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Larson DB. A Vision for Global CT Radiation Dose Optimization. J Am Coll Radiol 2024; 21:1311-1317. [PMID: 38302037 DOI: 10.1016/j.jacr.2024.01.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 01/11/2024] [Accepted: 01/17/2024] [Indexed: 02/03/2024]
Abstract
The topic of CT radiation dose management is receiving renewed attention since the recent approval by CMS for new CT dose measures. Widespread variation in CT dose persists in practices across the world, suggesting that current dose optimization techniques are lacking. The author outlines a proposed strategy for facilitating global CT radiation dose optimization. CT radiation dose optimization can be defined as the routine use of CT scan parameters that consistently produce images just above the minimum threshold of acceptable image quality for a given clinical indication, accounting for relevant patient characteristics, using the most dose-efficient techniques available on the scanner. To accomplish this, an image quality-based target dose must be established for every protocol; for nonhead CT applications, these target dose values must be expressed as a function of patient size. As variation in outcomes is reduced, the dose targets can be decreased to more closely approximate the minimum image quality threshold. Maintaining CT radiation dose optimization requires a process control program, including measurement, evaluation, feedback, and control. This is best accomplished by local teams made up of radiologists, medical physicists, and technologists, supported with protected time and needed tools, including analytics and protocol management applications. Other stakeholders critical to facilitating CT radiation dose management include researchers, funding agencies, industry, regulators, accreditors, payers, and the ACR. Analogous coordinated approaches have transformed quality in other industries and can be the mechanism for achieving the universal goal of CT radiation dose optimization.
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Affiliation(s)
- David B Larson
- Executive Vice Chair, Department of Radiology, Stanford University School of Medicine, Stanford, California; and Chair, ACR Commission on Quality and Safety.
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2
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Wang Q, Han X, Song L, Zhang X, Zhang B, Gu Z, Jiang B, Li C, Li X, Yu Y. Automatic quality assessment of knee radiographs using knowledge graphs and convolutional neural networks. Med Phys 2024. [PMID: 39016559 DOI: 10.1002/mp.17316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 07/05/2024] [Accepted: 07/05/2024] [Indexed: 07/18/2024] Open
Abstract
BACKGROUND X-ray radiography is a widely used imaging technique worldwide, and its image quality directly affects diagnostic accuracy. Therefore, X-ray image quality control (QC) is essential. However, subjectively assessing image quality is inefficient and inconsistent, especially when large amounts of image data are being evaluated. Thus, subjective assessment cannot meet current QC needs. PURPOSE To meet current QC needs and improve the efficiency of image quality assessment, a complete set of quality assessment criteria must be established and implemented using artificial intelligence (AI) technology. Therefore, we proposed a multi-criteria AI system for automatically assessing the image quality of knee radiographs. METHODS A knee radiograph QC knowledge graph containing 16 "acquisition technique" labels representing 16 image quality defects and five "clarity" labels representing five grades of clarity were developed. Ten radiographic technologists conducted three rounds of QC based on this graph. The single-person QC results were denoted as QC1 and QC2, and the multi-person QC results were denoted as QC3. Each technologist labeled each image only once. The ResNet model structure was then used to simultaneously perform classification (detection of image quality defects) and regression (output of a clarity score) tasks to construct an image QC system. The QC3 results, comprising 4324 anteroposterior and lateral knee radiographs, were used for model training (70% of the images), validation (10%), and testing (20%). The 865 test set data were used to evaluate the effectiveness of the AI model, and an AI QC result, QC4, was automatically generated by the model after training. Finally, using a double-blind method, the senior QC expert reviewed the final QC results of the test set with reference to the results QC3 and QC4 and used them as a reference standard to evaluate the performance of the model. The precision and mean absolute error (MAE) were used to evaluate the quality of all the labels in relation to the reference standard. RESULTS For the 16 "acquisition technique" features, QC4 exhibited the highest weighted average precision (98.42% ± 0.81%), followed by QC3 (91.39% ± 1.35%), QC2 (87.84% ± 1.68%), and QC1 (87.35% ± 1.71%). For the image clarity features, the MAEs between QC1, QC2, QC3, and QC4 and the reference standard were 0.508 ± 0.021, 0.475 ± 0.019, 0.237 ± 0.016, and 0.303 ± 0.018, respectively. CONCLUSIONS The experimental results show that our automated quality assessment system performed well in classifying the acquisition technique used for knee radiographs. The image clarity quality evaluation accuracy of the model must be further improved but is generally close to that of radiographic technologists. Intelligent QC methods using knowledge graphs and convolutional neural networks have the potential for clinical applications.
