1
|
Shah C, Nachand D, Wald C, Chen PH. Keeping Patient Data Secure in the Age of Radiology Artificial Intelligence: Cybersecurity Considerations and Future Directions. J Am Coll Radiol 2023; 20:828-835. [PMID: 37488026 DOI: 10.1016/j.jacr.2023.06.023] [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: 04/24/2023] [Accepted: 06/14/2023] [Indexed: 07/26/2023]
Abstract
Artificial intelligence (AI)-based solutions are increasingly being incorporated into radiology workflows. Implementation of AI comes along with cybersecurity risks and challenges that practices should be aware of and mitigate for a successful and secure deployment. In this article, these cybersecurity issues are examined through the lens of the "CIA" triad framework-confidentiality, integrity, and availability. We discuss the implications of implementation configurations and development approaches on data security and confidentiality and the potential impact that the insertion of AI can have on the truthfulness of data, access to data, and the cybersecurity attack surface. Finally, we provide a checklist to address important security considerations before deployment of an AI application, and discuss future advances in AI addressing some of these security concerns.
Collapse
Affiliation(s)
- Chintan Shah
- Associate Staff, Section of Neuroradiology and Section of Imaging Informatics, Safety, Improvement, Quality and Experience Officer-Neuroradiology, Department of Radiology, Imaging Institute, Cleveland Clinic, Cleveland, Ohio.
| | - Douglas Nachand
- Staff, Section of Abdominal Imaging and Section of Imaging Informatics, Cleveland Clinic, Cleveland, Ohio
| | - Christoph Wald
- Professor of Radiology, Tufts University Medical School, Lahey Health, Boston, Massachusetts; Chair, Lahey Radiology, Chair, ACR Informatics Commission. https://twitter.com/waldchristoph
| | - Po-Hao Chen
- Chief Informatics Officer, Imaging Institute, Medical Director for Enterprise Radiology, IT Division, Staff, Section of Musculoskeletal Imaging, Cleveland Clinic, Cleveland, Ohio Chair, Informatics Advisory Council, ACR; Co-Chair, 2023 Data Science Summit, ACR. https://twitter.com/howardpchen
| |
Collapse
|
2
|
Nazir S, Dickson DM, Akram MU. Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks. Comput Biol Med 2023; 156:106668. [PMID: 36863192 DOI: 10.1016/j.compbiomed.2023.106668] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Revised: 01/12/2023] [Accepted: 02/10/2023] [Indexed: 02/21/2023]
Abstract
Artificial Intelligence (AI) techniques of deep learning have revolutionized the disease diagnosis with their outstanding image classification performance. In spite of the outstanding results, the widespread adoption of these techniques in clinical practice is still taking place at a moderate pace. One of the major hindrance is that a trained Deep Neural Networks (DNN) model provides a prediction, but questions about why and how that prediction was made remain unanswered. This linkage is of utmost importance for the regulated healthcare domain to increase the trust in the automated diagnosis system by the practitioners, patients and other stakeholders. The application of deep learning for medical imaging has to be interpreted with caution due to the health and safety concerns similar to blame attribution in the case of an accident involving autonomous cars. The consequences of both a false positive and false negative cases are far reaching for patients' welfare and cannot be ignored. This is exacerbated by the fact that the state-of-the-art deep learning algorithms comprise of complex interconnected structures, millions of parameters, and a 'black box' nature, offering little understanding of their inner working unlike the traditional machine learning algorithms. Explainable AI (XAI) techniques help to understand model predictions which help develop trust in the system, accelerate the disease diagnosis, and meet adherence to regulatory requirements. This survey provides a comprehensive review of the promising field of XAI for biomedical imaging diagnostics. We also provide a categorization of the XAI techniques, discuss the open challenges, and provide future directions for XAI which would be of interest to clinicians, regulators and model developers.
Collapse
Affiliation(s)
- Sajid Nazir
- Department of Computing, Glasgow Caledonian University, Glasgow, UK.
