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Waldner MJ, Strobel D. Ultrasound Diagnosis of Hepatocellular Carcinoma: Is the Future Defined by Artificial Intelligence? ULTRASCHALL IN DER MEDIZIN (STUTTGART, GERMANY : 1980) 2024; 45:8-12. [PMID: 38301631 DOI: 10.1055/a-2171-2674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Affiliation(s)
| | - Deike Strobel
- Medical Clinic 1, Erlangen University Hospital, Erlangen, Germany
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2
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Samala RK, Drukker K, Shukla-Dave A, Chan HP, Sahiner B, Petrick N, Greenspan H, Mahmood U, Summers RM, Tourassi G, Deserno TM, Regge D, Näppi JJ, Yoshida H, Huo Z, Chen Q, Vergara D, Cha KH, Mazurchuk R, Grizzard KT, Huisman H, Morra L, Suzuki K, Armato SG, Hadjiiski L. AI and machine learning in medical imaging: key points from development to translation. BJR ARTIFICIAL INTELLIGENCE 2024; 1:ubae006. [PMID: 38828430 PMCID: PMC11140849 DOI: 10.1093/bjrai/ubae006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 04/02/2024] [Accepted: 04/25/2024] [Indexed: 06/05/2024]
Abstract
Innovation in medical imaging artificial intelligence (AI)/machine learning (ML) demands extensive data collection, algorithmic advancements, and rigorous performance assessments encompassing aspects such as generalizability, uncertainty, bias, fairness, trustworthiness, and interpretability. Achieving widespread integration of AI/ML algorithms into diverse clinical tasks will demand a steadfast commitment to overcoming issues in model design, development, and performance assessment. The complexities of AI/ML clinical translation present substantial challenges, requiring engagement with relevant stakeholders, assessment of cost-effectiveness for user and patient benefit, timely dissemination of information relevant to robust functioning throughout the AI/ML lifecycle, consideration of regulatory compliance, and feedback loops for real-world performance evidence. This commentary addresses several hurdles for the development and adoption of AI/ML technologies in medical imaging. Comprehensive attention to these underlying and often subtle factors is critical not only for tackling the challenges but also for exploring novel opportunities for the advancement of AI in radiology.
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Affiliation(s)
- Ravi K Samala
- Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, 20993, United States
| | - Karen Drukker
- Department of Radiology, University of Chicago, Chicago, IL, 60637, United States
| | - Amita Shukla-Dave
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, United States
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, United States
| | - Heang-Ping Chan
- Department of Radiology, University of Michigan, Ann Arbor, MI, 48109, United States
| | - Berkman Sahiner
- Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, 20993, United States
| | - Nicholas Petrick
- Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, 20993, United States
| | - Hayit Greenspan
- Biomedical Engineering and Imaging Institute, Department of Radiology, Icahn School of Medicine at Mt Sinai, New York, NY, 10029, United States
| | - Usman Mahmood
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, United States
| | - Ronald M Summers
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, 20892, United States
| | - Georgia Tourassi
- Computing and Computational Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN, 37830, United States
| | - Thomas M Deserno
- Peter L. Reichertz Institute for Medical Informatics, TU Braunschweig and Hannover Medical School, Braunschweig, Niedersachsen, 38106, Germany
| | - Daniele Regge
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, 10060, Italy
- Department of Translational Research and of New Surgical and Medical Technologies of the University of Pisa, Pisa, 56126, Italy
| | - Janne J Näppi
- 3D Imaging Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, United States
| | - Hiroyuki Yoshida
- 3D Imaging Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, United States
| | - Zhimin Huo
- Tencent America, Palo Alto, CA, 94306, United States
| | - Quan Chen
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, AZ, 85054, United States
| | - Daniel Vergara
- Department of Radiology, University of Washington, Seattle, WA, 98195, United States
| | - Kenny H Cha
- Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, 20993, United States
| | - Richard Mazurchuk
- Division of Cancer Prevention, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, United States
| | - Kevin T Grizzard
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, 06510, United States
| | - Henkjan Huisman
- Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, Gelderland, 6525 GA, Netherlands
| | - Lia Morra
- Department of Control and Computer Engineering, Politecnico di Torino, Torino, Piemonte, 10129, Italy
| | - Kenji Suzuki
- Institute of Innovative Research, Tokyo Institute of Technology, Midori-ku, Yokohama, Kanagawa, 226-8503, Japan
| | - Samuel G Armato
- Department of Radiology, University of Chicago, Chicago, IL, 60637, United States
| | - Lubomir Hadjiiski
- Department of Radiology, University of Michigan, Ann Arbor, MI, 48109, United States
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Fiagbedzi E, Hasford F, Tagoe SN. The influence of artificial intelligence on the work of the medical physicist in radiotherapy practice: a short review. BJR Open 2023; 5:20230003. [PMID: 37942499 PMCID: PMC10630976 DOI: 10.1259/bjro.20230003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 04/11/2023] [Accepted: 08/02/2023] [Indexed: 11/10/2023] Open
Abstract
There have been many applications and influences of Artificial intelligence (AI) in many sectors and its professionals, that of radiotherapy and the medical physicist is no different. AI and technological advances have necessitated changing roles of medical physicists due to the development of modernized technology with image-guided accessories for the radiotherapy treatment of cancer patients. Given the changing role of medical physicists in ensuring patient safety and optimal care, AI can reshape radiotherapy practice now and in some years to come. Medical physicists' roles in radiotherapy practice have evolved to meet technology for the management of better patient care in the age of modern radiotherapy. This short review provides an insight into the influence of AI on the changing role of medical physicists in each specific chain of the workflow in radiotherapy in which they are involved.
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Affiliation(s)
| | - Francis Hasford
- Department of Medical Physics, Accra-Ghana, University of Ghana, Accra, Ghana
| | - Samuel Nii Tagoe
- Department of Medical Physics, Accra-Ghana, University of Ghana, Accra, Ghana
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El Naqa I, Karolak A, Luo Y, Folio L, Tarhini AA, Rollison D, Parodi K. Translation of AI into oncology clinical practice. Oncogene 2023; 42:3089-3097. [PMID: 37684407 DOI: 10.1038/s41388-023-02826-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 08/23/2023] [Accepted: 08/25/2023] [Indexed: 09/10/2023]
Abstract
Artificial intelligence (AI) is a transformative technology that is capturing popular imagination and can revolutionize biomedicine. AI and machine learning (ML) algorithms have the potential to break through existing barriers in oncology research and practice such as automating workflow processes, personalizing care, and reducing healthcare disparities. Emerging applications of AI/ML in the literature include screening and early detection of cancer, disease diagnosis, response prediction, prognosis, and accelerated drug discovery. Despite this excitement, only few AI/ML models have been properly validated and fewer have become regulated products for routine clinical use. In this review, we highlight the main challenges impeding AI/ML clinical translation. We present different clinical use cases from the domains of radiology, radiation oncology, immunotherapy, and drug discovery in oncology. We dissect the unique challenges and opportunities associated with each of these cases. Finally, we summarize the general requirements for successful AI/ML implementation in the clinic, highlighting specific examples and points of emphasis including the importance of multidisciplinary collaboration of stakeholders, role of domain experts in AI augmentation, transparency of AI/ML models, and the establishment of a comprehensive quality assurance program to mitigate risks of training bias and data drifts, all culminating toward safer and more beneficial AI/ML applications in oncology labs and clinics.
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Affiliation(s)
- Issam El Naqa
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, 33612, USA.
| | - Aleksandra Karolak
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, 33612, USA
| | - Yi Luo
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, 33612, USA
| | - Les Folio
- Diagnostic Imaging & Interventional Radiology, Moffitt Cancer Center, Tampa, FL, 33612, USA
| | - Ahmad A Tarhini
- Cutaneous Oncology and Immunology, Moffitt Cancer Center, Tampa, FL, 33612, USA
| | - Dana Rollison
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, 33612, USA
| | - Katia Parodi
- Department of Medical Physics, Ludwig-Maximilians-Universität München, Munich, Germany
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Mello-Thoms C, Mello CAB. Clinical applications of artificial intelligence in radiology. Br J Radiol 2023; 96:20221031. [PMID: 37099398 PMCID: PMC10546456 DOI: 10.1259/bjr.20221031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 03/28/2023] [Accepted: 03/28/2023] [Indexed: 04/27/2023] Open
Abstract
The rapid growth of medical imaging has placed increasing demands on radiologists. In this scenario, artificial intelligence (AI) has become an attractive partner, one that may complement case interpretation and may aid in various non-interpretive aspects of the work in the radiological clinic. In this review, we discuss interpretative and non-interpretative uses of AI in the clinical practice, as well as report on the barriers to AI's adoption in the clinic. We show that AI currently has a modest to moderate penetration in the clinical practice, with many radiologists still being unconvinced of its value and the return on its investment. Moreover, we discuss the radiologists' liabilities regarding the AI decisions, and explain how we currently do not have regulation to guide the implementation of explainable AI or of self-learning algorithms.
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Affiliation(s)
| | - Carlos A B Mello
- Centro de Informática, Universidade Federal de Pernambuco, Recife, Brazil
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Drabiak K, Kyzer S, Nemov V, El Naqa I. AI and machine learning ethics, law, diversity, and global impact. Br J Radiol 2023; 96:20220934. [PMID: 37191072 PMCID: PMC10546451 DOI: 10.1259/bjr.20220934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 03/20/2023] [Accepted: 03/29/2023] [Indexed: 05/17/2023] Open
Abstract
Artificial intelligence (AI) and its machine learning (ML) algorithms are offering new promise for personalized biomedicine and more cost-effective healthcare with impressive technical capability to mimic human cognitive capabilities. However, widespread application of this promising technology has been limited in the medical domain and expectations have been tampered by ethical challenges and concerns regarding patient privacy, legal responsibility, trustworthiness, and fairness. To balance technical innovation with ethical applications of AI/ML, developers must demonstrate the AI functions as intended and adopt strategies to minimize the risks for failure or bias. This review describes the new ethical challenges created by AI/ML for clinical care and identifies specific considerations for its practice in medicine. We provide an overview of regulatory and legal issues applicable in Europe and the United States, a description of technical aspects to consider, and present recommendations for trustworthy AI/ML that promote transparency, minimize risks of bias or error, and protect the patient well-being.
