of Medicine, Division of Cardiology, Queen's University, Kingston, Canada
| | - Laura Mentella
- Department of Medicine, Division of Cardiology, University of Toronto, Toronto, Canada
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA, USA
| | | | - Zoltan Ruzsa
- Invasive Cardiology Division, University of Szeged, Szeged, Hungary
| | - Narpinder Singh
- Department of Food Science and Technology, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India
| | | | | | - Seemant Chaturvedi
- Department of Neurology & Stroke Program, University of Maryland, Baltimore, MD, USA
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre and University of Nicosia Medical School, Cyprus
| | - Vijay Rathore
- Nephrology Department, Kaiser Permanente, Sacramento, CA, USA
| | - Inder Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
| | - Jagjit S Teji
- Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Mostafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON, Canada
| | - Esma R Isenovic
- Department of Radiobiology and Molecular Genetics, National Institute of The Republic of Serbia, University of Belgrade, Beograd, Serbia
| | | | - Puneet Khanna
- Department of Anaesthesiology, AIIMS, New Delhi, India
| | - Mostafa M Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID, USA
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria, Cagliari, Italy
| | - Jasjit S Suri
- Asia Pacific Vascular Society, New Delhi, India
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
- Department of Computer Engineering, Graphic Era Deemed to be University, Dehradun, India.
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5
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Giuste FO, He L, Lais P, Shi W, Zhu Y, Hornback A, Tsai C, Isgut M, Anderson B, Wang MD. Early and fair COVID-19 outcome risk assessment using robust feature selection. Sci Rep 2023; 13:18981. [PMID: 37923795 PMCID: PMC10624921 DOI: 10.1038/s41598-023-36175-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 05/29/2023] [Indexed: 11/06/2023] Open
Abstract
Personalized medicine plays an important role in treatment optimization for COVID-19 patient management. Early treatment in patients at high risk of severe complications is vital to prevent death and ventilator use. Predicting COVID-19 clinical outcomes using machine learning may provide a fast and data-driven solution for optimizing patient care by estimating the need for early treatment. In addition, it is essential to accurately predict risk across demographic groups, particularly those underrepresented in existing models. Unfortunately, there is a lack of studies demonstrating the equitable performance of machine learning models across patient demographics. To overcome this existing limitation, we generate a robust machine learning model to predict patient-specific risk of death or ventilator use in COVID-19 positive patients using features available at the time of diagnosis. We establish the value of our solution across patient demographics, including gender and race. In addition, we improve clinical trust in our automated predictions by generating interpretable patient clustering, patient-level clinical feature importance, and global clinical feature importance within our large real-world COVID-19 positive patient dataset. We achieved 89.38% area under receiver operating curve (AUROC) performance for severe outcomes prediction and our robust feature ranking approach identified the presence of dementia as a key indicator for worse patient outcomes. We also demonstrated that our deep-learning clustering approach outperforms traditional clustering in separating patients by severity of outcome based on mutual information performance. Finally, we developed an application for automated and fair patient risk assessment with minimal manual data entry using existing data exchange standards.
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Affiliation(s)
- Felipe O Giuste
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30322, USA
| | - Lawrence He
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30322, USA
| | - Peter Lais
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30322, USA
| | - Wenqi Shi
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30322, USA
| | - Yuanda Zhu
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30322, USA
| | - Andrew Hornback
- School of Computer Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30322, USA
| | - Chiche Tsai
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30322, USA
| | - Monica Isgut
- School of Biology, Georgia Institute of Technology, Atlanta, GA, 30322, USA
| | - Blake Anderson
- Department of Medicine, Emory University, Atlanta, GA, 30322, USA
| | - May D Wang
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30322, USA.
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6
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Woodman RJ, Mangoni AA. A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future. Aging Clin Exp Res 2023; 35:2363-2397. [PMID: 37682491 PMCID: PMC10627901 DOI: 10.1007/s40520-023-02552-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 08/24/2023] [Indexed: 09/09/2023]
Abstract
The increasing access to health data worldwide is driving a resurgence in machine learning research, including data-hungry deep learning algorithms. More computationally efficient algorithms now offer unique opportunities to enhance diagnosis, risk stratification, and individualised approaches to patient management. Such opportunities are particularly relevant for the management of older patients, a group that is characterised by complex multimorbidity patterns and significant interindividual variability in homeostatic capacity, organ function, and response to treatment. Clinical tools that utilise machine learning algorithms to determine the optimal choice of treatment are slowly gaining the necessary approval from governing bodies and being implemented into healthcare, with significant implications for virtually all medical disciplines during the next phase of digital medicine. Beyond obtaining regulatory approval, a crucial element in implementing these tools is the trust and support of the people that use them. In this context, an increased understanding by clinicians of artificial intelligence and machine learning algorithms provides an appreciation of the possible benefits, risks, and uncertainties, and improves the chances for successful adoption. This review provides a broad taxonomy of machine learning algorithms, followed by a more detailed description of each algorithm class, their purpose and capabilities, and examples of their applications, particularly in geriatric medicine. Additional focus is given on the clinical implications and challenges involved in relying on devices with reduced interpretability and the progress made in counteracting the latter via the development of explainable machine learning.
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Affiliation(s)
- Richard J Woodman
- Centre of Epidemiology and Biostatistics, College of Medicine and Public Health, Flinders University, GPO Box 2100, Adelaide, SA, 5001, Australia.
| | - Arduino A Mangoni
- Discipline of Clinical Pharmacology, College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
- Department of Clinical Pharmacology, Flinders Medical Centre, Southern Adelaide Local Health Network, Adelaide, SA, Australia
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