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Zhang Q, Fotaki A, Ghadimi S, Wang Y, Doneva M, Wetzl J, Delfino JG, O'Regan DP, Prieto C, Epstein FH. Improving the efficiency and accuracy of CMR with AI - review of evidence and proposition of a roadmap to clinical translation. J Cardiovasc Magn Reson 2024:101051. [PMID: 38909656 DOI: 10.1016/j.jocmr.2024.101051] [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: 03/17/2024] [Revised: 06/09/2024] [Accepted: 06/18/2024] [Indexed: 06/25/2024] Open
Abstract
Cardiovascular magnetic resonance (CMR) is an important imaging modality for the assessment of heart disease; however, limitations of CMR include long exam times and high complexity compared to other cardiac imaging modalities. Recently advancements in artificial intelligence (AI) technology have shown great potential to address many CMR limitations. While the developments are remarkable, translation of AI-based methods into real-world CMR clinical practice remains at a nascent stage and much work lies ahead to realize the full potential of AI for CMR. Herein we review recent cutting-edge and representative examples demonstrating how AI can advance CMR in areas such as exam planning, accelerated image reconstruction, post-processing, quality control, classification and diagnosis. These advances can be applied to speed up and simplify essentially every application including cine, strain, late gadolinium enhancement, parametric mapping, 3D whole heart, flow, perfusion and others. AI is a unique technology based on training models using data. Beyond reviewing the literature, this paper discusses important AI-specific issues in the context of CMR, including (1) properties and characteristics of datasets for training and validation, (2) previously published guidelines for reporting CMR AI research, (3) considerations around clinical deployment, (4) responsibilities of clinicians and the need for multi-disciplinary teams in the development and deployment of AI in CMR, (5) industry considerations, and (6) regulatory perspectives. Understanding and consideration of all these factors will contribute to the effective and ethical deployment of AI to improve clinical CMR.
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Affiliation(s)
- Qiang Zhang
- Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK; Big Data Institute, University of Oxford, Oxford, UK.
| | - Anastasia Fotaki
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; Royal Brompton Hospital, Guy's and St Thomas' NHS Foundation Trust, London, UK.
| | - Sona Ghadimi
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
| | - Yu Wang
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
| | | | - Jens Wetzl
- Siemens Healthineers AG, Erlangen, Germany.
| | - Jana G Delfino
- US Food and Drug Administration, Center for Devices and Radiological Health (CDRH), Office of Science and Engineering Laboratories (OSEL), Silver Spring, MD, USA.
| | - Declan P O'Regan
- MRC Laboratory of Medical Sciences, Imperial College London, UK.
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile.
| | - Frederick H Epstein
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
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2
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Shi Z, Hu B, Lu M, Chen Z, Zhang M, Yu Y, Zhou C, Zhong J, Wu B, Zhang X, Wei Y, Zhang LJ. Assessing the Impact of an Artificial Intelligence-Based Model for Intracranial Aneurysm Detection in CT Angiography on Patient Diagnosis and Outcomes (IDEAL Study)-a protocol for a multicenter, double-blinded randomized controlled trial. Trials 2024; 25:358. [PMID: 38835091 PMCID: PMC11151720 DOI: 10.1186/s13063-024-08184-9] [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: 12/09/2023] [Accepted: 05/20/2024] [Indexed: 06/06/2024] Open
Abstract
BACKGROUND This multicenter, double-blinded, randomized controlled trial (RCT) aims to assess the impact of an artificial intelligence (AI)-based model on the efficacy of intracranial aneurysm detection in CT angiography (CTA) and its influence on patients' short-term and long-term outcomes. METHODS Study design: Prospective, multicenter, double-blinded RCT. SETTINGS The model was designed for the automatic detection of intracranial aneurysms from original CTA images. PARTICIPANTS Adult inpatients and outpatients who are scheduled for head CTA scanning. Randomization groups: (1) Experimental Group: Head CTA interpreted by radiologists with the assistance of the True-AI-integrated intracranial aneurysm diagnosis strategy (True-AI arm). (2) Control Group: Head CTA interpreted by radiologists with the assistance of the Sham-AI-integrated intracranial aneurysm diagnosis strategy (Sham-AI arm). RANDOMIZATION Block randomization, stratified by center, gender, and age group. PRIMARY OUTCOMES Coprimary outcomes of superiority in patient-level sensitivity and noninferiority in specificity for the True-AI arm to the Sham-AI arm in intracranial aneurysms. SECONDARY OUTCOMES Diagnostic performance for other intracranial lesions, detection rates, workload of CTA interpretation, resource utilization, treatment-related clinical events, aneurysm-related events, quality of life, and cost-effectiveness analysis. BLINDING Study participants and participating radiologists will be blinded to the intervention. SAMPLE SIZE Based on our pilot study, the patient-level sensitivity is assumed to be 0.65 for the Sham-AI arm and 0.75 for the True-AI arm, with specificities of 0.90 and 0.88, respectively. The prevalence of intracranial aneurysms for patients undergoing head CTA in the hospital is approximately 12%. To establish superiority in sensitivity and noninferiority in specificity with a margin of 5% using a one-sided α = 0.025 to ensure that the power of coprimary endpoint testing reached 0.80 and a 5% attrition rate, the sample size was determined to be 6450 in a 1:1 allocation to True-AI or Sham-AI arm. DISCUSSION The study will determine the precise impact of the AI system on the detection performance for intracranial aneurysms in a double-blinded design and following the real-world effects on patients' short-term and long-term outcomes. TRIAL REGISTRATION This trial has been registered with the NIH, U.S. National Library of Medicine at ClinicalTrials.gov, ID: NCT06118840 . Registered 11 November 2023.
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Affiliation(s)
- Zhao Shi
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002, China
| | - Bin Hu
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002, China
| | - Mengjie Lu
- Health Science Center, Ningbo University, Zhejiang, 315211, China
| | - Zijian Chen
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002, China
| | - Manting Zhang
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 210002, China
| | - Yizhou Yu
- Department of Computer Science, The University of Hong Kong, Hong Kong, China
| | - Changsheng Zhou
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002, China
| | - Jian Zhong
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002, China
| | - Bingqian Wu
- Jinling Hospital, Nanjing Medical University, Nanjing, 210002, China
| | - Xueming Zhang
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002, China
| | - Yongyue Wei
- Center for Public Health and Epidemic Preparedness & Response, Peking University, Beijing, 100191, China
| | - Long Jiang Zhang
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210002, China.
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3
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Yue Y, Jiang M, Zhang X, Xu J, Ye H, Zhang F, Li Z, Li Y. Mpox-AISM: AI-mediated super monitoring for mpox and like-mpox. iScience 2024; 27:109766. [PMID: 38711448 PMCID: PMC11070687 DOI: 10.1016/j.isci.2024.109766] [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: 05/27/2023] [Revised: 09/16/2023] [Accepted: 04/15/2024] [Indexed: 05/08/2024] Open
Abstract
Swift and accurate diagnosis for earlier-stage monkeypox (mpox) patients is crucial to avoiding its spread. However, the similarities between common skin disorders and mpox and the need for professional diagnosis unavoidably impaired the diagnosis of earlier-stage mpox patients and contributed to mpox outbreak. To address the challenge, we proposed "Super Monitoring", a real-time visualization technique employing artificial intelligence (AI) and Internet technology to diagnose earlier-stage mpox cheaply, conveniently, and quickly. Concretely, AI-mediated "Super Monitoring" (mpox-AISM) integrates deep learning models, data augmentation, self-supervised learning, and cloud services. According to publicly accessible datasets, mpox-AISM's Precision, Recall, Specificity, and F1-score in diagnosing mpox reach 99.3%, 94.1%, 99.9%, and 96.6%, respectively, and it achieves 94.51% accuracy in diagnosing mpox, six like-mpox skin disorders, and normal skin. With the Internet and communication terminal, mpox-AISM has the potential to perform real-time and accurate diagnosis for earlier-stage mpox in real-world scenarios, thereby preventing mpox outbreak.