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Affiliation(s)
- Qian Wang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xiao Han
- College of Medical Information Engineering, Anhui University of Chinese Medicine, Hefei, China
| | - Liangliang Song
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xin Zhang
- College of Computer Science and Technology, Anhui University, Hefei, China
| | - Biao Zhang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Artificial Intelligence Research Institute, Hefei Comprehensive National Science Center, Hefei, China
| | - Zongyun Gu
- College of Medical Information Engineering, Anhui University of Chinese Medicine, Hefei, China
- Artificial Intelligence Research Institute, Hefei Comprehensive National Science Center, Hefei, China
| | - Bo Jiang
- College of Computer Science and Technology, Anhui University, Hefei, China
| | - Chuanfu Li
- College of Medical Information Engineering, Anhui University of Chinese Medicine, Hefei, China
- Artificial Intelligence Research Institute, Hefei Comprehensive National Science Center, Hefei, China
- Anhui Provincial Imaging Diagnosis Quality Control Center, Anhui Provincial Health Commission, Hefei, China
| | - Xiaohu Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Anhui Provincial Imaging Diagnosis Quality Control Center, Anhui Provincial Health Commission, Hefei, China
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Anhui Provincial Imaging Diagnosis Quality Control Center, Anhui Provincial Health Commission, Hefei, China
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3
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Kim Y, Lee S, Jo G, Kwon A, Kang J, Kim J, Huh K, Yi W, Heo M, Choi S. Comparative analysis of clinical image evaluation charts for panoramic radiography. Oral Radiol 2024:10.1007/s11282-024-00765-3. [PMID: 38977537 DOI: 10.1007/s11282-024-00765-3] [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: 03/27/2024] [Accepted: 06/30/2024] [Indexed: 07/10/2024]
Abstract
OBJECTIVE To compare and analyze professional (P chart) and simple (S chart) clinical image evaluation charts for evaluating panoramic radiograph image quality. METHODS Ten evaluators assessed 285 clinical panoramic radiograph images. The evaluators were divided into oral and maxillofacial radiologists (OMFR, n = 5) and general dentist (dentists not specializing in oral and maxillofacial radiology, G, n = 5) groups. For image evaluation, P and S charts provided by the Korean Academy of Oral and Maxillofacial Radiology were used. Scores of items for each evaluation chart were used to compare the reliability, correlation, evaluation scores, evaluation time, and preference, and statistical analyses were performed using IBM SPSS Statistics. RESULTS The S chart showed similar levels of evaluation scores at shorter evaluation time, as compared to the P chart. In the results for each evaluation chart, all analyzed correlations were statistically significant. Total score, image density/contrast/sharpness, and overall image quality items showed a very high positive correlation in the P chart. While the overall range of correlation coefficients was relatively lower in the S chart than the P chart, the same items showed high correlation coefficients. In the preference evaluation, both the professional and generalist groups preferred the S chart. CONCLUSIONS A comparative analysis with the P chart, revisions, and upgrades are needed for the S chart items that showed low correlations in this study, such as artifacts, coverage area, and patient movement.
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Affiliation(s)
- Yeonhee Kim
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea
| | - Samsun Lee
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea.
| | - Gyudong Jo
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea
| | - Ahyoung Kwon
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea
| | - Juhee Kang
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea
| | - Joeun Kim
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea
| | - Kyunghoe Huh
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea
| | - Wonjin Yi
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea
| | - Minsuk Heo
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea
| | - Soonchul Choi
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea
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Mese I, Taslicay CA, Sivrioglu AK. Improving radiology workflow using ChatGPT and artificial intelligence. Clin Imaging 2023; 103:109993. [PMID: 37812965 DOI: 10.1016/j.clinimag.2023.109993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 08/19/2023] [Accepted: 09/28/2023] [Indexed: 10/11/2023]
Abstract
Artificial Intelligence is a branch of computer science that aims to create intelligent machines capable of performing tasks that typically require human intelligence. One of the branches of artificial intelligence is natural language processing, which is dedicated to studying the interaction between computers and human language. ChatGPT is a sophisticated natural language processing tool that can understand and respond to complex questions and commands in natural language. Radiology is a vital aspect of modern medicine that involves the use of imaging technologies to diagnose and treat medical conditions artificial intelligence, including ChatGPT, can be integrated into radiology workflows to improve efficiency, accuracy, and patient care. ChatGPT can streamline various radiology workflow steps, including patient registration, scheduling, patient check-in, image acquisition, interpretation, and reporting. While ChatGPT has the potential to transform radiology workflows, there are limitations to the technology that must be addressed, such as the potential for bias in artificial intelligence algorithms and ethical concerns. As technology continues to advance, ChatGPT is likely to become an increasingly important tool in the field of radiology, and in healthcare more broadly.