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Affiliation(s)
- Katherine Drabiak
- Colleges of Public Health and Medicine, University of South Florida, Tampa, FL, USA
| | - Skylar Kyzer
- Colleges of Public Health and Medicine, University of South Florida, Tampa, FL, USA
| | - Valerie Nemov
- Colleges of Public Health and Medicine, University of South Florida, Tampa, FL, USA
| | - Issam El Naqa
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, USA
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Li H, Drukker K, Hu Q, Whitney HM, Fuhrman JD, Giger ML. Predicting intensive care need for COVID-19 patients using deep learning on chest radiography. J Med Imaging (Bellingham) 2023; 10:044504. [PMID: 37608852 PMCID: PMC10440543 DOI: 10.1117/1.jmi.10.4.044504] [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: 01/01/2023] [Revised: 07/12/2023] [Accepted: 08/01/2023] [Indexed: 08/24/2023] Open
Abstract
Purpose Image-based prediction of coronavirus disease 2019 (COVID-19) severity and resource needs can be an important means to address the COVID-19 pandemic. In this study, we propose an artificial intelligence/machine learning (AI/ML) COVID-19 prognosis method to predict patients' needs for intensive care by analyzing chest X-ray radiography (CXR) images using deep learning. Approach The dataset consisted of 8357 CXR exams from 5046 COVID-19-positive patients as confirmed by reverse transcription polymerase chain reaction (RT-PCR) tests for the SARS-CoV-2 virus with a training/validation/test split of 64%/16%/20% on a by patient level. Our model involved a DenseNet121 network with a sequential transfer learning technique employed to train on a sequence of gradually more specific and complex tasks: (1) fine-tuning a model pretrained on ImageNet using a previously established CXR dataset with a broad spectrum of pathologies; (2) refining on another established dataset to detect pneumonia; and (3) fine-tuning using our in-house training/validation datasets to predict patients' needs for intensive care within 24, 48, 72, and 96 h following the CXR exams. The classification performances were evaluated on our independent test set (CXR exams of 1048 patients) using the area under the receiver operating characteristic curve (AUC) as the figure of merit in the task of distinguishing between those COVID-19-positive patients who required intensive care following the imaging exam and those who did not. Results Our proposed AI/ML model achieved an AUC (95% confidence interval) of 0.78 (0.74, 0.81) when predicting the need for intensive care 24 h in advance, and at least 0.76 (0.73, 0.80) for 48 h or more in advance using predictions based on the AI prognostic marker derived from CXR images. Conclusions This AI/ML prediction model for patients' needs for intensive care has the potential to support both clinical decision-making and resource management.
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Affiliation(s)
- Hui Li
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Karen Drukker
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Qiyuan Hu
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Heather M. Whitney
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Jordan D. Fuhrman
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Maryellen L. Giger
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
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Dikici E, Nguyen XV, Takacs N, Prevedello LM. Prediction of model generalizability for unseen data: Methodology and case study in brain metastases detection in T1-Weighted contrast-enhanced 3D MRI. Comput Biol Med 2023; 159:106901. [PMID: 37068317 DOI: 10.1016/j.compbiomed.2023.106901] [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: 12/22/2022] [Revised: 03/08/2023] [Accepted: 04/09/2023] [Indexed: 04/19/2023]
Abstract
BACKGROUND AND PURPOSE A medical AI system's generalizability describes the continuity of its performance acquired from varying geographic, historical, and methodologic settings. Previous literature on this topic has mostly focused on "how" to achieve high generalizability (e.g., via larger datasets, transfer learning, data augmentation, model regularization schemes), with limited success. Instead, we aim to understand "when" the generalizability is achieved: Our study presents a medical AI system that could estimate its generalizability status for unseen data on-the-fly. MATERIALS AND METHODS We introduce a latent space mapping (LSM) approach utilizing Fréchet distance loss to force the underlying training data distribution into a multivariate normal distribution. During the deployment, a given test data's LSM distribution is processed to detect its deviation from the forced distribution; hence, the AI system could predict its generalizability status for any previously unseen data set. If low model generalizability is detected, then the user is informed by a warning message integrated into a sample deployment workflow. While the approach is applicable for most classification deep neural networks (DNNs), we demonstrate its application to a brain metastases (BM) detector for T1-weighted contrast-enhanced (T1c) 3D MRI. The BM detection model was trained using 175 T1c studies acquired internally (from the authors' institution) and tested using (1) 42 internally acquired exams and (2) 72 externally acquired exams from the publicly distributed Brain Mets dataset provided by the Stanford University School of Medicine. Generalizability scores, false positive (FP) rates, and sensitivities of the BM detector were computed for the test datasets. RESULTS AND CONCLUSION The model predicted its generalizability to be low for 31% of the testing data (i.e., two of the internally and 33 of the externally acquired exams), where it produced (1) ∼13.5 false positives (FPs) at 76.1% BM detection sensitivity for the low and (2) ∼10.5 FPs at 89.2% BM detection sensitivity for the high generalizability groups respectively. These results suggest that the proposed formulation enables a model to predict its generalizability for unseen data.
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Affiliation(s)
- Engin Dikici
- The Ohio State University, College of Medicine, Department of Radiology, Columbus, OH, 43210, USA.
| | - Xuan V Nguyen
- The Ohio State University, College of Medicine, Department of Radiology, Columbus, OH, 43210, USA
| | - Noah Takacs
- The Ohio State University, College of Medicine, Department of Radiology, Columbus, OH, 43210, USA
| | - Luciano M Prevedello
- The Ohio State University, College of Medicine, Department of Radiology, Columbus, OH, 43210, USA
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Balakrishnan V, Kehrabi Y, Ramanathan G, Paul SA, Tiong CK. Machine learning approaches in diagnosing tuberculosis through biomarkers - A systematic review. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2023; 179:16-25. [PMID: 36931609 DOI: 10.1016/j.pbiomolbio.2023.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 02/25/2023] [Accepted: 03/09/2023] [Indexed: 03/17/2023]
Abstract
Biomarker-based tests may facilitate Tuberculosis (TB) diagnosis, accelerate treatment initiation, and thus improve outcomes. This review synthesizes the literature on biomarker-based detection for TB diagnosis using machine learning. The systematic review approach follows the PRISMA guideline. Articles were sought using relevant keywords from Web of Science, PubMed, and Scopus, resulting in 19 eligible studies after a meticulous screening. All the studies were found to have focused on the supervised learning approach, with Support Vector Machine (SVM) and Random Forest emerging as the top two algorithms, with the highest accuracy, sensitivity and specificity reported to be 97.0%, 99.2%, and 98.0%, respectively. Further, protein-based biomarkers were widely explored, followed by gene-based such as RNA sequence and, Spoligotypes. Publicly available datasets were observed to be popularly used by the studies reviewed whilst studies targeting specific cohorts such as HIV patients or children gathering their own data from healthcare facilities, leading to smaller datasets. Of these, most studies used the leave one out cross validation technique to mitigate overfitting. The review shows that machine learning is increasingly assessed in research to improve TB diagnosis through biomarkers, as promising results were shown in terms of model's detection performance. This provides insights on the possible application of machine learning approaches to diagnose TB using biomarkers as opposed to the traditional methods that can be time consuming. Low-middle income settings, where access to basic biomarkers could be provided as compared to sputum-based tests that are not always available, could be a major application of such models.
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Affiliation(s)
- Vimala Balakrishnan
- Faculty of Computer Science and Information Technology, Universiti Malaya, 50603, Kuala Lumpur, Malaysia.
| | - Yousra Kehrabi
- Department of Infectious Diseases, Hôpital Bichat-Claude Bernard, Assistance Publique Hôpitaux de Paris, Paris, France
| | - Ghayathri Ramanathan
- Faculty of Computer Science and Information Technology, Universiti Malaya, 50603, Kuala Lumpur, Malaysia
| | - Scott Arjay Paul
- School of Biosciences, Faculty of Health and Medical Sciences, Taylor's University, Subang Jaya, Malaysia
| | - Chiong Kian Tiong
- Faculty of Medicine, Universiti Malaya, 50603, Kuala Lumpur, Malaysia
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Din M, Agarwal S, Grzeda M, Wood DA, Modat M, Booth TC. Detection of cerebral aneurysms using artificial intelligence: a systematic review and meta-analysis. J Neurointerv Surg 2023; 15:262-271. [PMID: 36375834 PMCID: PMC9985742 DOI: 10.1136/jnis-2022-019456] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 10/11/2022] [Indexed: 11/16/2022]
Abstract
BACKGROUND Subarachnoid hemorrhage from cerebral aneurysm rupture is a major cause of morbidity and mortality. Early aneurysm identification, aided by automated systems, may improve patient outcomes. Therefore, a systematic review and meta-analysis of the diagnostic accuracy of artificial intelligence (AI) algorithms in detecting cerebral aneurysms using CT, MRI or DSA was performed. METHODS MEDLINE, Embase, Cochrane Library and Web of Science were searched until August 2021. Eligibility criteria included studies using fully automated algorithms to detect cerebral aneurysms using MRI, CT or DSA. Following Preferred Reporting Items for Systematic Reviews and Meta-Analysis: Diagnostic Test Accuracy (PRISMA-DTA), articles were assessed using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Meta-analysis included a bivariate random-effect model to determine pooled sensitivity, specificity, and area under the receiver operator characteristic curve (ROC-AUC). PROSPERO CRD42021278454. RESULTS 43 studies were included, and 41/43 (95%) were retrospective. 34/43 (79%) used AI as a standalone tool, while 9/43 (21%) used AI assisting a reader. 23/43 (53%) used deep learning. Most studies had high bias risk and applicability concerns, limiting conclusions. Six studies in the standalone AI meta-analysis gave (pooled) 91.2% (95% CI 82.2% to 95.8%) sensitivity; 16.5% (95% CI 9.4% to 27.1%) false-positive rate (1-specificity); 0.936 ROC-AUC. Five reader-assistive AI studies gave (pooled) 90.3% (95% CI 88.0% - 92.2%) sensitivity; 7.9% (95% CI 3.5% to 16.8%) false-positive rate; 0.910 ROC-AUC. CONCLUSION AI has the potential to support clinicians in detecting cerebral aneurysms. Interpretation is limited due to high risk of bias and poor generalizability. Multicenter, prospective studies are required to assess AI in clinical practice.