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Affiliation(s)
- Yubiao Yue
- School of Biomedical Engineering, Guangzhou Medical University, Guangzhou 511436, China
| | - Minghua Jiang
- Department of science and education, Dermatological department, Foshan Sanshui District People’s Hospital, Foshan 528199, China
| | - Xinyue Zhang
- School of Biomedical Engineering, Guangzhou Medical University, Guangzhou 511436, China
| | - Jialong Xu
- School of Biomedical Engineering, Guangzhou Medical University, Guangzhou 511436, China
| | - Huacong Ye
- School of Biomedical Engineering, Guangzhou Medical University, Guangzhou 511436, China
| | - Fan Zhang
- Department of science and education, Dermatological department, Foshan Sanshui District People’s Hospital, Foshan 528199, China
| | - Zhenzhang Li
- School of Mathematics and Systems Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China
- School of Biomedical Engineering, Guangzhou Medical University, Guangzhou 511436, China
| | - Yang Li
- School of Biomedical Engineering, Guangzhou Medical University, Guangzhou 511436, China
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Aasem M, Javed Iqbal M. Toward explainable AI in radiology: Ensemble-CAM for effective thoracic disease localization in chest X-ray images using weak supervised learning. Front Big Data 2024; 7:1366415. [PMID: 38756502 PMCID: PMC11096460 DOI: 10.3389/fdata.2024.1366415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Accepted: 04/08/2024] [Indexed: 05/18/2024] Open
Abstract
Chest X-ray (CXR) imaging is widely employed by radiologists to diagnose thoracic diseases. Recently, many deep learning techniques have been proposed as computer-aided diagnostic (CAD) tools to assist radiologists in minimizing the risk of incorrect diagnosis. From an application perspective, these models have exhibited two major challenges: (1) They require large volumes of annotated data at the training stage and (2) They lack explainable factors to justify their outcomes at the prediction stage. In the present study, we developed a class activation mapping (CAM)-based ensemble model, called Ensemble-CAM, to address both of these challenges via weakly supervised learning by employing explainable AI (XAI) functions. Ensemble-CAM utilizes class labels to predict the location of disease in association with interpretable features. The proposed work leverages ensemble and transfer learning with class activation functions to achieve three objectives: (1) minimizing the dependency on strongly annotated data when locating thoracic diseases, (2) enhancing confidence in predicted outcomes by visualizing their interpretable features, and (3) optimizing cumulative performance via fusion functions. Ensemble-CAM was trained on three CXR image datasets and evaluated through qualitative and quantitative measures via heatmaps and Jaccard indices. The results reflect the enhanced performance and reliability in comparison to existing standalone and ensembled models.
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Affiliation(s)
- Muhammad Aasem
- Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan
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5
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Bendtsen MG, Hitz MF. Opportunistic Identification of Vertebral Compression Fractures on CT Scans of the Chest and Abdomen, Using an AI Algorithm, in a Real-Life Setting. Calcif Tissue Int 2024; 114:468-479. [PMID: 38530406 PMCID: PMC11061033 DOI: 10.1007/s00223-024-01196-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 02/13/2024] [Indexed: 03/28/2024]
Abstract
This study evaluated the performance of a vertebral fracture detection algorithm (HealthVCF) in a real-life setting and assessed the impact on treatment and diagnostic workflow. HealthVCF was used to identify moderate and severe vertebral compression fractures (VCF) at a Danish hospital. Around 10,000 CT scans were processed by the HealthVCF and CT scans positive for VCF formed both the baseline and 6-months follow-up cohort. To determine performance of the algorithm 1000 CT scans were evaluated by specialized radiographers to determine performance of the algorithm. Sensitivity was 0.68 (CI 0.581-0.776) and specificity 0.91 (CI 0.89-0.928). At 6-months follow-up, 18% of the 538 patients in the retrospective cohort were dead, 78 patients had been referred for a DXA scan, while 25 patients had been diagnosed with osteoporosis. A higher mortality rate was seen in patients not known with osteoporosis at baseline compared to patients known with osteoporosis at baseline, 12.8% versus 22.6% (p = 0.003). Patients receiving bisphosphonates had a lower mortality rate (9.6%) compared to the rest of the population (20.9%) (p = 0.003). HealthVCF demonstrated a poorer performance than expected, and the tested version is not generalizable to the Danish population. Based on its specificity, the HealthVCF can be used as a tool to prioritize resources in opportunistic identification of VCF's. Implementing such a tool on its own only resulted in a small number of new diagnoses of osteoporosis and referrals to DXA scans during a 6-month follow-up period. To increase efficiency, the HealthVCF should be integrated with Fracture Liaison Services (FLS).
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Affiliation(s)
| | - Mette Friberg Hitz
- Research Unit, Medical Department, Zealand University Hospital, Koege, Denmark
- Institute of Clinical Medicine, University of Copenhagen, Koege, Denmark
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6
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Han R, Acosta JN, Shakeri Z, Ioannidis JPA, Topol EJ, Rajpurkar P. Randomised controlled trials evaluating artificial intelligence in clinical practice: a scoping review. Lancet Digit Health 2024; 6:e367-e373. [PMID: 38670745 PMCID: PMC11068159 DOI: 10.1016/s2589-7500(24)00047-5] [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: 05/08/2023] [Revised: 03/01/2024] [Accepted: 03/04/2024] [Indexed: 04/28/2024]
Abstract
This scoping review of randomised controlled trials on artificial intelligence (AI) in clinical practice reveals an expanding interest in AI across clinical specialties and locations. The USA and China are leading in the number of trials, with a focus on deep learning systems for medical imaging, particularly in gastroenterology and radiology. A majority of trials (70 [81%] of 86) report positive primary endpoints, primarily related to diagnostic yield or performance; however, the predominance of single-centre trials, little demographic reporting, and varying reports of operational efficiency raise concerns about the generalisability and practicality of these results. Despite the promising outcomes, considering the likelihood of publication bias and the need for more comprehensive research including multicentre trials, diverse outcome measures, and improved reporting standards is crucial. Future AI trials should prioritise patient-relevant outcomes to fully understand AI's true effects and limitations in health care.
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Affiliation(s)
- Ryan Han
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA; Department of Computer Science, Stanford University, Stanford, CA, USA; University of California Los Angeles-Caltech Medical Scientist Training Program, Los Angeles, CA, USA
| | - Julián N Acosta
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA; Rad AI, San Francisco, CA, USA
| | - Zahra Shakeri
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - John P A Ioannidis
- Stanford Prevention Research Center, Department of Medicine, Stanford University, Stanford, CA, USA; Meta-Research Innovation Center at Stanford, Stanford University, Stanford, CA, USA
| | - Eric J Topol
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA, USA.
| | - Pranav Rajpurkar
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
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7
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Brady AP, Allen B, Chong J, Kotter E, Kottler N, Mongan J, Oakden-Rayner L, Dos Santos DP, Tang A, Wald C, Slavotinek J. Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement From the ACR, CAR, ESR, RANZCR & RSNA. Can Assoc Radiol J 2024; 75:226-244. [PMID: 38251882 DOI: 10.1177/08465371231222229] [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] [Indexed: 01/23/2024] Open
Abstract
Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever‑growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi‑society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools.