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Affiliation(s)
- Ismail Mese
- Department of Radiology, Health Sciences University, Erenkoy Mental Health and Neurology Training and Research Hospital, 19 Mayıs, Sinan Ercan Cd. No: 23, Kadıköy/Istanbul 34736, Turkey.
| | | | - Ali Kemal Sivrioglu
- Department of Radiology, Liv Hospital Vadistanbul, Ayazağa Mahallesi, Kemerburgaz Caddesi, Vadistanbul Park Etabı, 7F Blok, 34396 Sarıyer/İstanbul, Turkey
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5
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Kress GT, Popa ES, Thompson PM, Bookheimer SY, Thomopoulos SI, Ching CRK, Zheng H, Hirsh DA, Merrill DA, Panos SE, Raji CA, Siddarth P, Bramen JE. Preliminary validation of a structural magnetic resonance imaging metric for tracking dementia-related neurodegeneration and future decline. Neuroimage Clin 2023; 39:103458. [PMID: 37421927 PMCID: PMC10338152 DOI: 10.1016/j.nicl.2023.103458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 06/20/2023] [Indexed: 07/10/2023]
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by cognitive decline and atrophy in the medial temporal lobe (MTL) and subsequent brain regions. Structural magnetic resonance imaging (sMRI) has been widely used in research and clinical care for diagnosis and monitoring AD progression. However, atrophy patterns are complex and vary by patient. To address this issue, researchers have made efforts to develop more concise metrics that can summarize AD-specific atrophy. Many of these methods can be difficult to interpret clinically, hampering adoption. In this study, we introduce a novel index which we call an "AD-NeuroScore," that uses a modified Euclidean-inspired distance function to calculate differences between regional brain volumes associated with cognitive decline. The index is adjusted for intracranial volume (ICV), age, sex, and scanner model. We validated AD-NeuroScore using 929 older adults from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, with a mean age of 72.7 years (SD = 6.3; 55.1-91.5) and cognitively normal (CN), mild cognitive impairment (MCI), or AD diagnoses. Our validation results showed that AD-NeuroScore was significantly associated with diagnosis and disease severity scores (measured by MMSE, CDR-SB, and ADAS-11) at baseline. Furthermore, baseline AD-NeuroScore was associated with both changes in diagnosis and disease severity scores at all time points with available data. The performance of AD-NeuroScore was equivalent or superior to adjusted hippocampal volume (AHV), a widely used metric in AD research. Further, AD-NeuroScore typically performed as well as or sometimes better when compared to other existing sMRI-based metrics. In conclusion, we have introduced a new metric, AD-NeuroScore, which shows promising results in detecting AD, benchmarking disease severity, and predicting disease progression. AD-NeuroScore differentiates itself from other metrics by being clinically practical and interpretable.
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Affiliation(s)
- Gavin T Kress
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA; Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Emily S Popa
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA 90292, USA
| | - Susan Y Bookheimer
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA; David Geffen School of Medicine, University of California, Los Angeles, Westwood, CA 90095, USA
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA 90292, USA
| | - Christopher R K Ching
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA 90292, USA
| | - Hong Zheng
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA 90292, USA
| | - Daniel A Hirsh
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA.
| | - David A Merrill
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA; Department of Translational Neurosciences and Neurotherapeutics, Providence Saint John's Cancer Institute, Santa Monica, CA 90404, USA; UCLA Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Westwood, CA 90095, USA
| | - Stella E Panos
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA
| | - Cyrus A Raji
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO, USA
| | - Prabha Siddarth
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA; UCLA Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Westwood, CA 90095, USA
| | - Jennifer E Bramen
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA.