| | - Diane M Dickson
- Department of Podiatry and Radiography, Research Centre for Health, Glasgow Caledonian University, Glasgow, UK
| | - Muhammad Usman Akram
- Computer and Software Engineering Department, National University of Sciences and Technology, Islamabad, Pakistan
| |
Collapse
|
3
|
Simpson-Page E, Coogan P, Kron T, Lowther N, Murray R, Noble C, Smith I, Wilks R, Crowe SB. Webinar and survey on quality management principles within the Australian and New Zealand ACPSEM Workforce. Phys Eng Sci Med 2022; 45:679-685. [PMID: 35834171 DOI: 10.1007/s13246-022-01160-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Healthcare relies upon the accurate and safe delivery of patient care. This is only achievable when systems are developed to ensure high quality, robust outcomes, for instance quality management systems. The concept of quality management can take on a different meaning depending on the context in which it is found. To add complication, the amount of education required for quality management will vary depending on one's exposure to the implementation of quality systems. In part to address these issues, the Australasian College of Physical Scientists and Engineers in Medicine (ACPSEM) Queensland Branch held a quality management webinar for members and non-members across Australia and New Zealand. The purpose of the webinar was to educate and facilitate discussion regarding the application of quality management principles for the ACPSEM profession. In conjunction, a pre- and post-webinar survey was conducted to gain an insight into existing knowledge and attitudes within the professions governed by the ACPSEM and students undertaking related studies. This paper authored by the webinar speakers reintroduces the quality management principles that were discussed in webinar, exemplifies the importance of quality management skills within the ACPSEM professions and presents the results of the surveys, promoting the need for more educational resources on quality management tools.
Collapse
Affiliation(s)
- Emily Simpson-Page
- Cancer Care Services, Royal Brisbane and Women's Hospital, Brisbane, Australia.
| | - Paul Coogan
- Q-TRaCE, Department of Nuclear Medicine & Specialised PET Services Queensland, Royal Brisbane and Women's Hospital, Brisbane, Australia
| | - Tomas Kron
- Physical Sciences Department, Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Nicholas Lowther
- Wellington Blood & Cancer Centre, Wellington Hospital, Wellington, New Zealand
| | - Rebecca Murray
- Herston Biofabrication Institute, Metro North Hospital and Health Service, Brisbane, Australia
| | - Christopher Noble
- Department of Radiation Oncology, Princess Alexandra Hospital, Brisbane, Australia
| | - Ian Smith
- St. Andrews War Memorial Hospital, Brisbane, Australia
| | - Rachael Wilks
- Cancer Care Services, Royal Brisbane and Women's Hospital, Brisbane, Australia.,Herston Biofabrication Institute, Metro North Hospital and Health Service, Brisbane, Australia.,School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia
| | - Scott B Crowe
- Cancer Care Services, Royal Brisbane and Women's Hospital, Brisbane, Australia.,Herston Biofabrication Institute, Metro North Hospital and Health Service, Brisbane, Australia.,School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia.,School of Chemistry and Physics, Queensland University of Technology, Brisbane, Australia
| |
Collapse
|
4
|
Automatic Breast Tumor Screening of Mammographic Images with Optimal Convolutional Neural Network. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12084079] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Mammography is a first-line imaging examination approach used for early breast tumor screening. Computational techniques based on deep-learning methods, such as convolutional neural network (CNN), are routinely used as classifiers for rapid automatic breast tumor screening in mammography examination. Classifying multiple feature maps on two-dimensional (2D) digital images, a multilayer CNN has multiple convolutional-pooling layers and fully connected networks, which can increase the screening accuracy and reduce the error rate. However, this multilayer architecture presents some limitations, such as high computational complexity, large-scale training dataset requirements, and poor suitability for real-time clinical applications. Hence, this study designs an optimal multilayer architecture for a CNN-based classifier for automatic breast tumor screening, consisting of three convolutional layers, two pooling layers, a flattening layer, and a classification layer. In the first convolutional layer, the proposed classifier performs the fractional-order convolutional process to enhance the image and remove unwanted noise for obtaining the desired object’s edges; in the second and third convolutional-pooling layers, two kernel convolutional and pooling operations are used to ensure the continuous enhancement and sharpening of the feature patterns for further extracting of the desired features at different scales and different levels. Moreover, there is a reduction of the dimensions of the feature patterns. In the classification layer, a multilayer network with an adaptive moment estimation algorithm is used to refine a classifier’s network parameters for mammography classification by separating tumor-free feature patterns from tumor feature patterns. Images can be selected from a curated breast imaging subset of a digital database for screening mammography (CBIS-DDSM), and K-fold cross-validations are performed. The experimental results indicate promising performance for automatic breast tumor screening in terms of recall (%), precision (%), accuracy (%), F1 score, and Youden’s index.