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Affiliation(s)
| | - Bibb Allen
- Department of Radiology, Grandview Medical Center, Birmingham, AL, USA
- Data Science Institute, American College of Radiology, Reston, VA, USA
| | - Jaron Chong
- Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Elmar Kotter
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Nina Kottler
- Radiology Partners, El Segundo, CA, USA
- Stanford Center for Artificial Intelligence in Medicine & Imaging, Palo Alto, CA, USA
| | - John Mongan
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | - Lauren Oakden-Rayner
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia
| | - Daniel Pinto Dos Santos
- Department of Radiology, University Hospital of Cologne, Cologne, Germany
- Department of Radiology, University Hospital of Frankfurt, Frankfurt, Germany
| | - An Tang
- Department of Radiology, Radiation Oncology, and Nuclear Medicine, Université de Montréal, Montréal, QC, Canada
| | - Christoph Wald
- Department of Radiology, Lahey Hospital & Medical Center, Burlington, MA, USA
- Tufts University Medical School, Boston, MA, USA
- American College of Radiology, Reston, VA, USA
| | - John Slavotinek
- South Australia Medical Imaging, Flinders Medical Centre Adelaide, SA, Australia
- College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
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8
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Hillis JM, Visser JJ, Cliff ERS, van der Geest-Aspers K, Bizzo BC, Dreyer KJ, Adams-Prassl J, Andriole KP. The lucent yet opaque challenge of regulating artificial intelligence in radiology. NPJ Digit Med 2024; 7:69. [PMID: 38491126 PMCID: PMC10942968 DOI: 10.1038/s41746-024-01071-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Accepted: 03/07/2024] [Indexed: 03/18/2024] Open
Affiliation(s)
- James M Hillis
- Data Science Office, Mass General Brigham, Boston, MA, USA.
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
| | - Jacob J Visser
- Department of Radiology & Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Edward R Scheffer Cliff
- Harvard Medical School, Boston, MA, USA
- Program on Regulation, Therapeutics and Law, Brigham and Women's Hospital, Boston, MA, USA
| | | | - Bernardo C Bizzo
- Data Science Office, Mass General Brigham, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Keith J Dreyer
- Data Science Office, Mass General Brigham, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | | | - Katherine P Andriole
- Data Science Office, Mass General Brigham, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
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9
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Yao S, Dai F, Sun P, Zhang W, Qian B, Lu H. Enhancing the fairness of AI prediction models by Quasi-Pareto improvement among heterogeneous thyroid nodule population. Nat Commun 2024; 15:1958. [PMID: 38438371 PMCID: PMC10912763 DOI: 10.1038/s41467-024-44906-y] [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: 05/17/2023] [Accepted: 01/09/2024] [Indexed: 03/06/2024] Open
Abstract
Artificial Intelligence (AI) models for medical diagnosis often face challenges of generalizability and fairness. We highlighted the algorithmic unfairness in a large thyroid ultrasound dataset with significant diagnostic performance disparities across subgroups linked causally to sample size imbalances. To address this, we introduced the Quasi-Pareto Improvement (QPI) approach and a deep learning implementation (QP-Net) combining multi-task learning and domain adaptation to improve model performance among disadvantaged subgroups without compromising overall population performance. On the thyroid ultrasound dataset, our method significantly mitigated the area under curve (AUC) disparity for three less-prevalent subgroups by 0.213, 0.112, and 0.173 while maintaining the AUC for dominant subgroups; we also further confirmed the generalizability of our approach on two public datasets: the ISIC2019 skin disease dataset and the CheXpert chest radiograph dataset. Here we show the QPI approach to be widely applicable in promoting AI for equitable healthcare outcomes.
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Affiliation(s)
- Siqiong Yao
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, PR China
- SJTU-Yale Joint Center of Biostatistics and Data Science, National Center for Translational Medicine, MoE Key Lab of Artificial Intelligence, AI Institute Shanghai Jiao Tong University, Shanghai, 200240, PR China
| | - Fang Dai
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, PR China
| | - Peng Sun
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, PR China
| | - Weituo Zhang
- Hongqiao International Institute of Medicine, Shanghai Tong Ren Hospital and School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, PR China.
| | - Biyun Qian
- Hongqiao International Institute of Medicine, Shanghai Tong Ren Hospital and School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, PR China.
| | - Hui Lu
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, PR China.
- SJTU-Yale Joint Center of Biostatistics and Data Science, National Center for Translational Medicine, MoE Key Lab of Artificial Intelligence, AI Institute Shanghai Jiao Tong University, Shanghai, 200240, PR China.
- Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine, NHC Key Laboratory of Medical Embryogenesis and Developmental Molecular Biology & Shanghai Key Laboratory of Embryo and Reproduction Engineering, Shanghai, 200020, PR China.
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10
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Kore A, Abbasi Bavil E, Subasri V, Abdalla M, Fine B, Dolatabadi E, Abdalla M. Empirical data drift detection experiments on real-world medical imaging data. Nat Commun 2024; 15:1887. [PMID: 38424096 PMCID: PMC10904813 DOI: 10.1038/s41467-024-46142-w] [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: 07/31/2023] [Accepted: 02/14/2024] [Indexed: 03/02/2024] Open
Abstract
While it is common to monitor deployed clinical artificial intelligence (AI) models for performance degradation, it is less common for the input data to be monitored for data drift - systemic changes to input distributions. However, when real-time evaluation may not be practical (eg., labeling costs) or when gold-labels are automatically generated, we argue that tracking data drift becomes a vital addition for AI deployments. In this work, we perform empirical experiments on real-world medical imaging to evaluate three data drift detection methods' ability to detect data drift caused (a) naturally (emergence of COVID-19 in X-rays) and (b) synthetically. We find that monitoring performance alone is not a good proxy for detecting data drift and that drift-detection heavily depends on sample size and patient features. Our work discusses the need and utility of data drift detection in various scenarios and highlights gaps in knowledge for the practical application of existing methods.
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Affiliation(s)
- Ali Kore
- Vector Institute, Toronto, Canada
| | | | - Vallijah Subasri
- Peter Munk Cardiac Center, University Health Network, Toronto, ON, Canada
| | - Moustafa Abdalla
- Department of Surgery, Harvard Medical School, Massachusetts General Hospital, Boston, USA
| | - Benjamin Fine
- Institute for Better Health, Trillium Health Partners, Mississauga, Canada
- Department of Medical Imaging, University of Toronto, Toronto, Canada
| | - Elham Dolatabadi
- Vector Institute, Toronto, Canada
- School of Health Policy and Management, Faculty of Health, York University, Toronto, Canada
| | - Mohamed Abdalla
- Institute for Better Health, Trillium Health Partners, Mississauga, Canada.