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6
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Gupta RV, Kalra MK, Ebrahimian S, Kaviani P, Primak A, Bizzo B, Dreyer KJ. Complex Relationship Between Artificial Intelligence and CT Radiation Dose. Acad Radiol 2022; 29:1709-1719. [PMID: 34836775 DOI: 10.1016/j.acra.2021.10.024] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 10/15/2021] [Accepted: 10/17/2021] [Indexed: 12/22/2022]
Abstract
Concerns over need for CT radiation dose optimization and reduction led to improved scanner efficiency and introduction of several reconstruction techniques and image processing-based software. The latest technologies use artificial intelligence (AI) for CT dose optimization and image quality improvement. While CT dose optimization has and can benefit from AI, variations in scanner technologies, reconstruction methods, and scan protocols can lead to substantial variations in radiation doses and image quality across and within different scanners. These variations in turn can influence performance of AI algorithms being deployed for tasks such as detection, segmentation, characterization, and quantification. We review the complex relationship between AI and CT radiation dose.
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Affiliation(s)
- Reya V Gupta
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Court, Suite 248, Boston, Massachusetts
| | - Mannudeep K Kalra
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Court, Suite 248, Boston, Massachusetts.
| | - Shadi Ebrahimian
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Court, Suite 248, Boston, Massachusetts
| | - Parisa Kaviani
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Court, Suite 248, Boston, Massachusetts
| | - Andrew Primak
- Siemens Medical Solutions USA Inc, Malvern, Pennsylvania
| | - Bernardo Bizzo
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Court, Suite 248, Boston, Massachusetts; MGH & BWH Center for Clinical Data Science, Boston, Massachusetts
| | - Keith J Dreyer
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Court, Suite 248, Boston, Massachusetts; MGH & BWH Center for Clinical Data Science, Boston, Massachusetts
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7
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Xu Z, Xiang D, He J. Data Privacy Protection in News Crowdfunding in the Era of Artificial Intelligence. JOURNAL OF GLOBAL INFORMATION MANAGEMENT 2022. [DOI: 10.4018/jgim.286760] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
This paper aims to study the protection of data privacy in news crowdfunding in the era of artificial intelligence. This paper respectively quotes the encryption algorithm of artificial intelligence data protection and the BP neural network prediction model to analyze the data privacy protection in news crowdfunding in the artificial intelligence era. Finally, this paper also combines the questionnaire survey method to understand the public’s awareness of privacy. The results of this paper show that artificial intelligence can promote personal data awareness and privacy, improve personal data and privacy measures and methods, and improve the effectiveness and level of privacy and privacy. In the analysis, the survey found that male college students only have 81.1% of the cognition of personal trait information, only 78.5% of network trace information, and only 78.3% of female college students’ cognition of personal credit.
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Affiliation(s)
- Zhiqiang Xu
- School of Film and Animation, China-ASEAN Art College of Chengdu University & School of Digital Media and Creative Design, Sichuan College of the Communication, China & The Education University of Hong Kong, China
| | - Dong Xiang
- School of Digital Media and Creative Design, Sichuan College of Communication, China
| | - Jialiang He
- School of Information and Communication Engineering, Dalian Nationalities University, China
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8
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Goergen SK, Frazer HM, Reddy S. Quality use of artificial intelligence in medical imaging: What do radiologists need to know? J Med Imaging Radiat Oncol 2022; 66:225-232. [PMID: 35243782 DOI: 10.1111/1754-9485.13379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 12/14/2021] [Indexed: 11/27/2022]
Abstract
The application of artificial intelligence, and in particular machine learning, to the practice of radiology, is already impacting the quality of imaging care. It will increasingly do so in the future. Radiologists need to be aware of factors that govern the quality of these tools at the development, regulatory and clinical implementation stages in order to make judicious decisions about their use in daily practice.
