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Zhang K, Khosravi B, Vahdati S, Erickson BJ. FDA Review of Radiologic AI Algorithms: Process and Challenges. Radiology 2024; 310:e230242. [PMID: 38165243 DOI: 10.1148/radiol.230242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
A Food and Drug Administration (FDA)-cleared artificial intelligence (AI) algorithm misdiagnosed a finding as an intracranial hemorrhage in a patient, who was finally diagnosed with an ischemic stroke. This scenario highlights a notable failure mode of AI tools, emphasizing the importance of human-machine interaction. In this report, the authors summarize the review processes by the FDA for software as a medical device and the unique regulatory designs for radiologic AI/machine learning algorithms to ensure their safety in clinical practice. Then the challenges in maximizing the efficacy of these tools posed by their clinical implementation are discussed.
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Affiliation(s)
- Kuan Zhang
- From the Department of Radiology, Mayo Clinic, 200 1st St SW, Rochester, MN 55905
| | - Bardia Khosravi
- From the Department of Radiology, Mayo Clinic, 200 1st St SW, Rochester, MN 55905
| | - Sanaz Vahdati
- From the Department of Radiology, Mayo Clinic, 200 1st St SW, Rochester, MN 55905
| | - Bradley J Erickson
- From the Department of Radiology, Mayo Clinic, 200 1st St SW, Rochester, MN 55905
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Maghami M, Sattari SA, Tahmasbi M, Panahi P, Mozafari J, Shirbandi K. Diagnostic test accuracy of machine learning algorithms for the detection intracranial hemorrhage: a systematic review and meta-analysis study. Biomed Eng Online 2023; 22:114. [PMID: 38049809 PMCID: PMC10694901 DOI: 10.1186/s12938-023-01172-1] [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: 07/20/2023] [Accepted: 11/17/2023] [Indexed: 12/06/2023] Open
Abstract
BACKGROUND This systematic review and meta-analysis were conducted to objectively evaluate the evidence of machine learning (ML) in the patient diagnosis of Intracranial Hemorrhage (ICH) on computed tomography (CT) scans. METHODS Until May 2023, systematic searches were conducted in ISI Web of Science, PubMed, Scopus, Cochrane Library, IEEE Xplore Digital Library, CINAHL, Science Direct, PROSPERO, and EMBASE for studies that evaluated the diagnostic precision of ML model-assisted ICH detection. Patients with and without ICH as the target condition who were receiving CT-Scan were eligible for the research, which used ML algorithms based on radiologists' reports as the gold reference standard. For meta-analysis, pooled sensitivities, specificities, and a summary receiver operating characteristics curve (SROC) were used. RESULTS At last, after screening the title, abstract, and full paper, twenty-six retrospective and three prospective, and two retrospective/prospective studies were included. The overall (Diagnostic Test Accuracy) DTA of retrospective studies with a pooled sensitivity was 0.917 (95% CI 0.88-0.943, I2 = 99%). The pooled specificity was 0.945 (95% CI 0.918-0.964, I2 = 100%). The pooled diagnostic odds ratio (DOR) was 219.47 (95% CI 104.78-459.66, I2 = 100%). These results were significant for the specificity of the different network architecture models (p-value = 0.0289). However, the results for sensitivity (p-value = 0.6417) and DOR (p-value = 0.2187) were not significant. The ResNet algorithm has higher pooled specificity than other algorithms with 0.935 (95% CI 0.854-0.973, I2 = 93%). CONCLUSION This meta-analysis on DTA of ML algorithms for detecting ICH by assessing non-contrast CT-Scans shows the ML has an acceptable performance in diagnosing ICH. Using ResNet in ICH detection remains promising prediction was improved via training in an Architecture Learning Network (ALN).
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Affiliation(s)
- Masoud Maghami
- Medical Doctor (MD), School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Shahab Aldin Sattari
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Marziyeh Tahmasbi
- Department of Medical Imaging and Radiation Sciences, School of Allied Medical Sciences, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Pegah Panahi
- Medical Doctor (MD), School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
| | - Javad Mozafari
- Department of Emergency Medicine, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
- Department of Radiology, Resident (MD), EUREGIO-KLINIK Albert-Schweitzer-Straße GmbH, Nordhorn, Germany
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Agrawal A, Khatri GD, Khurana B, Sodickson AD, Liang Y, Dreizin D. A survey of ASER members on artificial intelligence in emergency radiology: trends, perceptions, and expectations. Emerg Radiol 2023; 30:267-277. [PMID: 36913061 PMCID: PMC10362990 DOI: 10.1007/s10140-023-02121-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 02/28/2023] [Indexed: 03/14/2023]
Abstract
PURPOSE There is a growing body of diagnostic performance studies for emergency radiology-related artificial intelligence/machine learning (AI/ML) tools; however, little is known about user preferences, concerns, experiences, expectations, and the degree of penetration of AI tools in emergency radiology. Our aim is to conduct a survey of the current trends, perceptions, and expectations regarding AI among American Society of Emergency Radiology (ASER) members. METHODS An anonymous and voluntary online survey questionnaire was e-mailed to all ASER members, followed by two reminder e-mails. A descriptive analysis of the data was conducted, and results summarized. RESULTS A total of 113 members responded (response rate 12%). The majority were attending radiologists (90%) with greater than 10 years' experience (80%) and from an academic practice (65%). Most (55%) reported use of commercial AI CAD tools in their practice. Workflow prioritization based on pathology detection, injury or disease severity grading and classification, quantitative visualization, and auto-population of structured reports were identified as high-value tasks. Respondents overwhelmingly indicated a need for explainable and verifiable tools (87%) and the need for transparency in the development process (80%). Most respondents did not feel that AI would reduce the need for emergency radiologists in the next two decades (72%) or diminish interest in fellowship programs (58%). Negative perceptions pertained to potential for automation bias (23%), over-diagnosis (16%), poor generalizability (15%), negative impact on training (11%), and impediments to workflow (10%). CONCLUSION ASER member respondents are in general optimistic about the impact of AI in the practice of emergency radiology and its impact on the popularity of emergency radiology as a subspecialty. The majority expect to see transparent and explainable AI models with the radiologist as the decision-maker.