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11
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Boverhof BJ, Redekop WK, Bos D, Starmans MPA, Birch J, Rockall A, Visser JJ. Radiology AI Deployment and Assessment Rubric (RADAR) to bring value-based AI into radiological practice. Insights Imaging 2024; 15:34. [PMID: 38315288 PMCID: PMC10844175 DOI: 10.1186/s13244-023-01599-z] [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: 08/31/2023] [Accepted: 11/14/2023] [Indexed: 02/07/2024] Open
Abstract
OBJECTIVE To provide a comprehensive framework for value assessment of artificial intelligence (AI) in radiology. METHODS This paper presents the RADAR framework, which has been adapted from Fryback and Thornbury's imaging efficacy framework to facilitate the valuation of radiology AI from conception to local implementation. Local efficacy has been newly introduced to underscore the importance of appraising an AI technology within its local environment. Furthermore, the RADAR framework is illustrated through a myriad of study designs that help assess value. RESULTS RADAR presents a seven-level hierarchy, providing radiologists, researchers, and policymakers with a structured approach to the comprehensive assessment of value in radiology AI. RADAR is designed to be dynamic and meet the different valuation needs throughout the AI's lifecycle. Initial phases like technical and diagnostic efficacy (RADAR-1 and RADAR-2) are assessed pre-clinical deployment via in silico clinical trials and cross-sectional studies. Subsequent stages, spanning from diagnostic thinking to patient outcome efficacy (RADAR-3 to RADAR-5), require clinical integration and are explored via randomized controlled trials and cohort studies. Cost-effectiveness efficacy (RADAR-6) takes a societal perspective on financial feasibility, addressed via health-economic evaluations. The final level, RADAR-7, determines how prior valuations translate locally, evaluated through budget impact analysis, multi-criteria decision analyses, and prospective monitoring. CONCLUSION The RADAR framework offers a comprehensive framework for valuing radiology AI. Its layered, hierarchical structure, combined with a focus on local relevance, aligns RADAR seamlessly with the principles of value-based radiology. CRITICAL RELEVANCE STATEMENT The RADAR framework advances artificial intelligence in radiology by delineating a much-needed framework for comprehensive valuation. KEYPOINTS • Radiology artificial intelligence lacks a comprehensive approach to value assessment. • The RADAR framework provides a dynamic, hierarchical method for thorough valuation of radiology AI. • RADAR advances clinical radiology by bridging the artificial intelligence implementation gap.
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Affiliation(s)
- Bart-Jan Boverhof
- Erasmus School of Health Policy and Management, Erasmus University Rotterdam, Rotterdam, The Netherlands
| | - W Ken Redekop
- Erasmus School of Health Policy and Management, Erasmus University Rotterdam, Rotterdam, The Netherlands
| | - Daniel Bos
- Department of Epidemiology, Erasmus University Medical Centre, Rotterdam, The Netherlands
- Department of Radiology & Nuclear Medicine, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Martijn P A Starmans
- Department of Radiology & Nuclear Medicine, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | | | - Andrea Rockall
- Department of Surgery & Cancer, Imperial College London, London, UK
| | - Jacob J Visser
- Department of Radiology & Nuclear Medicine, Erasmus University Medical Centre, Rotterdam, The Netherlands.
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12
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Maier-Hein L, Reinke A, Godau P, Tizabi MD, Buettner F, Christodoulou E, Glocker B, Isensee F, Kleesiek J, Kozubek M, Reyes M, Riegler MA, Wiesenfarth M, Kavur AE, Sudre CH, Baumgartner M, Eisenmann M, Heckmann-Nötzel D, Rädsch T, Acion L, Antonelli M, Arbel T, Bakas S, Benis A, Blaschko MB, Cardoso MJ, Cheplygina V, Cimini BA, Collins GS, Farahani K, Ferrer L, Galdran A, van Ginneken B, Haase R, Hashimoto DA, Hoffman MM, Huisman M, Jannin P, Kahn CE, Kainmueller D, Kainz B, Karargyris A, Karthikesalingam A, Kofler F, Kopp-Schneider A, Kreshuk A, Kurc T, Landman BA, Litjens G, Madani A, Maier-Hein K, Martel AL, Mattson P, Meijering E, Menze B, Moons KGM, Müller H, Nichyporuk B, Nickel F, Petersen J, Rajpoot N, Rieke N, Saez-Rodriguez J, Sánchez CI, Shetty S, van Smeden M, Summers RM, Taha AA, Tiulpin A, Tsaftaris SA, Van Calster B, Varoquaux G, Jäger PF. Metrics reloaded: recommendations for image analysis validation. Nat Methods 2024; 21:195-212. [PMID: 38347141 PMCID: PMC11182665 DOI: 10.1038/s41592-023-02151-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 12/12/2023] [Indexed: 02/15/2024]
Abstract
Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. In biomedical image analysis, chosen performance metrics often do not reflect the domain interest, and thus fail to adequately measure scientific progress and hinder translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. Developed by a large international consortium in a multistage Delphi process, it is based on the novel concept of a problem fingerprint-a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), dataset and algorithm output. On the basis of the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as classification tasks at image, object or pixel level, namely image-level classification, object detection, semantic segmentation and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases.
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Affiliation(s)
- Lena Maier-Hein
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany.
- German Cancer Research Center (DKFZ) Heidelberg, HI Helmholtz Imaging, Heidelberg, Germany.
- Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany.
- Medical Faculty, Heidelberg University, Heidelberg, Germany.
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany.
| | - Annika Reinke
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany.
- German Cancer Research Center (DKFZ) Heidelberg, HI Helmholtz Imaging, Heidelberg, Germany.
- Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany.