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Affiliation(s)
- Stacy K Goergen
- Monash Imaging, Monash Health, Melbourne, Victoria, Australia.,Department of Imaging, School of Clinical Sciences, Monash University, Melbourne, Victoria, Australia
| | - Helen Ml Frazer
- St Vincent's BreastScreen, St Vincent's Hospital Melbourne, Melbourne, Victoria, Australia.,BreastScreen Victoria, Melbourne, Victoria, Australia
| | - Sandeep Reddy
- School of Medicine, Deakin University, Geelong, Victoria, Australia
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9
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The R-AI-DIOLOGY checklist: a practical checklist for evaluation of artificial intelligence tools in clinical neuroradiology. Neuroradiology 2022; 64:851-864. [DOI: 10.1007/s00234-021-02890-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 12/21/2021] [Indexed: 11/24/2022]
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10
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Bento M, Fantini I, Park J, Rittner L, Frayne R. Deep Learning in Large and Multi-Site Structural Brain MR Imaging Datasets. Front Neuroinform 2022; 15:805669. [PMID: 35126080 PMCID: PMC8811356 DOI: 10.3389/fninf.2021.805669] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Accepted: 12/27/2021] [Indexed: 12/22/2022] Open
Abstract
Large, multi-site, heterogeneous brain imaging datasets are increasingly required for the training, validation, and testing of advanced deep learning (DL)-based automated tools, including structural magnetic resonance (MR) image-based diagnostic and treatment monitoring approaches. When assembling a number of smaller datasets to form a larger dataset, understanding the underlying variability between different acquisition and processing protocols across the aggregated dataset (termed “batch effects”) is critical. The presence of variation in the training dataset is important as it more closely reflects the true underlying data distribution and, thus, may enhance the overall generalizability of the tool. However, the impact of batch effects must be carefully evaluated in order to avoid undesirable effects that, for example, may reduce performance measures. Batch effects can result from many sources, including differences in acquisition equipment, imaging technique and parameters, as well as applied processing methodologies. Their impact, both beneficial and adversarial, must be considered when developing tools to ensure that their outputs are related to the proposed clinical or research question (i.e., actual disease-related or pathological changes) and are not simply due to the peculiarities of underlying batch effects in the aggregated dataset. We reviewed applications of DL in structural brain MR imaging that aggregated images from neuroimaging datasets, typically acquired at multiple sites. We examined datasets containing both healthy control participants and patients that were acquired using varying acquisition protocols. First, we discussed issues around Data Access and enumerated the key characteristics of some commonly used publicly available brain datasets. Then we reviewed methods for correcting batch effects by exploring the two main classes of approaches: Data Harmonization that uses data standardization, quality control protocols or other similar algorithms and procedures to explicitly understand and minimize unwanted batch effects; and Domain Adaptation that develops DL tools that implicitly handle the batch effects by using approaches to achieve reliable and robust results. In this narrative review, we highlighted the advantages and disadvantages of both classes of DL approaches, and described key challenges to be addressed in future studies.
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Affiliation(s)
- Mariana Bento
- Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Calgary Image Processing and Analysis Centre, Foothills Medical Centre, Calgary, AB, Canada
- *Correspondence: Mariana Bento
| | - Irene Fantini
- School of Electrical and Computer Engineering, University of Campinas, Campinas, Brazil
| | - Justin Park
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Calgary Image Processing and Analysis Centre, Foothills Medical Centre, Calgary, AB, Canada
- Radiology and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Leticia Rittner
- School of Electrical and Computer Engineering, University of Campinas, Campinas, Brazil
| | - Richard Frayne
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Calgary Image Processing and Analysis Centre, Foothills Medical Centre, Calgary, AB, Canada
- Radiology and Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, AB, Canada
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11
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Sandino CM, Cole EK, Alkan C, Chaudhari AS, Loening AM, Hyun D, Dahl J, Imran AAZ, Wang AS, Vasanawala SS. Upstream Machine Learning in Radiology. Radiol Clin North Am 2021; 59:967-985. [PMID: 34689881 PMCID: PMC8549864 DOI: 10.1016/j.rcl.2021.07.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Machine learning (ML) and Artificial intelligence (AI) has the potential to dramatically improve radiology practice at multiple stages of the imaging pipeline. Most of the attention has been garnered by applications focused on improving the end of the pipeline: image interpretation. However, this article reviews how AI/ML can be applied to improve upstream components of the imaging pipeline, including exam modality selection, hardware design, exam protocol selection, data acquisition, image reconstruction, and image processing. A breadth of applications and their potential for impact is shown across multiple imaging modalities, including ultrasound, computed tomography, and MRI.