Collapse
|
5
|
Retico A, Avanzo M, Boccali T, Bonacorsi D, Botta F, Cuttone G, Martelli B, Salomoni D, Spiga D, Trianni A, Stasi M, Iori M, Talamonti C. Enhancing the impact of Artificial Intelligence in Medicine: A joint AIFM-INFN Italian initiative for a dedicated cloud-based computing infrastructure. Phys Med 2021; 91:140-150. [PMID: 34801873 DOI: 10.1016/j.ejmp.2021.10.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 10/04/2021] [Accepted: 10/05/2021] [Indexed: 12/23/2022] Open
Abstract
Artificial Intelligence (AI) techniques have been implemented in the field of Medical Imaging for more than forty years. Medical Physicists, Clinicians and Computer Scientists have been collaborating since the beginning to realize software solutions to enhance the informative content of medical images, including AI-based support systems for image interpretation. Despite the recent massive progress in this field due to the current emphasis on Radiomics, Machine Learning and Deep Learning, there are still some barriers to overcome before these tools are fully integrated into the clinical workflows to finally enable a precision medicine approach to patients' care. Nowadays, as Medical Imaging has entered the Big Data era, innovative solutions to efficiently deal with huge amounts of data and to exploit large and distributed computing resources are urgently needed. In the framework of a collaboration agreement between the Italian Association of Medical Physicists (AIFM) and the National Institute for Nuclear Physics (INFN), we propose a model of an intensive computing infrastructure, especially suited for training AI models, equipped with secure storage systems, compliant with data protection regulation, which will accelerate the development and extensive validation of AI-based solutions in the Medical Imaging field of research. This solution can be developed and made operational by Physicists and Computer Scientists working on complementary fields of research in Physics, such as High Energy Physics and Medical Physics, who have all the necessary skills to tailor the AI-technology to the needs of the Medical Imaging community and to shorten the pathway towards the clinical applicability of AI-based decision support systems.
Collapse
Affiliation(s)
- Alessandra Retico
- National Institute for Nuclear Physics (INFN), Pisa Division, 56127 Pisa, Italy
| | - Michele Avanzo
- Medical Physics Department, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, 33081 Aviano, Italy
| | - Tommaso Boccali
- National Institute for Nuclear Physics (INFN), Pisa Division, 56127 Pisa, Italy
| | - Daniele Bonacorsi
- University of Bologna, 40126 Bologna, Italy; INFN, Bologna Division, 40126 Bologna, Italy
| | - Francesca Botta
- Medical Physics Unit, Istituto Europeo di oncologia IRCCS, 20141 Milan, Italy
| | - Giacomo Cuttone
- INFN, Southern National Laboratory (LNS), 95123 Catania, Italy
| | | | | | | | - Annalisa Trianni
- Medical Physics Unit, Ospedale Santa Chiara APSS, 38122 Trento, Italy
| | - Michele Stasi
- Medical Physics Unit, A.O. Ordine Mauriziano di Torino, 10128 Torino, Italy
| | - Mauro Iori
- Medical Physics Unit, Azienda USL-IRCCS di Reggio Emilia, 42122 Reggio Emilia, Italy.
| | - Cinzia Talamonti
- Department Biomedical Experimental and Clinical Science "Mario Serio", University of Florence, 50134 Florence, Italy; INFN, Florence Division, 50134 Florence, Italy
| |
Collapse
|
6
|
Zanca F, Avanzo M, Colgan N, Crijns W, Guidi G, Hernandez-Giron I, Kagadis GC, Diaz O, Zaidi H, Russo P, Toma-Dasu I, Kortesniemi M. Focus issue: Artificial intelligence in medical physics. Phys Med 2021; 83:287-291. [PMID: 34004585 DOI: 10.1016/j.ejmp.2021.05.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Affiliation(s)
- F Zanca
- Palindromo Consulting, Leuven, Belgium
| | - M Avanzo
- Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Department of Medical Physics, 33081 Aviano, PN, Italy
| | - N Colgan
- School of Physics, National University of Ireland Galway, Galway, Ireland
| | - W Crijns
- Department Oncology, Laboratory of Experimental Radiotherapy, KU Leuven and Department of Radiation Oncology, UZ Leuven, Belgium
| | - G Guidi
- Medical Physics, Az. Ospedaliero-Universitaria di Modena, Modena, Italy
| | - I Hernandez-Giron
- Leiden University Medical Center (LUMC), Radiology Department, Division of Image Processing, Albinusdreef 2, 2333ZA Leiden, The Netherlands
| | - G C Kagadis
- 3DMI Research Group, Department of Medical Physics, School of Medicine, University of Patras, GR 265 04, Greece
| | - O Diaz
- Faculty of Mathematics and Computer Science, University of Barcelona, Barcelona, Spain
| | - H Zaidi
- Geneva University Hospital, Division of Nuclear Medicine and Molecular Imaging, CH-1211 Geneva, Switzerland
| | - P Russo
- Università di Napoli Federico II, Dipartimento di Fisica "Ettore Pancini", I-80126 Naples, Italy
| | - I Toma-Dasu
- Department of Physics, Medical Radiation Physics, Stockholm University, Stockholm, Sweden; Department of Oncology and Pathology, Medical Radiation Physics, Karolinska Institutet, Stockholm, Sweden
| | - M Kortesniemi
- HUS Medical Imaging Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| |
Collapse
|