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Affiliation(s)
- Anjali Agrawal
- New Delhi operations, Teleradiology Solutions, Delhi, India
| | - Garvit D Khatri
- Nuclear Medicine, Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA
| | - Bharti Khurana
- Emergency Radiology, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Aaron D Sodickson
- Emergency Radiology, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Yuanyuan Liang
- Epidemiology & Public Health, University of Maryland School of Medicine, Baltimore, MD, USA
| | - David Dreizin
- Trauma and Emergency Radiology, Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, Baltimore, MD, USA.
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Dreizin D, Staziaki PV, Khatri GD, Beckmann NM, Feng Z, Liang Y, Delproposto ZS, Klug M, Spann JS, Sarkar N, Fu Y. Artificial intelligence CAD tools in trauma imaging: a scoping review from the American Society of Emergency Radiology (ASER) AI/ML Expert Panel. Emerg Radiol 2023; 30:251-265. [PMID: 36917287 PMCID: PMC10640925 DOI: 10.1007/s10140-023-02120-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 02/27/2023] [Indexed: 03/16/2023]
Abstract
BACKGROUND AI/ML CAD tools can potentially improve outcomes in the high-stakes, high-volume model of trauma radiology. No prior scoping review has been undertaken to comprehensively assess tools in this subspecialty. PURPOSE To map the evolution and current state of trauma radiology CAD tools along key dimensions of technology readiness. METHODS Following a search of databases, abstract screening, and full-text document review, CAD tool maturity was charted using elements of data curation, performance validation, outcomes research, explainability, user acceptance, and funding patterns. Descriptive statistics were used to illustrate key trends. RESULTS A total of 4052 records were screened, and 233 full-text articles were selected for content analysis. Twenty-one papers described FDA-approved commercial tools, and 212 reported algorithm prototypes. Works ranged from foundational research to multi-reader multi-case trials with heterogeneous external data. Scalable convolutional neural network-based implementations increased steeply after 2016 and were used in all commercial products; however, options for explainability were narrow. Of FDA-approved tools, 9/10 performed detection tasks. Dataset sizes ranged from < 100 to > 500,000 patients, and commercialization coincided with public dataset availability. Cross-sectional torso datasets were uniformly small. Data curation methods with ground truth labeling by independent readers were uncommon. No papers assessed user acceptance, and no method included human-computer interaction. The USA and China had the highest research output and frequency of research funding. CONCLUSIONS Trauma imaging CAD tools are likely to improve patient care but are currently in an early stage of maturity, with few FDA-approved products for a limited number of uses. The scarcity of high-quality annotated data remains a major barrier.
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Affiliation(s)
- David Dreizin
- Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, Baltimore, MD, USA.
| | - Pedro V Staziaki
- Cardiothoracic Imaging, Department of Radiology, Larner College of Medicine, University of Vermont, Burlington, VT, USA
| | - Garvit D Khatri
- Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA
| | - Nicholas M Beckmann
- Memorial Hermann Orthopedic & Spine Hospital, McGovern Medical School at UTHealth, Houston, TX, USA
| | - Zhaoyong Feng
- Epidemiology & Public Health, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Yuanyuan Liang
- Epidemiology & Public Health, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Zachary S Delproposto
- Division of Emergency Radiology, Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | | | - J Stephen Spann
- Department of Radiology, University of Alabama at Birmingham Heersink School of Medicine, Birmingham, AL, USA
| | - Nathan Sarkar
- University of Maryland School of Medicine, Baltimore, MD, USA
| | - Yunting Fu
- Health Sciences and Human Services Library, University of Maryland, Baltimore, Baltimore, MD, USA
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Soyer P, Fishman EK, Rowe SP, Patlas MN, Chassagnon G. Does artificial intelligence surpass the radiologist? Diagn Interv Imaging 2022; 103:445-447. [PMID: 35973913 DOI: 10.1016/j.diii.2022.08.001] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 08/02/2022] [Indexed: 12/30/2022]
Affiliation(s)
- Philippe Soyer
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, 75014 Paris, France; Université Paris Cité, Faculté de Médecine, 75006, Paris, France.
| | - Elliot K Fishman
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Steven P Rowe
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Michael N Patlas
- Department of Radiology, Hamilton General Hospital, McMaster University Hamilton, ON, Canada L8L 2X2
| | - Guillaume Chassagnon
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, 75014 Paris, France; Université Paris Cité, Faculté de Médecine, 75006, Paris, France
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