| | - Patrick Godau
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
- Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany
| | - Minu D Tizabi
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany
| | - Florian Buettner
- German Cancer Consortium (DKTK), partner site Frankfurt/Mainz, a partnership between DKFZ and UCT Frankfurt-Marburg, Frankfurt am Main, Germany
- German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany
- Department of Medicine, Goethe University Frankfurt, Frankfurt am Main, Germany
- Department of Informatics, Goethe University Frankfurt, Frankfurt am Main, Germany
- Frankfurt Cancer Insititute, Frankfurt am Main, Germany
| | - Evangelia Christodoulou
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
| | - Ben Glocker
- Department of Computing, Imperial College London, South Kensington Campus, London, UK
| | - Fabian Isensee
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
- German Cancer Research Center (DKFZ) Heidelberg, HI Applied Computer Vision Lab, Heidelberg, Germany
| | - Jens Kleesiek
- Institute for AI in Medicine, University Medicine Essen, Essen, Germany
| | - Michal Kozubek
- Centre for Biomedical Image Analysis and Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Mauricio Reyes
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
- Department of Radiation Oncology, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Michael A Riegler
- Simula Metropolitan Center for Digital Engineering, Oslo, Norway
- Department of Computer Science, UiT The Arctic University of Norway, Tromsø, Norway
| | - Manuel Wiesenfarth
- German Cancer Research Center (DKFZ) Heidelberg, Division of Biostatistics, Heidelberg, Germany
| | - A Emre Kavur
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
- German Cancer Research Center (DKFZ) Heidelberg, HI Applied Computer Vision Lab, Heidelberg, Germany
| | - Carole H Sudre
- MRC Unit for Lifelong Health and Ageing at UCL and Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- School of Biomedical Engineering and Imaging Science, King's College London, London, UK
| | - Michael Baumgartner
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
| | - Matthias Eisenmann
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
| | - Doreen Heckmann-Nötzel
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and University Medical Center Heidelberg, Heidelberg, Germany
| | - Tim Rädsch
- German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany
- German Cancer Research Center (DKFZ) Heidelberg, HI Helmholtz Imaging, Heidelberg, Germany
| | - Laura Acion
- Instituto de Cálculo, CONICET - Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Michela Antonelli
- School of Biomedical Engineering and Imaging Science, King's College London, London, UK
- Centre for Medical Image Computing, University College London, London, UK
| | - Tal Arbel
- Centre for Intelligent Machines and MILA (Québec Artificial Intelligence Institute), McGill University, Montréal, Quebec, Canada
| | - Spyridon Bakas
- Division of Computational Pathology, Department of Pathology & Laboratory Medicine, Indiana University School of Medicine, IU Health Information and Translational Sciences Building, Indianapolis, IN, USA
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Arriel Benis
- Department of Digital Medical Technologies, Holon Institute of Technology, Holon, Israel
- European Federation for Medical Informatics, Le Mont-sur-Lausanne, Switzerland
| | - Matthew B Blaschko
- Center for Processing Speech and Images, Department of Electrical Engineering, KU Leuven, Leuven, Belgium
| | - M Jorge Cardoso
- School of Biomedical Engineering and Imaging Science, King's College London, London, UK
| | - Veronika Cheplygina
- Department of Computer Science, IT University of Copenhagen, Copenhagen, Denmark
| | - Beth A Cimini
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Gary S Collins
- Centre for Statistics in Medicine, University of Oxford, Nuffield Orthopaedic Centre, Oxford, UK
| | - Keyvan Farahani
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Bethesda, MD, USA
| | - Luciana Ferrer
- Instituto de Investigación en Ciencias de la Computación (ICC), CONICET-UBA, Ciudad Autónoma de Buenos Aires, Buenos Aires, Argentina
| | - Adrian Galdran
- BCN Medtech, Universitat Pompeu Fabra, Barcelona, Spain
- Australian Institute for Machine Learning AIML, University of Adelaide, Adelaide, South Australia, Australia
| | - Bram van Ginneken
- Fraunhofer MEVIS, Bremen, Germany
- Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Robert Haase
- Technische Universität (TU) Dresden, DFG Cluster of Excellence 'Physics of Life', Dresden, Germany
- Center for Systems Biology, Dresden, Germany
- Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Leipzig University, Leipzig, Germany
| | - Daniel A Hashimoto
- Department of Surgery, Perelman School of Medicine, Philadelphia, PA, USA
- General Robotics Automation Sensing and Perception Laboratory, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael M Hoffman
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Merel Huisman
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Pierre Jannin
- Laboratoire Traitement du Signal et de l'Image - UMR_S 1099, Université de Rennes 1, Rennes, France
- INSERM, Paris, France
| | - Charles E Kahn
- Department of Radiology and Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Dagmar Kainmueller
- Max-Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Biomedical Image Analysis and HI Helmholtz Imaging, Berlin, Germany
- Digital Engineering Faculty, University of Potsdam, Potsdam, Germany
| | - Bernhard Kainz
- Department of Computing, Faculty of Engineering, Imperial College London, London, UK
- Department AIBE, Friedrich-Alexander-Universität (FAU), Erlangen-Nürnberg, Germany
| | | | | | | | - Annette Kopp-Schneider
- German Cancer Research Center (DKFZ) Heidelberg, Division of Biostatistics, Heidelberg, Germany
| | - Anna Kreshuk
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Tahsin Kurc
- Department of Biomedical Informatics, Stony Brook University, Health Science Center, Stony Brook, NY, USA
| | | | - Geert Litjens
- Department of Pathology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Amin Madani
- Department of Surgery, University Health Network, Philadelphia, PA, USA
| | - Klaus Maier-Hein
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
- Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Anne L Martel
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Peter Mattson
- Google, 1600 Amphitheatre Pkwy, Mountain View, CA, USA
| | - Erik Meijering
- School of Computer Science and Engineering, University of New South Wales, UNSW Sydney, Kensington, New South Wales, Australia
| | - Bjoern Menze
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, UMC Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Henning Müller
- Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland
- Medical Faculty, University of Geneva, Geneva, Switzerland
| | - Brennan Nichyporuk
- MILA (Québec Artificial Intelligence Institute), Montréal, Quebec, Canada
| | - Felix Nickel
- Department of General, Visceral and Thoracic Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jens Petersen
- German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg, Germany
| | - Nasir Rajpoot
- Tissue Image Analytics Laboratory, Department of Computer Science, University of Warwick, Coventry, UK
| | | | - Julio Saez-Rodriguez
- Institute for Computational Biomedicine, Heidelberg University, Heidelberg, Germany
- Faculty of Medicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Clara I Sánchez
- Informatics Institute, Faculty of Science, University of Amsterdam, Amsterdam, the Netherlands
| | | | - Maarten van Smeden
- Julius Center for Health Sciences and Primary Care, UMC Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Ronald M Summers
- National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Abdel A Taha
- Institute of Information Systems Engineering, TU Wien, Vienna, Austria
| | - Aleksei Tiulpin
- Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Neurocenter Oulu, Oulu University Hospital, Oulu, Finland
| | | | - Ben Van Calster
- Department of Development and Regeneration and EPI-centre, KU Leuven, Leuven, Belgium
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Gaël Varoquaux
- Parietal project team, INRIA Saclay-Île de France, Palaiseau, France
| | - Paul F Jäger
- German Cancer Research Center (DKFZ) Heidelberg, HI Helmholtz Imaging, Heidelberg, Germany.
- German Cancer Research Center (DKFZ) Heidelberg, Interactive Machine Learning Group, Heidelberg, Germany.
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13
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Brady AP, Allen B, Chong J, Kotter E, Kottler N, Mongan J, Oakden-Rayner L, Pinto Dos Santos D, Tang A, Wald C, Slavotinek J. Developing, purchasing, implementing and monitoring AI tools in radiology: Practical considerations. A multi-society statement from the ACR, CAR, ESR, RANZCR & RSNA. J Med Imaging Radiat Oncol 2024; 68:7-26. [PMID: 38259140 DOI: 10.1111/1754-9485.13612] [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: 11/23/2023] [Accepted: 11/23/2023] [Indexed: 01/24/2024]
Abstract
Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools.