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Affiliation(s)
- Christopher M Sandino
- Department of Electrical Engineering, Stanford University, 350 Serra Mall, Stanford, CA 94305, USA
| | - Elizabeth K Cole
- Department of Electrical Engineering, Stanford University, 350 Serra Mall, Stanford, CA 94305, USA
| | - Cagan Alkan
- Department of Electrical Engineering, Stanford University, 350 Serra Mall, Stanford, CA 94305, USA
| | - Akshay S Chaudhari
- Department of Biomedical Data Science, 1201 Welch Road, Stanford, CA 94305, USA; Department of Radiology, Stanford University, 1201 Welch Road, Stanford, CA 94305, USA
| | - Andreas M Loening
- Department of Radiology, Stanford University, 1201 Welch Road, Stanford, CA 94305, USA
| | - Dongwoon Hyun
- Department of Radiology, Stanford University, 1201 Welch Road, Stanford, CA 94305, USA
| | - Jeremy Dahl
- Department of Radiology, Stanford University, 1201 Welch Road, Stanford, CA 94305, USA
| | | | - Adam S Wang
- Department of Radiology, Stanford University, 1201 Welch Road, Stanford, CA 94305, USA
| | - Shreyas S Vasanawala
- Department of Radiology, Stanford University, 1201 Welch Road, Stanford, CA 94305, USA.
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12
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La integración de la inteligencia artificial en el abordaje clínico del paciente: enfoque en la imagen cardiaca. Rev Esp Cardiol 2021. [DOI: 10.1016/j.recesp.2020.07.012] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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13
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LewandrowskI KU, Muraleedharan N, Eddy SA, Sobti V, Reece BD, Ramírez León JF, Shah S. Feasibility of Deep Learning Algorithms for Reporting in Routine Spine Magnetic Resonance Imaging. Int J Spine Surg 2020; 14:S86-S97. [PMID: 33298549 DOI: 10.14444/7131] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Artificial intelligence is gaining traction in automated medical imaging analysis. Development of more accurate magnetic resonance imaging (MRI) predictors of successful clinical outcomes is necessary to better define indications for surgery, improve clinical outcomes with targeted minimally invasive and endoscopic procedures, and realize cost savings by avoiding more invasive spine care. OBJECTIVE To demonstrate the ability for deep learning neural network models to identify features in MRI DICOM datasets that represent varying intensities or severities of common spinal pathologies and injuries and to demonstrate the feasibility of generating automated verbal MRI reports comparable to those produced by reading radiologists. METHODS A 3-dimensional (3D) anatomical model of the lumbar spine was fitted to each of the patient's MRIs by a team of technicians. MRI T1, T2, sagittal, axial, and transverse reconstruction image series were used to train segmentation models by the intersection of the 3D model through these image sequences. Class definitions were extracted from the radiologist report for the central canal: (0) no disc bulge/protrusion/canal stenosis, (1) disc bulge without canal stenosis, (2) disc bulge resulting in canal stenosis, and (3) disc herniation/protrusion/extrusion resulting in canal stenosis. Both the left and right neural foramina were assessed with either (0) neural foraminal stenosis absent, or (1) neural foramina stenosis present. Reporting criteria for the pathologies at each disc level and, when available, the grading of severity were extracted, and a natural language processing model was used to generate a verbal and written report. These data were then used to train a set of very deep convolutional neural network models, optimizing for minimal binary cross-entropy for each classification. RESULTS The initial prediction validation of the implemented deep learning algorithm was done on 20% of the dataset, which was not used for artificial intelligence training. Of the 17,800 total disc locations for which MRI images and radiology reports were available, 14,720 were used to train the model, and 3560 were used to validate against. The convergence of validation accuracy achieved with the deep learning algorithm for the foraminal stenosis detector was 81% (sensitivity = 72.4.4%, specificity = 83.1%) after 25 complete iterations through the entire training dataset (epoch). The accuracy was 86.2% (sensitivity = 91.1%, specificity = 82.5%) for the central stenosis detector and 85.2% (sensitivity = 81.8%, specificity = 87.4%) for the disc herniation detector. CONCLUSIONS Deep learning algorithms may be used for routine reporting in spine MRI. There was a minimal disparity among accuracy, sensitivity, and specificity, indicating that the data were not overfitted to the training set. We concluded that variability in the training data tends to reduce overfitting and overtraining as the deep neural network models learn to focus on the common pathologies. Future studies should demonstrate the accuracy of deep neural network models and the predictive value of favorable clinical outcomes with intervention and surgery. LEVEL OF EVIDENCE 3. CLINICAL RELEVANCE Feasibility, clinical teaching, and evaluation study.