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Affiliation(s)
- Munaib Din
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Siddharth Agarwal
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Mariusz Grzeda
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - David A Wood
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Thomas C Booth
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Department of Neuroradiology, King's College Hospital NHS Foundation Trust, London, UK
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Pogue JA, Cardenas CE, Cao Y, Popple RA, Soike M, Boggs DH, Stanley DN, Harms J. Leveraging intelligent optimization for automated, cardiac-sparing accelerated partial breast treatment planning. Front Oncol 2023; 13:1130119. [PMID: 36845685 PMCID: PMC9950631 DOI: 10.3389/fonc.2023.1130119] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 02/02/2023] [Indexed: 02/12/2023] Open
Abstract
Background Accelerated partial breast irradiation (APBI) yields similar rates of recurrence and cosmetic outcomes as compared to whole breast radiation therapy (RT) when patients and treatment techniques are appropriately selected. APBI combined with stereotactic body radiation therapy (SBRT) is a promising technique for precisely delivering high levels of radiation while avoiding uninvolved breast tissue. Here we investigate the feasibility of automatically generating high quality APBI plans in the Ethos adaptive workspace with a specific emphasis on sparing the heart. Methods Nine patients (10 target volumes) were utilized to iteratively tune an Ethos APBI planning template for automatic plan generation. Twenty patients previously treated on a TrueBeam Edge accelerator were then automatically replanned using this template without manual intervention or reoptimization. The unbiased validation cohort Ethos plans were benchmarked via adherence to planning objectives, a comparison of DVH and quality indices against the clinical Edge plans, and qualitative reviews by two board-certified radiation oncologists. Results 85% (17/20) of automated validation cohort plans met all planning objectives; three plans did not achieve the contralateral lung V1.5Gy objective, but all other objectives were achieved. Compared to the Eclipse generated plans, the proposed Ethos template generated plans with greater evaluation planning target volume (PTV_Eval) V100% coverage (p = 0.01), significantly decreased heart V1.5Gy (p< 0.001), and increased contralateral breast V5Gy, skin D0.01cc, and RTOG conformity index (p = 0.03, p = 0.03, and p = 0.01, respectively). However, only the reduction in heart dose was significant after correcting for multiple testing. Physicist-selected plans were deemed clinically acceptable without modification for 75% and 90% of plans by physicians A and B, respectively. Physicians A and B scored at least one automatically generated plan as clinically acceptable for 100% and 95% of planning intents, respectively. Conclusions Standard left- and right-sided planning templates automatically generated APBI plans of comparable quality to manually generated plans treated on a stereotactic linear accelerator, with a significant reduction in heart dose compared to Eclipse generated plans. The methods presented in this work elucidate an approach for generating automated, cardiac-sparing APBI treatment plans for daily adaptive RT with high efficiency.
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Affiliation(s)
| | - Carlos E. Cardenas
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Yanan Cao
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Richard A. Popple
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Michael Soike
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Drexell Hunter Boggs
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Dennis N. Stanley
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Joseph Harms
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, AL, United States
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Hatt M, Krizsan AK, Rahmim A, Bradshaw TJ, Costa PF, Forgacs A, Seifert R, Zwanenburg A, El Naqa I, Kinahan PE, Tixier F, Jha AK, Visvikis D. Joint EANM/SNMMI guideline on radiomics in nuclear medicine : Jointly supported by the EANM Physics Committee and the SNMMI Physics, Instrumentation and Data Sciences Council. Eur J Nucl Med Mol Imaging 2023; 50:352-375. [PMID: 36326868 PMCID: PMC9816255 DOI: 10.1007/s00259-022-06001-6] [Citation(s) in RCA: 40] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 10/09/2022] [Indexed: 11/05/2022]
Abstract
PURPOSE The purpose of this guideline is to provide comprehensive information on best practices for robust radiomics analyses for both hand-crafted and deep learning-based approaches. METHODS In a cooperative effort between the EANM and SNMMI, we agreed upon current best practices and recommendations for relevant aspects of radiomics analyses, including study design, quality assurance, data collection, impact of acquisition and reconstruction, detection and segmentation, feature standardization and implementation, as well as appropriate modelling schemes, model evaluation, and interpretation. We also offer an outlook for future perspectives. CONCLUSION Radiomics is a very quickly evolving field of research. The present guideline focused on established findings as well as recommendations based on the state of the art. Though this guideline recognizes both hand-crafted and deep learning-based radiomics approaches, it primarily focuses on the former as this field is more mature. This guideline will be updated once more studies and results have contributed to improved consensus regarding the application of deep learning methods for radiomics. Although methodological recommendations in the present document are valid for most medical image modalities, we focus here on nuclear medicine, and specific recommendations when necessary are made for PET/CT, PET/MR, and quantitative SPECT.
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Affiliation(s)
- M Hatt
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | | | - A Rahmim
- Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada
| | - T J Bradshaw
- Department of Radiology, University of Wisconsin, Madison, WI, USA
| | - P F Costa
- Department of Nuclear Medicine, West German Cancer Center, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany
| | | | - R Seifert
- Department of Nuclear Medicine, West German Cancer Center, University of Duisburg-Essen and German Cancer Consortium (DKTK)-University Hospital Essen, Essen, Germany.
- Department of Nuclear Medicine, Münster University Hospital, Münster, Germany.
| | - A Zwanenburg
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
- National Center for Tumor Diseases (NCT/UCC), Dresden, Germany
- German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - I El Naqa
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, 33626, USA
| | - P E Kinahan
- Imaging Research Laboratory, PET/CT Physics, Department of Radiology, UW Medical Center, University of Washington, Seattle, WA, USA
| | - F Tixier
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
| | - A K Jha
- McKelvey School of Engineering and Mallinckrodt Institute of Radiology, Washington University in St. Louis, Saint Louis, MO, USA
| | - D Visvikis
- LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France
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13
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Terunuma T, Sakae T, Hu Y, Takei H, Moriya S, Okumura T, Sakurai H. Explainability and controllability of patient-specific deep learning with attention-based augmentation for markerless image-guided radiotherapy. Med Phys 2023; 50:480-494. [PMID: 36354286 PMCID: PMC10100026 DOI: 10.1002/mp.16095] [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: 10/18/2021] [Revised: 10/27/2022] [Accepted: 10/27/2022] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND We reported the concept of patient-specific deep learning (DL) for real-time markerless tumor segmentation in image-guided radiotherapy (IGRT). The method was aimed to control the attention of convolutional neural networks (CNNs) by artificial differences in co-occurrence probability (CoOCP) in training datasets, that is, focusing CNN attention on soft tissues while ignoring bones. However, the effectiveness of this attention-based data augmentation has not been confirmed by explainable techniques. Furthermore, compared to reasonable ground truths, the feasibility of tumor segmentation in clinical kilovolt (kV) X-ray fluoroscopic (XF) images has not been confirmed. PURPOSE The first aim of this paper was to present evidence that the proposed method provides an explanation and control of DL behavior. The second purpose was to validate the real-time lung tumor segmentation in clinical kV XF images for IGRT. METHODS This retrospective study included 10 patients with lung cancer. Patient-specific and XF angle-specific image pairs comprising digitally reconstructed radiographs (DRRs) and projected-clinical-target-volume (pCTV) images were calculated from four-dimensional computer tomographic data and treatment planning information. The training datasets were primarily augmented by random overlay (RO) and noise injection (NI): RO aims to differentiate positional CoOCP in soft tissues and bones, and NI aims to make a difference in the frequency of occurrence of local and global image features. The CNNs for each patient-and-angle were automatically optimized in the DL training stage to transform the training DRRs into pCTV images. In the inference stage, the trained CNNs transformed the test XF images into pCTV images, thus identifying target positions and shapes. RESULTS The visual analysis of DL attention heatmaps for a test image demonstrated that our method focused CNN attention on soft tissue and global image features rather than bones and local features. The processing time for each patient-and-angle-specific dataset in the training stage was ∼30 min, whereas that in the inference stage was 8 ms/frame. The estimated three-dimensional 95 percentile tracking error, Jaccard index, and Hausdorff distance for 10 patients were 1.3-3.9 mm, 0.85-0.94, and 0.6-4.9 mm, respectively. CONCLUSIONS The proposed attention-based data augmentation with both RO and NI made the CNN behavior more explainable and more controllable. The results obtained demonstrated the feasibility of real-time markerless lung tumor segmentation in kV XF images for IGRT.
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Affiliation(s)
- Toshiyuki Terunuma
- Faculty of Medicine, University of Tsukuba, Tsukuba, Japan.,Proton Medical Research Center, University of Tsukuba Hospital, Tsukuba, Japan
| | - Takeji Sakae
- Faculty of Medicine, University of Tsukuba, Tsukuba, Japan.,Proton Medical Research Center, University of Tsukuba Hospital, Tsukuba, Japan
| | - Yachao Hu
- Proton Medical Research Center, University of Tsukuba Hospital, Tsukuba, Japan.,Center Hospital and Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Hideyuki Takei
- Faculty of Medicine, University of Tsukuba, Tsukuba, Japan.,Proton Medical Research Center, University of Tsukuba Hospital, Tsukuba, Japan
| | - Shunsuke Moriya
- Faculty of Medicine, University of Tsukuba, Tsukuba, Japan.,Proton Medical Research Center, University of Tsukuba Hospital, Tsukuba, Japan
| | - Toshiyuki Okumura
- Faculty of Medicine, University of Tsukuba, Tsukuba, Japan.,Proton Medical Research Center, University of Tsukuba Hospital, Tsukuba, Japan
| | - Hideyuki Sakurai
- Faculty of Medicine, University of Tsukuba, Tsukuba, Japan.,Proton Medical Research Center, University of Tsukuba Hospital, Tsukuba, Japan
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14
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Niraula D, Cui S, Pakela J, Wei L, Luo Y, Ten Haken RK, El Naqa I. Current status and future developments in predicting outcomes in radiation oncology. Br J Radiol 2022; 95:20220239. [PMID: 35867841 PMCID: PMC9793488 DOI: 10.1259/bjr.20220239] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Advancements in data-driven technologies and the inclusion of information-rich multiomics features have significantly improved the performance of outcomes modeling in radiation oncology. For this current trend to be sustainable, challenges related to robust data modeling such as small sample size, low size to feature ratio, noisy data, as well as issues related to algorithmic modeling such as complexity, uncertainty, and interpretability, need to be mitigated if not resolved. Emerging computational technologies and new paradigms such as federated learning, human-in-the-loop, quantum computing, and novel interpretability methods show great potential in overcoming these challenges and bridging the gap towards precision outcome modeling in radiotherapy. Examples of these promising technologies will be presented and their potential role in improving outcome modeling will be discussed.