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Affiliation(s)
| | - Bibb Allen
- Department of Radiology, Grandview Medical Center, Birmingham, Alabama, USA
- American College of Radiology Data Science Institute, Reston, Virginia, USA
| | - Jaron Chong
- Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Elmar Kotter
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Nina Kottler
- Radiology Partners, El Segundo, California, USA
- Stanford Center for Artificial Intelligence in Medicine & Imaging, Palo Alto, California, USA
| | - John Mongan
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA
| | - Lauren Oakden-Rayner
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, South Australia, Australia
| | - Daniel Pinto Dos Santos
- Department of Radiology, University Hospital of Cologne, Cologne, Germany
- Department of Radiology, University Hospital of Frankfurt, Frankfurt, Germany
| | - An Tang
- Department of Radiology, Radiation Oncology, and Nuclear Medicine, Université de Montréal, Montreal, Quebec, Canada
| | - Christoph Wald
- Department of Radiology, Lahey Hospital & Medical Center, Burlington, Massachusetts, USA
- Tufts University Medical School, Boston, Massachusetts, USA
- Commision On Informatics, and Member, Board of Chancellors, American College of Radiology, Reston, Virginia, USA
| | - John Slavotinek
- South Australia Medical Imaging, Flinders Medical Centre Adelaide, Adelaide, South Australia, Australia
- College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia
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14
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Zhou W, Ye Z, Huang G, Zhang X, Xu M, Liu B, Zhuang B, Tang Z, Wang S, Chen D, Pan Y, Xie X, Wang R, Zhou L. Interpretable artificial intelligence-based app assists inexperienced radiologists in diagnosing biliary atresia from sonographic gallbladder images. BMC Med 2024; 22:29. [PMID: 38267950 PMCID: PMC10809457 DOI: 10.1186/s12916-024-03247-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Accepted: 01/02/2024] [Indexed: 01/26/2024] Open
Abstract
BACKGROUND A previously trained deep learning-based smartphone app provides an artificial intelligence solution to help diagnose biliary atresia from sonographic gallbladder images, but it might be impractical to launch it in real clinical settings. This study aimed to redevelop a new model using original sonographic images and their derived smartphone photos and then test the new model's performance in assisting radiologists with different experiences to detect biliary atresia in real-world mimic settings. METHODS A new model was first trained retrospectively using 3659 original sonographic gallbladder images and their derived 51,226 smartphone photos and tested on 11,410 external validation smartphone photos. Afterward, the new model was tested in 333 prospectively collected sonographic gallbladder videos from 207 infants by 14 inexperienced radiologists (9 juniors and 5 seniors) and 4 experienced pediatric radiologists in real-world mimic settings. Diagnostic performance was expressed as the area under the receiver operating characteristic curve (AUC). RESULTS The new model outperformed the previously published model in diagnosing BA on the external validation set (AUC 0.924 vs 0.908, P = 0.004) with higher consistency (kappa value 0.708 vs 0.609). When tested in real-world mimic settings using 333 sonographic gallbladder videos, the new model performed comparable to experienced pediatric radiologists (average AUC 0.860 vs 0.876) and outperformed junior radiologists (average AUC 0.838 vs 0.773) and senior radiologists (average AUC 0.829 vs 0.749). Furthermore, the new model could aid both junior and senior radiologists to improve their diagnostic performances, with the average AUC increasing from 0.773 to 0.835 for junior radiologists and from 0.749 to 0.805 for senior radiologists. CONCLUSIONS The interpretable app-based model showed robust and satisfactory performance in diagnosing biliary atresia, and it could aid radiologists with limited experiences to improve their diagnostic performances in real-world mimic settings.
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Affiliation(s)
- Wenying Zhou
- Department of Medical Ultrasonics, Institute for Diagnostic and Interventional Ultrasound, The First Affiliated Hospital, Sun Yat-Sen University, No. 58, Zhongshan Er Road, Guangzhou, 510080, People's Republic of China
| | - Zejun Ye
- School of Computer Science and Engineering, Sun Yat-Sen University, No. 132, East Outer Ring Road, Guangzhou, 510006, People's Republic of China
| | - Guangliang Huang
- Department of Medical Ultrasonics, Institute for Diagnostic and Interventional Ultrasound, The First Affiliated Hospital, Sun Yat-Sen University, No. 58, Zhongshan Er Road, Guangzhou, 510080, People's Republic of China
| | - Xiaoer Zhang
- Department of Medical Ultrasonics, Institute for Diagnostic and Interventional Ultrasound, The First Affiliated Hospital, Sun Yat-Sen University, No. 58, Zhongshan Er Road, Guangzhou, 510080, People's Republic of China
| | - Ming Xu
- Department of Medical Ultrasonics, Institute for Diagnostic and Interventional Ultrasound, The First Affiliated Hospital, Sun Yat-Sen University, No. 58, Zhongshan Er Road, Guangzhou, 510080, People's Republic of China
| | - Baoxian Liu
- Department of Medical Ultrasonics, Institute for Diagnostic and Interventional Ultrasound, The First Affiliated Hospital, Sun Yat-Sen University, No. 58, Zhongshan Er Road, Guangzhou, 510080, People's Republic of China
| | - Bowen Zhuang
- Department of Medical Ultrasonics, Institute for Diagnostic and Interventional Ultrasound, The First Affiliated Hospital, Sun Yat-Sen University, No. 58, Zhongshan Er Road, Guangzhou, 510080, People's Republic of China
| | - Zijian Tang
- Department of Ultrasound, Shenzhen Children's Hospital, No. 7019, Yitian Road, Futian District, Shenzhen, 518026, People's Republic of China
| | - Shan Wang
- Department of Ultrasound, Shenzhen Children's Hospital, No. 7019, Yitian Road, Futian District, Shenzhen, 518026, People's Republic of China
| | - Dan Chen
- Department of Ultrasound, Guangdong Women and Children's Hospital, No. 521 Xingnan Avenue, Panyu District, Guangzhou, 511400, People's Republic of China
| | - Yunxiang Pan
- Department of Ultrasound, Guangdong Women and Children's Hospital, No. 521 Xingnan Avenue, Panyu District, Guangzhou, 511400, People's Republic of China
| | - Xiaoyan Xie
- Department of Medical Ultrasonics, Institute for Diagnostic and Interventional Ultrasound, The First Affiliated Hospital, Sun Yat-Sen University, No. 58, Zhongshan Er Road, Guangzhou, 510080, People's Republic of China
| | - Ruixuan Wang
- School of Computer Science and Engineering, Sun Yat-Sen University, No. 132, East Outer Ring Road, Guangzhou, 510006, People's Republic of China
| | - Luyao Zhou
- Department of Medical Ultrasonics, Institute for Diagnostic and Interventional Ultrasound, The First Affiliated Hospital, Sun Yat-Sen University, No. 58, Zhongshan Er Road, Guangzhou, 510080, People's Republic of China.
- Department of Ultrasound, Shenzhen Children's Hospital, No. 7019, Yitian Road, Futian District, Shenzhen, 518026, People's Republic of China.
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Brady AP, Allen B, Chong J, Kotter E, Kottler N, Mongan J, Oakden-Rayner L, Pinto Dos Santos D, Tang A, Wald C, Slavotinek J. Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement From the ACR, CAR, ESR, RANZCR & RSNA. J Am Coll Radiol 2024:S1546-1440(23)01020-7. [PMID: 38276923 DOI: 10.1016/j.jacr.2023.12.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2024]
Abstract
Artificial intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools. KEY POINTS.