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Affiliation(s)
- Kai-Uwe LewandrowskI
- Staff Orthopaedic Spine Surgeon Center for Advanced Spine Care of Southern Arizona and Surgical Institute of Tucson, Tucson, Arizona
| | | | | | - Vikram Sobti
- Innovative Radiology, PC, River Forest, Illinois
| | - Brian D Reece
- The Spine and Orthopedic Academic Research Institute, Lewisville, Texas
| | - Jorge Felipe Ramírez León
- Fundación Universitaria Sanitas, Bogotá, Colombia, Research Team, Centro de Columna. Bogotá, Colombia, Centro de Cirugía de Mínima Invasión, CECIMIN-Clínica Reina Sofía, Bogotá, Colombia
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14
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Larson DB, Harvey H, Rubin DL, Irani N, Tse JR, Langlotz CP. Regulatory Frameworks for Development and Evaluation of Artificial Intelligence-Based Diagnostic Imaging Algorithms: Summary and Recommendations. J Am Coll Radiol 2020; 18:413-424. [PMID: 33096088 PMCID: PMC7574690 DOI: 10.1016/j.jacr.2020.09.060] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 09/23/2020] [Accepted: 09/23/2020] [Indexed: 12/28/2022]
Abstract
Although artificial intelligence (AI)-based algorithms for diagnosis hold promise for improving care, their safety and effectiveness must be ensured to facilitate wide adoption. Several recently proposed regulatory frameworks provide a solid foundation but do not address a number of issues that may prevent algorithms from being fully trusted. In this article, we review the major regulatory frameworks for software as a medical device applications, identify major gaps, and propose additional strategies to improve the development and evaluation of diagnostic AI algorithms. We identify the following major shortcomings of the current regulatory frameworks: (1) conflation of the diagnostic task with the diagnostic algorithm, (2) superficial treatment of the diagnostic task definition, (3) no mechanism to directly compare similar algorithms, (4) insufficient characterization of safety and performance elements, (5) lack of resources to assess performance at each installed site, and (6) inherent conflicts of interest. We recommend the following additional measures: (1) separate the diagnostic task from the algorithm, (2) define performance elements beyond accuracy, (3) divide the evaluation process into discrete steps, (4) encourage assessment by a third-party evaluator, (5) incorporate these elements into the manufacturers’ development process. Specifically, we recommend four phases of development and evaluation, analogous to those that have been applied to pharmaceuticals and proposed for software applications, to help ensure world-class performance of all algorithms at all installed sites. In the coming years, we anticipate the emergence of a substantial body of research dedicated to ensuring the accuracy, reliability, and safety of the algorithms.
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Affiliation(s)
- David B Larson
- Vice Chair, Education and Clinical Operations, Department of Radiology, Stanford University School of Medicine, Stanford, California.
| | - Hugh Harvey
- Institute for Cognitive Neuroscience, University College, London, UK
| | - Daniel L Rubin
- Director of Biomedical Informatics at Stanford Cancer Institute, Departments of Biomedical Data Science, Radiology, and Medicine, Stanford University School of Medicine, Stanford, California
| | - Neville Irani
- Department of Radiology, University of Kansas Medical Center, Kansas City, Kansas
| | - Justin R Tse
- Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, California
| | - Curtis P Langlotz
- Associate Chair, Information Systems, Department of Radiology, Stanford University School of Medicine, Stanford, California
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15
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Loncaric F, Camara O, Piella G, Bijnens B. Integration of artificial intelligence into clinical patient management: focus on cardiac imaging. ACTA ACUST UNITED AC 2020; 74:72-80. [PMID: 32819849 DOI: 10.1016/j.rec.2020.07.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Accepted: 07/01/2020] [Indexed: 10/23/2022]
Abstract
Cardiac imaging is a crucial component in the management of patients with heart disease, and as such it influences multiple, inter-related parts of the clinical workflow: physician-patient contact, image acquisition, image pre- and postprocessing, study reporting, diagnostics and outcome predictions, medical interventions, and, finally, knowledge-building through clinical research. With the gradual and ubiquitous infiltration of artificial intelligence into cardiology, it has become clear that, when used appropriately, it will influence and potentially improve-through automation, standardization and data integration-all components of the clinical workflow. This review aims to present a comprehensive view of full integration of artificial intelligence into the standard clinical patient management-with a focus on cardiac imaging, but applicable to all information handling-and to discuss current barriers that remain to be overcome before its widespread implementation and integration.