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Affiliation(s)
- Dipesh Niraula
- Department of Machine Learning, H Lee Moffitt Cancer Center and Research Institute, Tampa, USA
| | - Sunan Cui
- Department of Radiation Oncology, Stanford Medicine, Stanford University, Stanford, USA
| | - Julia Pakela
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Lise Wei
- Department of Radiation Oncology, University of Michigan, Ann Arbor, USA
| | - Yi Luo
- Department of Machine Learning, H Lee Moffitt Cancer Center and Research Institute, Tampa, USA
| | | | - Issam El Naqa
- Department of Machine Learning, H Lee Moffitt Cancer Center and Research Institute, Tampa, USA
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15
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Baughan N, Douglas L, Giger ML. Past, Present, and Future of Machine Learning and Artificial Intelligence for Breast Cancer Screening. JOURNAL OF BREAST IMAGING 2022; 4:451-459. [PMID: 38416954 DOI: 10.1093/jbi/wbac052] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Indexed: 03/01/2024]
Abstract
Breast cancer screening has evolved substantially over the past few decades because of advancements in new image acquisition systems and novel artificial intelligence (AI) algorithms. This review provides a brief overview of the history, current state, and future of AI in breast cancer screening and diagnosis along with challenges involved in the development of AI systems. Although AI has been developing for interpretation tasks associated with breast cancer screening for decades, its potential to combat the subjective nature and improve the efficiency of human image interpretation is always expanding. The rapid advancement of computational power and deep learning has increased greatly in AI research, with promising performance in detection and classification tasks across imaging modalities. Most AI systems, based on human-engineered or deep learning methods, serve as concurrent or secondary readers, that is, as aids to radiologists for a specific, well-defined task. In the future, AI may be able to perform multiple integrated tasks, making decisions at the level of or surpassing the ability of humans. Artificial intelligence may also serve as a partial primary reader to streamline ancillary tasks, triaging cases or ruling out obvious normal cases. However, before AI is used as an independent, autonomous reader, various challenges need to be addressed, including explainability and interpretability, in addition to repeatability and generalizability, to ensure that AI will provide a significant clinical benefit to breast cancer screening across all populations.
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Affiliation(s)
- Natalie Baughan
- University of Chicago, Department of Radiology Committee on Medical Physics, Chicago, IL, USA
| | - Lindsay Douglas
- University of Chicago, Department of Radiology Committee on Medical Physics, Chicago, IL, USA
| | - Maryellen L Giger
- University of Chicago, Department of Radiology Committee on Medical Physics, Chicago, IL, USA
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16
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Artificial Intelligence for Outcome Modeling in Radiotherapy. Semin Radiat Oncol 2022; 32:351-364. [DOI: 10.1016/j.semradonc.2022.06.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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17
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Goisauf M, Cano Abadía M. Ethics of AI in Radiology: A Review of Ethical and Societal Implications. Front Big Data 2022; 5:850383. [PMID: 35910490 PMCID: PMC9329694 DOI: 10.3389/fdata.2022.850383] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 06/13/2022] [Indexed: 11/13/2022] Open
Abstract
Artificial intelligence (AI) is being applied in medicine to improve healthcare and advance health equity. The application of AI-based technologies in radiology is expected to improve diagnostic performance by increasing accuracy and simplifying personalized decision-making. While this technology has the potential to improve health services, many ethical and societal implications need to be carefully considered to avoid harmful consequences for individuals and groups, especially for the most vulnerable populations. Therefore, several questions are raised, including (1) what types of ethical issues are raised by the use of AI in medicine and biomedical research, and (2) how are these issues being tackled in radiology, especially in the case of breast cancer? To answer these questions, a systematic review of the academic literature was conducted. Searches were performed in five electronic databases to identify peer-reviewed articles published since 2017 on the topic of the ethics of AI in radiology. The review results show that the discourse has mainly addressed expectations and challenges associated with medical AI, and in particular bias and black box issues, and that various guiding principles have been suggested to ensure ethical AI. We found that several ethical and societal implications of AI use remain underexplored, and more attention needs to be paid to addressing potential discriminatory effects and injustices. We conclude with a critical reflection on these issues and the identified gaps in the discourse from a philosophical and STS perspective, underlining the need to integrate a social science perspective in AI developments in radiology in the future.
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18
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Allenby MC, Woodruff MA. Image analyses for engineering advanced tissue biomanufacturing processes. Biomaterials 2022; 284:121514. [DOI: 10.1016/j.biomaterials.2022.121514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Revised: 04/01/2022] [Accepted: 04/04/2022] [Indexed: 11/02/2022]
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19
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Pakela JM, Knopf A, Dong L, Rucinski A, Zou W. Management of Motion and Anatomical Variations in Charged Particle Therapy: Past, Present, and Into the Future. Front Oncol 2022; 12:806153. [PMID: 35356213 PMCID: PMC8959592 DOI: 10.3389/fonc.2022.806153] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 02/04/2022] [Indexed: 12/14/2022] Open
Abstract
The major aim of radiation therapy is to provide curative or palliative treatment to cancerous malignancies while minimizing damage to healthy tissues. Charged particle radiotherapy utilizing carbon ions or protons is uniquely suited for this task due to its ability to achieve highly conformal dose distributions around the tumor volume. For these treatment modalities, uncertainties in the localization of patient anatomy due to inter- and intra-fractional motion present a heightened risk of undesired dose delivery. A diverse range of mitigation strategies have been developed and clinically implemented in various disease sites to monitor and correct for patient motion, but much work remains. This review provides an overview of current clinical practices for inter and intra-fractional motion management in charged particle therapy, including motion control, current imaging and motion tracking modalities, as well as treatment planning and delivery techniques. We also cover progress to date on emerging technologies including particle-based radiography imaging, novel treatment delivery methods such as tumor tracking and FLASH, and artificial intelligence and discuss their potential impact towards improving or increasing the challenge of motion mitigation in charged particle therapy.
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Affiliation(s)
- Julia M Pakela
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, United States
| | - Antje Knopf
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands.,Department I of Internal Medicine, Center for Integrated Oncology Cologne, University Hospital of Cologne, Cologne, Germany
| | - Lei Dong
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, United States
| | - Antoni Rucinski
- Institute of Nuclear Physics, Polish Academy of Sciences, Krakow, Poland
| | - Wei Zou
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, United States
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20
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KARACA YE, ASLAN S, HARK C. SEPARATION OF DOMESTIC WASTE WITH DEEP LEARNING TECHNIQUES. COMPUTER SCIENCE 2022. [DOI: 10.53070/bbd.1071536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Thanks to the rapid development of deep learning technology, smart systems used in almost every part of our daily life are being developed. Developed applications not only made our lives easier, but also contributed positively to nature. Traditional waste separation methods fall short in terms of efficiency and accuracy. In addition to its high cost, it can also cause problems in terms of environmental risks. In recent years, artificial intelligence, machine learning and the deep learning techniques it brings have become a popular method for solving complex problems such as organic, household and packaging waste sorting. In this study, the problem of separation of domestic wastes, which is of great importance in terms of both human and living life and the protection of nature, is discussed. In the artificial intelligence cluster; Classification performances were compared by using popular conventional neural network (CNN) based ResNet-50, DenseNet-121, Inception-V3, VGG16 architectures to detect and sort household waste with deep learning, a sub-branch of machine learning.
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21
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Bockhold S, Foley SJ, Rainford LA, Corridori R, Eberstein A, Hoeschen C, Konijnenberg MW, Molyneux-Hodgson S, Paulo G, Santos J, McNulty JP. Exploring the translational challenge for medical applications of ionising radiation and corresponding radiation protection research. J Transl Med 2022; 20:137. [PMID: 35303930 PMCID: PMC8932076 DOI: 10.1186/s12967-022-03344-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 03/06/2022] [Indexed: 01/19/2023] Open
Abstract
Background Medical applications of ionising radiation and associated radiation protection research often encounter long delays and inconsistent implementation when translated into clinical practice. A coordinated effort is needed to analyse the research needs for innovation transfer in radiation-based high-quality healthcare across Europe which can inform the development of an innovation transfer framework tailored for equitable implementation of radiation research at scale. Methods Between March and September 2021 a Delphi methodology was employed to gain consensus on key translational challenges from a range of professional stakeholders. A total of three Delphi rounds were conducted using a series of electronic surveys comprised of open-ended and closed-type questions. The surveys were disseminated via the EURAMED Rocc-n-Roll consortium network and prominent medical societies in the field. Approximately 350 professionals were invited to participate. Participants’ level of agreement with each generated statement was captured using a 6-point Likert scale. Consensus was defined as median ≥ 4 with ≥ 60% of responses in the upper tertile of the scale. Additionally, the stability of responses across rounds was assessed. Results In the first Delphi round a multidisciplinary panel of 20 generated 127 unique statements. The second and third Delphi rounds recruited a broader sample of 130 individuals to rate the extent to which they agreed with each statement as a key translational challenge. A total of 60 consensus statements resulted from the iterative Delphi process of which 55 demonstrated good stability. Ten statements were identified as high priority challenges with ≥ 80% of statement ratings either ‘Agree’ or ‘Strongly Agree’. Conclusion A lack of interoperability between systems, insufficient resources, unsatisfactory education and training, and the need for greater public awareness surrounding the benefits, risks, and applications of ionising radiation were identified as principal translational challenges. These findings will help to inform a tailored innovation transfer framework for medical radiation research. Supplementary Information The online version contains supplementary material available at 10.1186/s12967-022-03344-4.
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Affiliation(s)
- Sophie Bockhold
- Radiography and Diagnostic Imaging, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland.
| | - Shane J Foley
- Radiography and Diagnostic Imaging, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland
| | - Louise A Rainford
- Radiography and Diagnostic Imaging, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland
| | | | | | - Christoph Hoeschen
- Institute of Medical Engineering, Otto Von Guericke Universität Magdeburg, Magdeburg, Germany
| | - Mark W Konijnenberg
- Department of Radiology and Nuclear Medicine, Erasmus Medical Centre, Rotterdam, Netherlands
| | | | - Graciano Paulo
- Escola Superior de Tecnologia da Saúde, Instituto Politécnico de Coimbra, Coimbra, Portugal
| | - Joana Santos
- Escola Superior de Tecnologia da Saúde, Instituto Politécnico de Coimbra, Coimbra, Portugal
| | - Jonathan P McNulty
- Radiography and Diagnostic Imaging, School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland
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22
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Garau N, Orro A, Summers P, De Maria L, Bertolotti R, Bassis D, Minotti M, De Fiori E, Baroni G, Paganelli C, Rampinelli C. Integrating Biological and Radiological Data in a Structured Repository: a Data Model Applied to the COSMOS Case Study. J Digit Imaging 2022; 35:970-982. [PMID: 35296941 PMCID: PMC9485502 DOI: 10.1007/s10278-022-00615-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 02/17/2022] [Accepted: 02/28/2022] [Indexed: 11/29/2022] Open
Abstract
Integrating the information coming from biological samples with digital data, such as medical images, has gained prominence with the advent of precision medicine. Research in this field faces an ever-increasing amount of data to manage and, as a consequence, the need to structure these data in a functional and standardized fashion to promote and facilitate cooperation among institutions. Inspired by the Minimum Information About BIobank data Sharing (MIABIS), we propose an extended data model which aims to standardize data collections where both biological and digital samples are involved. In the proposed model, strong emphasis is given to the cause-effect relationships among factors as these are frequently encountered in clinical workflows. To test the data model in a realistic context, we consider the Continuous Observation of SMOking Subjects (COSMOS) dataset as case study, consisting of 10 consecutive years of lung cancer screening and follow-up on more than 5000 subjects. The structure of the COSMOS database, implemented to facilitate the process of data retrieval, is therefore presented along with a description of data that we hope to share in a public repository for lung cancer screening research.