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Affiliation(s)
| | - Bibb Allen
- Department of Radiology, Grandview Medical Center, Birmingham, Alabama; American College of Radiology Data Science Institute, Reston, Virginia
| | - Jaron Chong
- Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Elmar Kotter
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Nina Kottler
- Radiology Partners, El Segundo, California; Stanford Center for Artificial Intelligence in Medicine & Imaging, Palo Alto, California
| | - John Mongan
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California
| | - Lauren Oakden-Rayner
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, Australia
| | - Daniel Pinto Dos Santos
- Department of Radiology, University Hospital of Cologne, Cologne, Germany; Department of Radiology, University Hospital of Frankfurt, Frankfurt, Germany
| | - An Tang
- Department of Radiology, Radiation Oncology, and Nuclear Medicine, Université de Montréal, Montréal, Québec, Canada
| | - Christoph Wald
- Department of Radiology, Lahey Hospital & Medical Center, Burlington, Massachusetts; Tufts University Medical School, Boston, Massachusetts; Commision on Informatics, and Member, Board of Chancellors, American College of Radiology, Virginia
| | - John Slavotinek
- South Australia Medical Imaging, Flinders Medical Centre Adelaide, Adelaide, Australia; College of Medicine and Public Health, Flinders University, Adelaide, Australia
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16
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Brady AP, Allen B, Chong J, Kotter E, Kottler N, Mongan J, Oakden-Rayner L, Dos Santos DP, Tang A, Wald C, Slavotinek J. Developing, purchasing, implementing and monitoring AI tools in radiology: practical considerations. A multi-society statement from the ACR, CAR, ESR, RANZCR & RSNA. Insights Imaging 2024; 15:16. [PMID: 38246898 PMCID: PMC10800328 DOI: 10.1186/s13244-023-01541-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2024] Open
Abstract
Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones.This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools.Key points • The incorporation of artificial intelligence (AI) in radiological practice demands increased monitoring of its utility and safety.• Cooperation between developers, clinicians, and regulators will allow all involved to address ethical issues and monitor AI performance.• AI can fulfil its promise to advance patient well-being if all steps from development to integration in healthcare are rigorously evaluated.
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Affiliation(s)
| | - Bibb Allen
- Department of Radiology, Grandview Medical Center, Birmingham, AL, USA
- American College of Radiology Data Science Institute, Reston, VA, USA
| | - Jaron Chong
- Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Elmar Kotter
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Nina Kottler
- Radiology Partners, El Segundo, CA, USA
- Stanford Center for Artificial Intelligence in Medicine & Imaging, Palo Alto, CA, USA
| | - John Mongan
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA
| | - Lauren Oakden-Rayner
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, Australia
| | - Daniel Pinto Dos Santos
- Department of Radiology, University Hospital of Cologne, Cologne, Germany
- Department of Radiology, University Hospital of Frankfurt, Frankfurt, Germany
| | - An Tang
- Department of Radiology, Radiation Oncology, and Nuclear Medicine, Université de Montréal, Montréal, Québec, Canada
| | - Christoph Wald
- Department of Radiology, Lahey Hospital & Medical Center, Burlington, MA, USA
- Tufts University Medical School, Boston, MA, USA
- Commision On Informatics, and Member, Board of Chancellors, American College of Radiology, Virginia, USA
| | - John Slavotinek
- South Australia Medical Imaging, Flinders Medical Centre Adelaide, Adelaide, Australia
- College of Medicine and Public Health, Flinders University, Adelaide, Australia
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17
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Brady AP, Allen B, Chong J, Kotter E, Kottler N, Mongan J, Oakden-Rayner L, dos Santos DP, Tang A, Wald C, Slavotinek J. Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement from the ACR, CAR, ESR, RANZCR and RSNA. Radiol Artif Intell 2024; 6:e230513. [PMID: 38251899 PMCID: PMC10831521 DOI: 10.1148/ryai.230513] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Abstract
Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools. This article is simultaneously published in Insights into Imaging (DOI 10.1186/s13244-023-01541-3), Journal of Medical Imaging and Radiation Oncology (DOI 10.1111/1754-9485.13612), Canadian Association of Radiologists Journal (DOI 10.1177/08465371231222229), Journal of the American College of Radiology (DOI 10.1016/j.jacr.2023.12.005), and Radiology: Artificial Intelligence (DOI 10.1148/ryai.230513). Keywords: Artificial Intelligence, Radiology, Automation, Machine Learning Published under a CC BY 4.0 license. ©The Author(s) 2024. Editor's Note: The RSNA Board of Directors has endorsed this article. It has not undergone review or editing by this journal.
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Affiliation(s)
| | - Bibb Allen
- Department of Radiology, Grandview Medical
Center, Birmingham, AL, USA
- American College of Radiology Data Science
Institute, Reston, VA, USA
| | - Jaron Chong
- Department of Medical Imaging, Schulich
School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Elmar Kotter
- Department of Diagnostic and
Interventional Radiology, Medical Center, Faculty of Medicine, University of
Freiburg, Freiburg, Germany
| | - Nina Kottler
- Radiology Partners, El Segundo, CA,
USA
- Stanford Center for Artificial
Intelligence in Medicine & Imaging, Palo Alto, CA, USA
| | - John Mongan
- Department of Radiology and Biomedical
Imaging, University of California, San Francisco, USA
| | - Lauren Oakden-Rayner
- Australian Institute for Machine Learning,
University of Adelaide, Adelaide, Australia
| | - Daniel Pinto dos Santos
- Department of Radiology, University
Hospital of Cologne, Cologne, Germany
- Department of Radiology, University
Hospital of Frankfurt, Frankfurt, Germany
| | - An Tang
- Department of Radiology, Radiation
Oncology, and Nuclear Medicine, Université de Montréal,
Montréal, Québec, Canada
| | - Christoph Wald
- Department of Radiology, Lahey Hospital
& Medical Center, Burlington, MA, USA
- Tufts University Medical School, Boston,
MA, USA
- Commission On Informatics, and Member,
Board of Chancellors, American College of Radiology, Virginia, USA
| | - John Slavotinek
- South Australia Medical Imaging,
Flinders Medical Centre Adelaide, Adelaide, Australia
- College of Medicine and Public Health,
Flinders University, Adelaide, Australia
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18
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Langius-Wiffen E, de Jong PA, Mohamed Hoesein FA, Dekker L, van den Hoven AF, Nijholt IM, Boomsma MF, Veldhuis WB. Added value of an artificial intelligence algorithm in reducing the number of missed incidental acute pulmonary embolism in routine portal venous phase chest CT. Eur Radiol 2024; 34:367-373. [PMID: 37532902 DOI: 10.1007/s00330-023-10029-z] [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: 03/02/2023] [Revised: 06/06/2023] [Accepted: 06/14/2023] [Indexed: 08/04/2023]
Abstract
OBJECTIVES The purpose of this study was to evaluate the incremental value of artificial intelligence (AI) compared to the diagnostic accuracy of radiologists alone in detecting incidental acute pulmonary embolism (PE) on routine portal venous contrast-enhanced chest computed tomography (CT). METHODS CTs of 3089 consecutive patients referred to the radiology department for a routine contrast-enhanced chest CT between 27-5-2020 and 31-12-2020, were retrospectively analysed by a CE-certified and FDA-approved AI algorithm. The diagnostic performance of the AI was compared to the initial report. To determine the reference standard, discordant findings were independently evaluated by two readers. In case of disagreement, another experienced cardiothoracic radiologist with knowledge of the initial report and the AI output adjudicated. RESULTS The prevalence of acute incidental PE in the reference standard was 2.2% (67 of 3089 patients). In 25 cases, AI detected initially unreported PE. This included three cases concerning central/lobar PE. Sensitivity of the AI algorithm was significantly higher than the outcome of the initial report (respectively 95.5% vs. 62.7%, p < 0.001), whereas specificity was very high for both (respectively 99.6% vs 99.9%, p = 0.012). The AI algorithm only showed a slightly higher amount of false-positive findings (11 vs. 2), resulting in a significantly lower PPV (85.3% vs. 95.5%, p = 0.047). CONCLUSION The AI algorithm showed high diagnostic accuracy in diagnosing incidental PE, detecting an additional 25 cases of initially unreported PE, accounting for 37.3% of all positive cases. CLINICAL RELEVANCE STATEMENT Radiologist support from AI algorithms in daily practice can prevent missed incidental acute PE on routine chest CT, without a high burden of false-positive cases. KEY POINTS • Incidental pulmonary embolism is often missed by radiologists in non-diagnostic scans with suboptimal contrast opacification within the pulmonary trunk. • An artificial intelligence algorithm showed higher sensitivity detecting incidental pulmonary embolism on routine portal venous chest CT compared to the initial report. • Implementation of artificial intelligence support in routine daily practice will reduce the number of missed incidental pulmonary embolism.