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Affiliation(s)
- Filip Loncaric
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.
| | - Oscar Camara
- BCN MedTech, Departament de Tecnologies de la Informació i les Comunicacions, Universitat Pompeu Fabra, Barcelona, Spain
| | - Gemma Piella
- BCN MedTech, Departament de Tecnologies de la Informació i les Comunicacions, Universitat Pompeu Fabra, Barcelona, Spain
| | - Bart Bijnens
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain; ICREA, Barcelona, Spain
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Chen X, Lu L, Shi J, Zhang X, Fan H, Fan B, Qu B, Lv Q, Hou S. Application and Prospect of a Mobile Hospital in Disaster Response. Disaster Med Public Health Prep 2020; 14:377-383. [PMID: 32317031 PMCID: PMC7251258 DOI: 10.1017/dmp.2020.113] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2020] [Revised: 04/18/2020] [Accepted: 04/18/2020] [Indexed: 02/06/2023]
Abstract
Disasters such as an earthquake, a flood, and an epidemic usually lead to large numbers of casualties accompanied by disruption of the functioning of local medical institutions. A rapid response of medical assistance and support is required. Mobile hospitals have been deployed by national and international organizations at disaster situations in the past decades, which play an important role in saving casualties and alleviating the shortage of medical resources. In this paper, we briefly introduce the types and characteristics of mobile hospitals used by medical teams in disaster rescue, including the aspects of structural form, organizational form, and mobile transportation. We also review the practices of mobile hospitals in disaster response and summarize the problems and needs of mobile hospitals in disaster rescue. Finally, we propose the development direction of mobile hospitals, especially on the development of intelligence, rapid deployment capabilities, and modularization, which provide suggestions for further research and development of mobile hospitals in the future.
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Affiliation(s)
- Xinlin Chen
- Institute of Disaster Medicine, Tianjin University; Tianjin Key Laboratory for Disaster Medicine Technology, Tianjin, China
| | - Lu Lu
- Institute of Disaster Medicine, Tianjin University; Tianjin Key Laboratory for Disaster Medicine Technology, Tianjin, China
| | - Jie Shi
- Institute of Disaster Medicine, Tianjin University; Tianjin Key Laboratory for Disaster Medicine Technology, Tianjin, China
| | - Xin Zhang
- Institute of Disaster Medicine, Tianjin University; Tianjin Key Laboratory for Disaster Medicine Technology, Tianjin, China
| | - Haojun Fan
- Institute of Disaster Medicine, Tianjin University; Tianjin Key Laboratory for Disaster Medicine Technology, Tianjin, China
| | - Bin Fan
- Institute of Disaster Medicine, Tianjin University; Tianjin Key Laboratory for Disaster Medicine Technology, Tianjin, China
| | - Bo Qu
- Institute of Disaster Medicine, Tianjin University; Tianjin Key Laboratory for Disaster Medicine Technology, Tianjin, China
| | - Qi Lv
- Institute of Disaster Medicine, Tianjin University; Tianjin Key Laboratory for Disaster Medicine Technology, Tianjin, China
| | - Shike Hou
- Institute of Disaster Medicine, Tianjin University; Tianjin Key Laboratory for Disaster Medicine Technology, Tianjin, China
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17
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Currie G, Hawk KE, Rohren E, Vial A, Klein R. Machine Learning and Deep Learning in Medical Imaging: Intelligent Imaging. J Med Imaging Radiat Sci 2019; 50:477-487. [DOI: 10.1016/j.jmir.2019.09.005] [Citation(s) in RCA: 156] [Impact Index Per Article: 31.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Accepted: 09/05/2019] [Indexed: 12/14/2022]
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18
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Allen B, Cook TS, Bello JA. Quality and Data Science. J Am Coll Radiol 2019; 16:1237-1238. [DOI: 10.1016/j.jacr.2019.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Accepted: 06/03/2019] [Indexed: 10/26/2022]
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