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Affiliation(s)
- Noemi Garau
- Dipartimento Di Elettronica, Informazione E Bioingegneria, Politecnico Di Milano, Milano, Italy. .,Division of Radiology, IEO, European Institute of Oncology IRCCS, Milan, Italy.
| | - Alessandro Orro
- Institute for Biomedical Technologies, National Research Council (ITB-CNR), Segrate, Italy
| | - Paul Summers
- Division of Radiology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Lorenza De Maria
- Division of Radiology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Raffaella Bertolotti
- Division of Data Management, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Danny Bassis
- School of Medicine, University of Milan, Milan, Italy
| | - Marta Minotti
- Division of Radiology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Elvio De Fiori
- Division of Radiology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Guido Baroni
- Dipartimento Di Elettronica, Informazione E Bioingegneria, Politecnico Di Milano, Milano, Italy.,Bioengineering Unit, CNAO Foundation, Pavia, Italy
| | - Chiara Paganelli
- Dipartimento Di Elettronica, Informazione E Bioingegneria, Politecnico Di Milano, Milano, Italy
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23
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Attenberger U, Reiser MF. [Future perspectives: how does artificial intelligence influence the development of our profession?]. Radiologe 2022; 62:267-270. [PMID: 35166872 DOI: 10.1007/s00117-022-00969-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/14/2022] [Indexed: 11/28/2022]
Affiliation(s)
- Ulrike Attenberger
- Klinik für diagnostische und interventionelle Radiologie, Universitätsklinikum Bonn, Venusberg-Campus 1, 53127, Bonn, Deutschland.
| | - Maximilian F Reiser
- Klinik und Poliklinik für Radiologie, Klinikum der Universität München, München, Deutschland
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24
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Siddiqui MF, Mouna A, Nicolas G, Rahat SAA, Mitalipova A, Emmanuel N, Tashmatova N. Computational Intelligence: A Step Forward in Cancer Biomarker Discovery and Therapeutic Target Prediction. COMPUTATIONAL INTELLIGENCE IN ONCOLOGY 2022:233-250. [DOI: 10.1007/978-981-16-9221-5_14] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/24/2024]
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25
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Qurashi AA, Alanazi RK, Alhazmi YM, Almohammadi AS, Alsharif WM, Alshamrani KM. Saudi Radiology Personnel's Perceptions of Artificial Intelligence Implementation: A Cross-Sectional Study. J Multidiscip Healthc 2021; 14:3225-3231. [PMID: 34848967 PMCID: PMC8627310 DOI: 10.2147/jmdh.s340786] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 10/29/2021] [Indexed: 12/15/2022] Open
Abstract
PURPOSE Artificial intelligence (AI) in radiology has been a subject of heated debate. The external perception is that algorithms and machines cannot offer better diagnosis than radiologists. Reluctance to implement AI maybe due to the opacity in how AI applications work and the challenging and lengthy validation process. In this study, Saudi radiology personnel's familiarity with AI applications and its usefulness in clinical practice were investigated. METHODS A cross-sectional study was conducted in Saudi Arabia among radiology personnel from March to April 2021. Radiology personnel nationwide were surveyed electronically using Google form. The questionnaire included 12-questions related to AI usefulness in clinical practice and participants' knowledge about AI and their acceptance level to learn and implement this technology into clinical practice. Participants' trust level was also measured; Kruskal-Wallis test was used to examine differences between groups. RESULTS A total of 224 respondents from various radiology-related occupations participated in the survey. The lowest trust level in AI applications was shown by radiologists (p = 0.033). Eighty-two percent of participants (n = 184) had never used AI in their departments. Most respondents (n = 160, 71.4%) reported lack of formal education regarding AI-based applications. Most participants (n = 214, 95.5%) showed strong interest in AI education and are willing to incorporate it into the clinical practice of radiology. Almost half of radiography students (22/46, 47.8%) believe that their job might be at risk due to AI application (p = 0.038). CONCLUSION Radiology personnel's knowledge of AI has a significant impact on their willingness to learn, use and adapt this technology in clinical practice. Participants demonstrated a positive attitude towards AI, showed a reasonable understanding and are highly motivated to learn and incorporate it into clinical practice. Some participants felt that their jobs were threatened by AI adaptation, but this belief might change with good training and education programmes.
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Affiliation(s)
- Abdulaziz A Qurashi
- Diagnostic Radiology Technology Department, College of Applied Medical Sciences, Taibah University, Madinah, Saudi Arabia
| | - Rashed K Alanazi
- Diagnostic Radiology Technology Department, College of Applied Medical Sciences, Taibah University, Madinah, Saudi Arabia
| | - Yasser M Alhazmi
- Diagnostic Radiology Technology Department, College of Applied Medical Sciences, Taibah University, Madinah, Saudi Arabia
| | - Ahmed S Almohammadi
- Diagnostic Radiology Technology Department, College of Applied Medical Sciences, Taibah University, Madinah, Saudi Arabia
| | - Walaa M Alsharif
- Diagnostic Radiology Technology Department, College of Applied Medical Sciences, Taibah University, Madinah, Saudi Arabia
| | - Khalid M Alshamrani
- College of Applied Medical Sciences, King Saud bin Abdulaziz University for Health Sciences, Jeddah, Saudi Arabia
- King Abdullah International Medical Research Center, Jeddah, Saudi Arabia
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26
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Abstract
This article gives a brief overview of the development of artificial intelligence in clinical breast imaging. For multiple decades, artificial intelligence (AI) methods have been developed and translated for breast imaging tasks such as detection, diagnosis, and assessing response to therapy. As imaging modalities arise to support breast cancer screening programs and diagnostic examinations, including full-field digital mammography, breast tomosynthesis, ultrasound, and MRI, AI techniques parallel the efforts with more complex algorithms, faster computers, and larger data sets. AI methods include human-engineered radiomics algorithms and deep learning methods. Examples of these AI-supported clinical tasks are given along with commentary on the future.
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Affiliation(s)
- Qiyuan Hu
- Committee on Medical Physics, Department of Radiology, The University of Chicago, 5841 S Maryland Avenue, MC2026, Chicago, IL 60637, USA
| | - Maryellen L Giger
- Committee on Medical Physics, Department of Radiology, The University of Chicago, 5841 S Maryland Avenue, MC2026, Chicago, IL 60637, USA.
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Han P, Lee SH, Noro K, Haller JW, Nakatsugawa M, Sugiyama S, Bowers M, Lakshminarayanan P, Hoff J, Friedes C, Hu C, McNutt TR, Voong KR, Lee J, Hales RK. Improving Early Identification of Significant Weight Loss Using Clinical Decision Support System in Lung Cancer Radiation Therapy. JCO Clin Cancer Inform 2021; 5:944-952. [PMID: 34473547 DOI: 10.1200/cci.20.00189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
PURPOSE Early identification of patients who may be at high risk of significant weight loss (SWL) is important for timely clinical intervention in lung cancer radiotherapy (RT). A clinical decision support system (CDSS) for SWL prediction was implemented within the routine clinical workflow and assessed on a prospective cohort of patients. MATERIALS AND METHODS CDSS incorporated a machine learning prediction model on the basis of radiomics and dosiomics image features and was connected to a web-based dashboard for streamlined patient enrollment, feature extraction, SWL prediction, and physicians' evaluation processes. Patients with lung cancer (N = 37) treated with definitive RT without prior RT were prospectively enrolled in the study. Radiomics and dosiomics features were extracted from CT and 3D dose volume, and SWL probability (≥ 0.5 considered as SWL) was predicted. Two physicians predicted whether the patient would have SWL before and after reviewing the CDSS prediction. The physician's prediction performance without and with CDSS and prediction changes before and after using CDSS were compared. RESULTS CDSS showed significantly better prediction accuracy than physicians (0.73 v 0.54) with higher specificity (0.81 v 0.50) but with lower sensitivity (0.55 v 0.64). Physicians changed their original prediction after reviewing CDSS prediction for four cases (three correctly and one incorrectly), for all of which CDSS prediction was correct. Physicians' prediction was improved with CDSS in accuracy (0.54-0.59), sensitivity (0.64-0.73), specificity (0.50-0.54), positive predictive value (0.35-0.40), and negative predictive value (0.76-0.82). CONCLUSION Machine learning-based CDSS showed the potential to improve SWL prediction in lung cancer RT. More investigation on a larger patient cohort is needed to properly interpret CDSS prediction performance and its benefit in clinical decision making.
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Affiliation(s)
- Peijin Han
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD
| | - Sang Ho Lee
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD
| | | | | | | | | | - Michael Bowers
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD
| | - Pranav Lakshminarayanan
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD
| | - Jeffrey Hoff
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD
| | - Cole Friedes
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD
| | - Chen Hu
- Department of Oncology Biostatistics and Bioinformatics, Johns Hopkins University, Baltimore, MD
| | - Todd R McNutt
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD
| | - K Ranh Voong
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD
| | - Junghoon Lee
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD
| | - Russell K Hales
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD
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Artificial intelligence: The opinions of radiographers and radiation therapists in Ireland. Radiography (Lond) 2021; 27 Suppl 1:S74-S82. [PMID: 34454835 DOI: 10.1016/j.radi.2021.07.022] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 07/05/2021] [Accepted: 07/24/2021] [Indexed: 11/20/2022]
Abstract
INTRODUCTION Implementation of Artificial Intelligence (AI) into medical imaging is much debated. Diagnostic Radiographers (DRs) and Radiation Therapists (RTTs) are at the forefront of this technological leap, thus an understanding of their views, in particular changes to their current roles, is key to safe, optimal implementation. METHODS An online survey was designed, including themes: role changes, clinical priorities for AI, patient benefits, and education. It was distributed nationally in the Republic of Ireland via the national professional body, clinical management, and social media. RESULTS 318 DRs and 77 RTTs participated. Priority areas for development included quality assurance, clinical audit, radiation dose optimisation, and improved workflow for DRs and treatment planning algorithm optimisation, clinical audit, and post processing for RTTs. There was resistance regarding AI use for patient facing roles and final image interpretation. 27.6% of DRs and 40.3% of RTTs currently use AI clinically and 46.1% of DRs and 41.2% of RTTs anticipate reduced staffing levels with AI. 64.9% of DRs and 70.6% of RTTs felt AI will be positive for patients, with the majority promoting AI regulation through national legislation. 86.1% of DRs and 94.0% of RTTs were favourable to AI implementation. CONCLUSION This research identifies priority AI development and implementation areas for DRs and RTTs. It thus highlights that DRs and RTTs should be involved in development of AI tools that would best support practice, and that clearly defined pathways for AI implementation into these key professions requires discussion so that optimum use and patient safety can ensue. IMPLICATIONS FOR PRACTICE Understanding opinions of AI has significant implications for practice, for ensuring optimal product development, implementation, and training, together with planning for potential DR and RTT role changes.
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Mahmood U, Shrestha R, Bates DDB, Mannelli L, Corrias G, Erdi YE, Kanan C. Detecting Spurious Correlations With Sanity Tests for Artificial Intelligence Guided Radiology Systems. Front Digit Health 2021; 3:671015. [PMID: 34713144 PMCID: PMC8521929 DOI: 10.3389/fdgth.2021.671015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 06/29/2021] [Indexed: 11/23/2022] Open
Abstract
Artificial intelligence (AI) has been successful at solving numerous problems in machine perception. In radiology, AI systems are rapidly evolving and show progress in guiding treatment decisions, diagnosing, localizing disease on medical images, and improving radiologists' efficiency. A critical component to deploying AI in radiology is to gain confidence in a developed system's efficacy and safety. The current gold standard approach is to conduct an analytical validation of performance on a generalization dataset from one or more institutions, followed by a clinical validation study of the system's efficacy during deployment. Clinical validation studies are time-consuming, and best practices dictate limited re-use of analytical validation data, so it is ideal to know ahead of time if a system is likely to fail analytical or clinical validation. In this paper, we describe a series of sanity tests to identify when a system performs well on development data for the wrong reasons. We illustrate the sanity tests' value by designing a deep learning system to classify pancreatic cancer seen in computed tomography scans.
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Affiliation(s)
- Usman Mahmood
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Robik Shrestha
- Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, United States
| | - David D. B. Bates
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Lorenzo Mannelli
- Institute of Research and Medical Care (IRCCS) SDN, Institute of Diagnostic and Nuclear Research, Naples, Italy
| | - Giuseppe Corrias
- Department of Radiology, University of Cagliari, Cagliari, Italy
| | - Yusuf Emre Erdi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Christopher Kanan
- Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, United States
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Thwaites D, Moses D, Haworth A, Barton M, Holloway L. Artificial intelligence in medical imaging and radiation oncology: Opportunities and challenges. J Med Imaging Radiat Oncol 2021; 65:481-485. [PMID: 34342138 DOI: 10.1111/1754-9485.13275] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 06/10/2021] [Indexed: 12/29/2022]
Affiliation(s)
- David Thwaites
- Institute of Medical Physics, School of Physics, University of Sydney, Sydney, New South Wales, Australia.,Sydney West Radiation Oncology Network, Crown Princess Mary Cancer Centre, Sydney, New South Wales, Australia
| | - Daniel Moses
- Department of Medical Imaging, Randwick Campus Hospitals, Sydney, New South Wales, Australia.,School of Biomedical Engineering, University of New South Wales, Sydney, New South Wales, Australia
| | - Annette Haworth
- Institute of Medical Physics, School of Physics, University of Sydney, Sydney, New South Wales, Australia
| | - Michael Barton
- Ingham Institute and South Western Clinical School, University of New South Wales, Sydney, New South Wales, Australia.,Liverpool and Macarthur Cancer Therapy Centres, Sydney, New South Wales, Australia
| | - Lois Holloway
- Institute of Medical Physics, School of Physics, University of Sydney, Sydney, New South Wales, Australia.,Ingham Institute and South Western Clinical School, University of New South Wales, Sydney, New South Wales, Australia.,Liverpool and Macarthur Cancer Therapy Centres, Sydney, New South Wales, Australia.,Centre for Medical Radiation Physics, University of Wollongong, Wollongong, New South Wales, Australia
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31
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Han EY, Cardenas CE, Nguyen C, Hancock D, Xiao Y, Mumme R, Court LE, Rhee DJ, Netherton TJ, Li J, Yeboa DN, Wang C, Briere TM, Balter P, Martel MK, Wen Z. Clinical implementation of automated treatment planning for whole-brain radiotherapy. J Appl Clin Med Phys 2021; 22:94-102. [PMID: 34250715 PMCID: PMC8425887 DOI: 10.1002/acm2.13350] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 05/19/2021] [Accepted: 06/17/2021] [Indexed: 12/22/2022] Open
Abstract
The purpose of the study was to develop and clinically deploy an automated, deep learning‐based approach to treatment planning for whole‐brain radiotherapy (WBRT). We collected CT images and radiotherapy treatment plans to automate a beam aperture definition from 520 patients who received WBRT. These patients were split into training (n = 312), cross‐validation (n = 104), and test (n = 104) sets which were used to train and evaluate a deep learning model. The DeepLabV3+ architecture was trained to automatically define the beam apertures on lateral‐opposed fields using digitally reconstructed radiographs (DRRs). For the beam aperture evaluation, 1st quantitative analysis was completed using a test set before clinical deployment and 2nd quantitative analysis was conducted 90 days after clinical deployment. The mean surface distance and the Hausdorff distances were compared in the anterior‐inferior edge between the clinically used and the predicted fields. Clinically used plans and deep‐learning generated plans were evaluated by various dose–volume histogram metrics of brain, cribriform plate, and lens. The 1st quantitative analysis showed that the average mean surface distance and Hausdorff distance were 7.1 mm (±3.8 mm) and 11.2 mm (±5.2 mm), respectively, in the anterior–inferior edge of the field. The retrospective dosimetric comparison showed that brain dose coverage (D99%, D95%, D1%) of the automatically generated plans was 29.7, 30.3, and 32.5 Gy, respectively, and the average dose of both lenses was up to 19.0% lower when compared to the clinically used plans. Following the clinical deployment, the 2nd quantitative analysis showed that the average mean surface distance and Hausdorff distance between the predicted and clinically used fields were 2.6 mm (±3.2 mm) and 4.5 mm (±5.6 mm), respectively. In conclusion, the automated patient‐specific treatment planning solution for WBRT was implemented in our clinic. The predicted fields appeared consistent with clinically used fields and the predicted plans were dosimetrically comparable.
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Affiliation(s)
- Eun Young Han
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Carlos E Cardenas
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Callistus Nguyen
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Donald Hancock
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yao Xiao
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Raymond Mumme
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Laurence E Court
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Dong Joo Rhee
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Tucker J Netherton
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jing Li
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Debra Nana Yeboa
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Chenyang Wang
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Tina M Briere
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Peter Balter
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Mary K Martel
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Zhifei Wen
- Department of Radiation Oncology, Hoag Hospital, Newport Beach, CA, USA
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32
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El Naqa I. Prospective clinical deployment of machine learning in radiation oncology. Nat Rev Clin Oncol 2021; 18:605-606. [PMID: 34244694 DOI: 10.1038/s41571-021-00541-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Issam El Naqa
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, USA.
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33
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Hope A, Verduin M, Dilling TJ, Choudhury A, Fijten R, Wee L, Aerts HJWL, El Naqa I, Mitchell R, Vooijs M, Dekker A, de Ruysscher D, Traverso A. Artificial Intelligence Applications to Improve the Treatment of Locally Advanced Non-Small Cell Lung Cancers. Cancers (Basel) 2021; 13:2382. [PMID: 34069307 PMCID: PMC8156328 DOI: 10.3390/cancers13102382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 04/21/2021] [Accepted: 05/03/2021] [Indexed: 11/16/2022] Open
Abstract
Locally advanced non-small cell lung cancer patients represent around one third of newly diagnosed lung cancer patients. There remains a large unmet need to find treatment strategies that can improve the survival of these patients while minimizing therapeutical side effects. Increasing the availability of patients' data (imaging, electronic health records, patients' reported outcomes, and genomics) will enable the application of AI algorithms to improve therapy selections. In this review, we discuss how artificial intelligence (AI) can be integral to improving clinical decision support systems. To realize this, a roadmap for AI must be defined. We define six milestones involving a broad spectrum of stakeholders, from physicians to patients, that we feel are necessary for an optimal transition of AI into the clinic.
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Affiliation(s)
- Andrew Hope
- Department of Radiation Oncology, University of Toronto, Toronto, ON 5MT 1P5, Canada;
- Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, ON 5MT 1P5, Canada
| | - Maikel Verduin
- Department of Radiation Oncology (Maastro) GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands; (M.V.); (A.C.); (R.F.); (L.W.); (M.V.); (A.D.); (D.d.R.)
| | - Thomas J Dilling
- Department of Radiation Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA;
| | - Ananya Choudhury
- Department of Radiation Oncology (Maastro) GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands; (M.V.); (A.C.); (R.F.); (L.W.); (M.V.); (A.D.); (D.d.R.)
| | - Rianne Fijten
- Department of Radiation Oncology (Maastro) GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands; (M.V.); (A.C.); (R.F.); (L.W.); (M.V.); (A.D.); (D.d.R.)
| | - Leonard Wee
- Department of Radiation Oncology (Maastro) GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands; (M.V.); (A.C.); (R.F.); (L.W.); (M.V.); (A.D.); (D.d.R.)
| | - Hugo JWL Aerts
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA 02115, USA;
- Department of Radiation Oncology, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA
- Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, 6228 ET Maastricht, The Netherlands
| | - Issam El Naqa
- Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA; (I.E.N.); (R.M.)
| | - Ross Mitchell
- Department of Machine Learning, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA; (I.E.N.); (R.M.)
| | - Marc Vooijs
- Department of Radiation Oncology (Maastro) GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands; (M.V.); (A.C.); (R.F.); (L.W.); (M.V.); (A.D.); (D.d.R.)
| | - Andre Dekker
- Department of Radiation Oncology (Maastro) GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands; (M.V.); (A.C.); (R.F.); (L.W.); (M.V.); (A.D.); (D.d.R.)
| | - Dirk de Ruysscher
- Department of Radiation Oncology (Maastro) GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands; (M.V.); (A.C.); (R.F.); (L.W.); (M.V.); (A.D.); (D.d.R.)
| | - Alberto Traverso
- Department of Radiation Oncology (Maastro) GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, 6229 ET Maastricht, The Netherlands; (M.V.); (A.C.); (R.F.); (L.W.); (M.V.); (A.D.); (D.d.R.)
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Bosmans H, Zanca F, Gelaude F. Procurement, commissioning and QA of AI based solutions: An MPE's perspective on introducing AI in clinical practice. Phys Med 2021; 83:257-263. [PMID: 33984579 DOI: 10.1016/j.ejmp.2021.04.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 03/24/2021] [Accepted: 04/06/2021] [Indexed: 12/11/2022] Open
Abstract
PURPOSE In this study, we propose a framework to help the MPE take up a unique and important role at the introduction of AI solutions in clinical practice, and more in particular at procurement, acceptance, commissioning and QA. MATERIAL AND METHODS The steps for the introduction of Medical Radiological Equipment in a hospital setting were extrapolated to AI tools. Literature review and in-house experience was added to prepare similar, yet dedicated test methods. RESULTS Procurement starts from the clinical cases to be solved and is usually a complex process with many stakeholders and possibly many candidate AI solutions. Specific KPIs and metrics need to be defined. Acceptance testing follows, to verify the installation and test for critical exams. Commissioning should test the suitability of the AI tool for the intended use in the local institution. Results may be predicted from peer reviewed papers that treat representative populations. If not available, local data sets can be prepared to assess the KPIs, or 'virtual clinical trials' could be used to create large, simulated test data sets. Quality assurance must be performed periodically to verify if KPIs are stable, especially if the software is upscaled or upgraded, and as soon as self-learning AI tools would enter the medical practice. DISCUSSION MPEs are well placed to bridge between manufacturer and medical team and help from procurement up to reporting to the management board. More work is needed to establish consolidated test protocols.
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Affiliation(s)
- Hilde Bosmans
- University Hospitals of the KU Leuven, Leuven, Belgium.
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35
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Deep Learning Based Airway Segmentation Using Key Point Prediction. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11083501] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
The purpose of this study was to investigate the accuracy of the airway volume measurement by a Regression Neural Network-based deep-learning model. A set of manually outlined airway data was set to build the algorithm for fully automatic segmentation of a deep learning process. Manual landmarks of the airway were determined by one examiner using a mid-sagittal plane of cone-beam computed tomography (CBCT) images of 315 patients. Clinical dataset-based training with data augmentation was conducted. Based on the annotated landmarks, the airway passage was measured and segmented. The accuracy of our model was confirmed by measuring the following between the examiner and the program: (1) a difference in volume of nasopharynx, oropharynx, and hypopharynx, and (2) the Euclidean distance. For the agreement analysis, 61 samples were extracted and compared. The correlation test showed a range of good to excellent reliability. A difference between volumes were analyzed using regression analysis. The slope of the two measurements was close to 1 and showed a linear regression correlation (r2 = 0.975, slope = 1.02, p < 0.001). These results indicate that fully automatic segmentation of the airway is possible by training via deep learning of artificial intelligence. Additionally, a high correlation between manual data and deep learning data was estimated.
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Zaidi H, El Naqa I. Quantitative Molecular Positron Emission Tomography Imaging Using Advanced Deep Learning Techniques. Annu Rev Biomed Eng 2021; 23:249-276. [PMID: 33797938 DOI: 10.1146/annurev-bioeng-082420-020343] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The widespread availability of high-performance computing and the popularity of artificial intelligence (AI) with machine learning and deep learning (ML/DL) algorithms at the helm have stimulated the development of many applications involving the use of AI-based techniques in molecular imaging research. Applications reported in the literature encompass various areas, including innovative design concepts in positron emission tomography (PET) instrumentation, quantitative image reconstruction and analysis techniques, computer-aided detection and diagnosis, as well as modeling and prediction of outcomes. This review reflects the tremendous interest in quantitative molecular imaging using ML/DL techniques during the past decade, ranging from the basic principles of ML/DL techniques to the various steps required for obtaining quantitatively accurate PET data, including algorithms used to denoise or correct for physical degrading factors as well as to quantify tracer uptake and metabolic tumor volume for treatment monitoring or radiation therapy treatment planning and response prediction.This review also addresses future opportunities and current challenges facing the adoption of ML/DL approaches and their role in multimodality imaging.
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Affiliation(s)
- Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, 1211 Geneva, Switzerland; .,Geneva Neuroscience Centre, University of Geneva, 1205 Geneva, Switzerland.,Department of Nuclear Medicine and Molecular Imaging, University of Groningen, 9700 RB Groningen, Netherlands.,Department of Nuclear Medicine, University of Southern Denmark, DK-5000 Odense, Denmark
| | - Issam El Naqa
- Department of Machine Learning, Moffitt Cancer Center, Tampa, Florida 33612, USA.,Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan 48109, USA.,Department of Oncology, McGill University, Montreal, Quebec H3A 1G5, Canada
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Abstract
Positron emission tomography (PET)/computed tomography (CT) are nuclear diagnostic imaging modalities that are routinely deployed for cancer staging and monitoring. They hold the advantage of detecting disease related biochemical and physiologic abnormalities in advance of anatomical changes, thus widely used for staging of disease progression, identification of the treatment gross tumor volume, monitoring of disease, as well as prediction of outcomes and personalization of treatment regimens. Among the arsenal of different functional imaging modalities, nuclear imaging has benefited from early adoption of quantitative image analysis starting from simple standard uptake value normalization to more advanced extraction of complex imaging uptake patterns; thanks to application of sophisticated image processing and machine learning algorithms. In this review, we discuss the application of image processing and machine/deep learning techniques to PET/CT imaging with special focus on the oncological radiotherapy domain as a case study and draw examples from our work and others to highlight current status and future potentials.
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Affiliation(s)
- Lise Wei
- Department of Radiation Oncology, Physics Division, University of Michigan, Ann Arbor, MI
| | - Issam El Naqa
- Department of Radiation Oncology, Physics Division, University of Michigan, Ann Arbor, MI.
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Balagurunathan Y, Mitchell R, El Naqa I. Requirements and reliability of AI in the medical context. Phys Med 2021; 83:72-78. [PMID: 33721700 PMCID: PMC8915137 DOI: 10.1016/j.ejmp.2021.02.024] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 02/04/2021] [Accepted: 02/23/2021] [Indexed: 12/12/2022] Open
Abstract
The digital information age has been a catalyst in creating a renewed interest in Artificial Intelligence (AI) approaches, especially the subclass of computer algorithms that are popularly grouped into Machine Learning (ML). These methods have allowed one to go beyond limited human cognitive ability into understanding the complexity in the high dimensional data. Medical sciences have seen a steady use of these methods but have been slow in adoption to improve patient care. There are some significant impediments that have diluted this effort, which include availability of curated diverse data sets for model building, reliable human-level interpretation of these models, and reliable reproducibility of these methods for routine clinical use. Each of these aspects has several limiting conditions that need to be balanced out, considering the data/model building efforts, clinical implementation, integration cost to translational effort with minimal patient level harm, which may directly impact future clinical adoption. In this review paper, we will assess each aspect of the problem in the context of reliable use of the ML methods in oncology, as a representative study case, with the goal to safeguard utility and improve patient care in medicine in general.
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Affiliation(s)
| | - Ross Mitchell
- Department of Machine Learning, H. Lee. Moffitt Cancer Center, Tampa, FL, USA; Health Data Services, H. Lee. Moffitt Cancer Center, Tampa, FL, USA.
| | - Issam El Naqa
- Department of Machine Learning, H. Lee. Moffitt Cancer Center, Tampa, FL, USA.
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Czerska K, Emert F, Kopec R, Langen K, McClelland JR, Meijers A, Miyamoto N, Riboldi M, Shimizu S, Terunuma T, Zou W, Knopf A, Rucinski A. Clinical practice vs. state-of-the-art research and future visions: Report on the 4D treatment planning workshop for particle therapy - Edition 2018 and 2019. Phys Med 2021; 82:54-63. [PMID: 33588228 DOI: 10.1016/j.ejmp.2020.12.013] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 12/09/2020] [Accepted: 12/16/2020] [Indexed: 12/18/2022] Open
Abstract
The 4D Treatment Planning Workshop for Particle Therapy, a workshop dedicated to the treatment of moving targets with scanned particle beams, started in 2009 and since then has been organized annually. The mission of the workshop is to create an informal ground for clinical medical physicists, medical physics researchers and medical doctors interested in the development of the 4D technology, protocols and their translation into clinical practice. The 10th and 11th editions of the workshop took place in Sapporo, Japan in 2018 and Krakow, Poland in 2019, respectively. This review report from the Sapporo and Krakow workshops is structured in two parts, according to the workshop programs. The first part comprises clinicians and physicists review of the status of 4D clinical implementations. Corresponding talks were given by speakers from five centers around the world: Maastro Clinic (The Netherlands), University Medical Center Groningen (The Netherlands), MD Anderson Cancer Center (United States), University of Pennsylvania (United States) and The Proton Beam Therapy Center of Hokkaido University Hospital (Japan). The second part is dedicated to novelties in 4D research, i.e. motion modelling, artificial intelligence and new technologies which are currently being investigated in the radiotherapy field.
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Affiliation(s)
- Katarzyna Czerska
- Institute of Nuclear Physics Polish Academy of Sciences, PL-31342 Krakow, Poland.
| | - Frank Emert
- Center for Proton Therapy, Paul Scherrer Institute, Switzerland
| | - Renata Kopec
- Institute of Nuclear Physics Polish Academy of Sciences, PL-31342 Krakow, Poland
| | - Katja Langen
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Jamie R McClelland
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Arturs Meijers
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Naoki Miyamoto
- Department of Medical Physics, Hokkaido University Hospital, Sapporo, Hokkaido, Japan; Faculty of Engineering, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Marco Riboldi
- Department of Medical Physics, Ludwig-Maximilians-Universität München, Germany
| | - Shinichi Shimizu
- Department of Medical Physics, Hokkaido University Hospital, Sapporo, Hokkaido, Japan; Department of Radiation Medical Science and Engineering, Faculty of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Toshiyuki Terunuma
- Faculty of Medicine, University of Tsukuba, Japan; Proton Medical Research Center, University of Tsukuba Hospital, Japan
| | - Wei Zou
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA
| | - Antje Knopf
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Antoni Rucinski
- Institute of Nuclear Physics Polish Academy of Sciences, PL-31342 Krakow, Poland
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El Naqa I, Li H, Fuhrman J, Hu Q, Gorre N, Chen W, Giger ML. Lessons learned in transitioning to AI in the medical imaging of COVID-19. J Med Imaging (Bellingham) 2021; 8:010902-10902. [PMID: 34646912 PMCID: PMC8488974 DOI: 10.1117/1.jmi.8.s1.010902] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 09/20/2021] [Indexed: 12/12/2022] Open
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has wreaked havoc across the world. It also created a need for the urgent development of efficacious predictive diagnostics, specifically, artificial intelligence (AI) methods applied to medical imaging. This has led to the convergence of experts from multiple disciplines to solve this global pandemic including clinicians, medical physicists, imaging scientists, computer scientists, and informatics experts to bring to bear the best of these fields for solving the challenges of the COVID-19 pandemic. However, such a convergence over a very brief period of time has had unintended consequences and created its own challenges. As part of Medical Imaging Data and Resource Center initiative, we discuss the lessons learned from career transitions across the three involved disciplines (radiology, medical imaging physics, and computer science) and draw recommendations based on these experiences by analyzing the challenges associated with each of the three associated transition types: (1) AI of non-imaging data to AI of medical imaging data, (2) medical imaging clinician to AI of medical imaging, and (3) AI of medical imaging to AI of COVID-19 imaging. The lessons learned from these career transitions and the diffusion of knowledge among them could be accomplished more effectively by recognizing their associated intricacies. These lessons learned in the transitioning to AI in the medical imaging of COVID-19 can inform and enhance future AI applications, making the whole of the transitions more than the sum of each discipline, for confronting an emergency like the COVID-19 pandemic or solving emerging problems in biomedicine.
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Affiliation(s)
- Issam El Naqa
- Moffitt Cancer Center, Department of Machine Learning, Tampa, Florida, United States
- The University of Chicago, Medical Imaging Data and Resource Center, Chicago, Illinois, United States
| | - Hui Li
- The University of Chicago, Medical Imaging Data and Resource Center, Chicago, Illinois, United States
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Jordan Fuhrman
- The University of Chicago, Medical Imaging Data and Resource Center, Chicago, Illinois, United States
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Qiyuan Hu
- The University of Chicago, Medical Imaging Data and Resource Center, Chicago, Illinois, United States
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Naveena Gorre
- Moffitt Cancer Center, Department of Machine Learning, Tampa, Florida, United States
- The University of Chicago, Medical Imaging Data and Resource Center, Chicago, Illinois, United States
| | - Weijie Chen
- The University of Chicago, Medical Imaging Data and Resource Center, Chicago, Illinois, United States
- US FDA, CDRH, Office of Science and Engineering Laboratories, Division of Imaging, Diagnosis, and Software Reliability, Silver Spring, Maryland, United States
| | - Maryellen L. Giger
- The University of Chicago, Medical Imaging Data and Resource Center, Chicago, Illinois, United States
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
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Jackson SA, Prise KM. 125 years of BJR and radiological research: reflecting on the anniversary series in celebration of the world's oldest radiology journal. Br J Radiol 2021; 94:bjr20219001. [PMID: 33305997 DOI: 10.1259/bjr.20219001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Affiliation(s)
- Simon A Jackson
- Peninsula Radiology Academy, University Hospitals Plymouth NHS Trust, Plymouth, UK
| | - Kevin M Prise
- Patrick G, Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
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Olthof AW, van Ooijen PMA, Rezazade Mehrizi MH. Promises of artificial intelligence in neuroradiology: a systematic technographic review. Neuroradiology 2020; 62:1265-1278. [PMID: 32318774 PMCID: PMC7479016 DOI: 10.1007/s00234-020-02424-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 03/27/2020] [Indexed: 12/15/2022]
Abstract
PURPOSE To conduct a systematic review of the possibilities of artificial intelligence (AI) in neuroradiology by performing an objective, systematic assessment of available applications. To analyse the potential impacts of AI applications on the work of neuroradiologists. METHODS We identified AI applications offered on the market during the period 2017-2019. We systematically collected and structured information in a relational database and coded for the characteristics of the applications, their functionalities for the radiology workflow and their potential impacts in terms of 'supporting', 'extending' and 'replacing' radiology tasks. RESULTS We identified 37 AI applications in the domain of neuroradiology from 27 vendors, together offering 111 functionalities. The majority of functionalities 'support' radiologists, especially for the detection and interpretation of image findings. The second-largest group of functionalities 'extends' the possibilities of radiologists by providing quantitative information about pathological findings. A small but noticeable portion of functionalities seek to 'replace' certain radiology tasks. CONCLUSION Artificial intelligence in neuroradiology is not only in the stage of development and testing but also available for clinical practice. The majority of functionalities support radiologists or extend their tasks. None of the applications can replace the entire radiology profession, but a few applications can do so for a limited set of tasks. Scientific validation of the AI products is more limited than the regulatory approval.
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Affiliation(s)
- Allard W Olthof
- Department of Radiology, Treant Health Care Group, Dr. G.H. Amshoffweg 1, Hoogeveen, The Netherlands.
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, Groningen, The Netherlands.
| | - Peter M A van Ooijen
- Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, Groningen, The Netherlands
- Data Science Center in Health (DASH), Machine Learning Lab, University of Groningen, University Medical Center Groningen, Zielstraweg 2, Groningen, The Netherlands
| | - Mohammad H Rezazade Mehrizi
- School of Business and Economics, Knowledge, Information and Innovation, KIN Center for Digital Innovation, Vrije Universiteit Amsterdam, De Boelelaan 1105, Amsterdam, The Netherlands
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Iwahashi S, Ghaibeh AA, Shimada M, Morine Y, Imura S, Ikemoto T, Saito Y, Hirose J. Predictability of postoperative recurrence on hepatocellular carcinoma through data mining method. Mol Clin Oncol 2020; 13:46. [PMID: 32874576 DOI: 10.3892/mco.2020.2116] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Accepted: 04/27/2020] [Indexed: 12/18/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is a highly lethal tumor and the majority of postoperative patients experience recurrence. In the present study, we focus on the predictability of postoperative recurrence on HCC through the data mining method. In total, 323 patients with HCC who underwent hepatic resection were included in the present study, 156 of whom suffered from cancer recurrence. Clinicopathological data including prognosis were analyzed using the data mining method for the predictability of postoperative recurrence on HCC. The resulting alternating decision tree (ADT) was described using data mining method. This tree was validated using a 10-fold cross validation process. The average and standard deviation of the accuracy, sensitivity, and specificity were 69.0±8.2, 59.7±14.5 and 77.7±10.2%, respectively. The identified postoperative recurrence factors were age, viral hepatitis, stage, GOT and T-cholesterol. Data mining method could identify the factors associated at different levels of significance with postoperative recurrence of HCC. These factors could help to predict the postoperative recurrence of HCC.
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Affiliation(s)
- Shuichi Iwahashi
- Department of Surgery, Institute of Health Biosciences, The University of Tokushima, Kuramoto-cho, Tokushima 770-8503, Japan
| | - A Ammar Ghaibeh
- Department of Medical Informatics, Institute of Health Biosciences, The University of Tokushima, Kuramoto-cho, Tokushima 770-8503, Japan
| | - Mitsuo Shimada
- Department of Surgery, Institute of Health Biosciences, The University of Tokushima, Kuramoto-cho, Tokushima 770-8503, Japan
| | - Yuji Morine
- Department of Surgery, Institute of Health Biosciences, The University of Tokushima, Kuramoto-cho, Tokushima 770-8503, Japan
| | - Satoru Imura
- Department of Surgery, Institute of Health Biosciences, The University of Tokushima, Kuramoto-cho, Tokushima 770-8503, Japan
| | - Tetsuya Ikemoto
- Department of Surgery, Institute of Health Biosciences, The University of Tokushima, Kuramoto-cho, Tokushima 770-8503, Japan
| | - Yu Saito
- Department of Surgery, Institute of Health Biosciences, The University of Tokushima, Kuramoto-cho, Tokushima 770-8503, Japan
| | - Jun Hirose
- Department of Medical Informatics, Institute of Health Biosciences, The University of Tokushima, Kuramoto-cho, Tokushima 770-8503, Japan
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Lonsdale H, Jalali A, Gálvez JA, Ahumada LM, Simpao AF. Artificial Intelligence in Anesthesiology: Hype, Hope, and Hurdles. Anesth Analg 2020; 130:1111-1113. [PMID: 32287116 DOI: 10.1213/ane.0000000000004751] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
| | - Ali Jalali
- From the Department of Anesthesiology.,Department of Health Informatics, Predictive Analytics Core, Johns Hopkins All Children's Hospital, St Petersburg, Florida.,Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Jorge A Gálvez
- Department of Anesthesiology and Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania.,Departments of Anesthesiology and Critical Care Medicine.,Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Luis M Ahumada
- From the Department of Anesthesiology.,Department of Health Informatics, Predictive Analytics Core, Johns Hopkins All Children's Hospital, St Petersburg, Florida.,Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Allan F Simpao
- Department of Anesthesiology and Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania.,Departments of Anesthesiology and Critical Care Medicine.,Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
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El Naqa I, Das S. The role of machine and deep learning in modern medical physics. Med Phys 2020; 47:e125-e126. [DOI: 10.1002/mp.14088] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 02/06/2020] [Accepted: 02/07/2020] [Indexed: 12/15/2022] Open
Affiliation(s)
- Issam El Naqa
- Department of Radiation Oncology University of Michigan Ann Arbor MI 48103USA
| | - Shiva Das
- Department of Radiation Oncology University of North Carolina Chapel Hill NC 277103USA
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Artificial Intelligence in radiotherapy: state of the art and future directions. Med Oncol 2020; 37:50. [PMID: 32323066 DOI: 10.1007/s12032-020-01374-w] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Accepted: 04/13/2020] [Indexed: 02/06/2023]
Abstract
Recent advances in computing capability allowed the development of sophisticated predictive models to assess complex relationships within observational data, described as Artificial Intelligence. Medicine is one of the several fields of application and Radiation oncology could benefit from these approaches, particularly in patients' medical records, imaging, baseline pathology, planning or instrumental data. Artificial Intelligence systems could simplify many steps of the complex workflow of radiotherapy such as segmentation, planning or delivery. However, Artificial Intelligence could be considered as a "black box" in which human operator may only understand input and output predictions and its application to the clinical practice remains a challenge. The low transparency of the overall system is questionable from manifold points of view (ethical included). Given the complexity of this issue, we collected the basic definitions to help the clinician to understand current literature, and overviewed experiences regarding implementation of AI within radiotherapy clinical workflow, aiming to describe this field from the clinician perspective.
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