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Affiliation(s)
- Eline Langius-Wiffen
- Department of Radiology, Isala Hospital, Dr. Van Heesweg 2, 8025 AB, Zwolle, The Netherlands.
| | - Pim A de Jong
- Department of Radiology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | | | - Lisette Dekker
- Department of Radiology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Andor F van den Hoven
- Department of Radiology, University Medical Centre Utrecht, Utrecht, The Netherlands
- Department of Nuclear Medicine, St. Antonius Hospital, Nieuwegein, The Netherlands
| | - Ingrid M Nijholt
- Department of Radiology, Isala Hospital, Dr. Van Heesweg 2, 8025 AB, Zwolle, The Netherlands
| | - Martijn F Boomsma
- Department of Radiology, Isala Hospital, Dr. Van Heesweg 2, 8025 AB, Zwolle, The Netherlands
- Division of Imaging and Oncology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Wouter B Veldhuis
- Department of Radiology, University Medical Centre Utrecht, Utrecht, The Netherlands
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Kim DY, Oh HW, Suh CH. Reporting Quality of Research Studies on AI Applications in Medical Images According to the CLAIM Guidelines in a Radiology Journal With a Strong Prominence in Asia. Korean J Radiol 2023; 24:1179-1189. [PMID: 38016678 DOI: 10.3348/kjr.2023.1027] [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: 10/20/2023] [Revised: 10/25/2023] [Accepted: 10/26/2023] [Indexed: 11/30/2023] Open
Abstract
OBJECTIVE We aimed to evaluate the reporting quality of research articles that applied deep learning to medical imaging. Using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) guidelines and a journal with prominence in Asia as a sample, we intended to provide an insight into reporting quality in the Asian region and establish a journal-specific audit. MATERIALS AND METHODS A total of 38 articles published in the Korean Journal of Radiology between June 2018 and January 2023 were analyzed. The analysis included calculating the percentage of studies that adhered to each CLAIM item and identifying items that were met by ≤ 50% of the studies. The article review was initially conducted independently by two reviewers, and the consensus results were used for the final analysis. We also compared adherence rates to CLAIM before and after December 2020. RESULTS Of the 42 items in the CLAIM guidelines, 12 items (29%) were satisfied by ≤ 50% of the included articles. None of the studies reported handling missing data (item #13). Only one study respectively presented the use of de-identification methods (#12), intended sample size (#19), robustness or sensitivity analysis (#30), and full study protocol (#41). Of the studies, 35% reported the selection of data subsets (#10), 40% reported registration information (#40), and 50% measured inter and intrarater variability (#18). No significant changes were observed in the rates of adherence to these 12 items before and after December 2020. CONCLUSION The reporting quality of artificial intelligence studies according to CLAIM guidelines, in our study sample, showed room for improvement. We recommend that the authors and reviewers have a solid understanding of the relevant reporting guidelines and ensure that the essential elements are adequately reported when writing and reviewing the manuscripts for publication.
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Affiliation(s)
- Dong Yeong Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | | | - Chong Hyun Suh
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
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20
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Kendrick J, Francis RJ, Hassan GM, Rowshanfarzad P, Ong JS, McCarthy M, Alexander S, Ebert MA. Prognostic utility of RECIP 1.0 with manual and AI-based segmentations in biochemically recurrent prostate cancer from [ 68Ga]Ga-PSMA-11 PET images. Eur J Nucl Med Mol Imaging 2023; 50:4077-4086. [PMID: 37550494 PMCID: PMC10611879 DOI: 10.1007/s00259-023-06382-2] [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: 05/16/2023] [Accepted: 08/02/2023] [Indexed: 08/09/2023]
Abstract
PURPOSE This study aimed to (i) validate the Response Evaluation Criteria in PSMA (RECIP 1.0) criteria in a cohort of biochemically recurrent (BCR) prostate cancer (PCa) patients and (ii) determine if this classification could be performed fully automatically using a trained artificial intelligence (AI) model. METHODS One hundred ninety-nine patients were imaged with [68Ga]Ga-PSMA-11 PET/CT once at the time of biochemical recurrence and then a second time a median of 6.0 months later to assess disease progression. Standard-of-care treatments were administered to patients in the interim. Whole-body tumour volume was quantified semi-automatically (TTVman) in all patients and using a novel AI method (TTVAI) in a subset (n = 74, the remainder were used in the training process of the model). Patients were classified as having progressive disease (RECIP-PD), or non-progressive disease (non RECIP-PD). Association of RECIP classifications with patient overall survival (OS) was assessed using the Kaplan-Meier method with the log rank test and univariate Cox regression analysis with derivation of hazard ratios (HRs). Concordance of manual and AI response classifications was evaluated using the Cohen's kappa statistic. RESULTS Twenty-six patients (26/199 = 13.1%) presented with RECIP-PD according to semi-automated delineations, which was associated with a significantly lower survival probability (log rank p < 0.005) and higher risk of death (HR = 3.78 (1.96-7.28), p < 0.005). Twelve patients (12/74 = 16.2%) presented with RECIP-PD according to AI-based segmentations, which was also associated with a significantly lower survival (log rank p = 0.013) and higher risk of death (HR = 3.75 (1.23-11.47), p = 0.02). Overall, semi-automated and AI-based RECIP classifications were in fair agreement (Cohen's k = 0.31). CONCLUSION RECIP 1.0 was demonstrated to be prognostic in a BCR PCa population and is robust to two different segmentation methods, including a novel AI-based method. RECIP 1.0 can be used to assess disease progression in PCa patients with less advanced disease. This study was registered with the Australian New Zealand Clinical Trials Registry (ACTRN12615000608561) on 11 June 2015.
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Affiliation(s)
- Jake Kendrick
- School of Physics, Mathematics and Computing, The University of Western Australia, Perth, Western Australia, Australia.
- Centre for Advanced Technologies in Cancer Research (CATCR), Perth, Western Australia, Australia.
| | - Roslyn J Francis
- Medical School, The University of Western Australia, Crawley, Western Australia, Australia
- Department of Nuclear Medicine, Sir Charles Gairdner Hospital, Perth, Western Australia, Australia
| | - Ghulam Mubashar Hassan
- School of Physics, Mathematics and Computing, The University of Western Australia, Perth, Western Australia, Australia
| | - Pejman Rowshanfarzad
- School of Physics, Mathematics and Computing, The University of Western Australia, Perth, Western Australia, Australia
- Centre for Advanced Technologies in Cancer Research (CATCR), Perth, Western Australia, Australia
| | - Jeremy Sl Ong
- Department of Nuclear Medicine, Fiona Stanley Hospital, Murdoch, Western Australia, Australia
| | - Michael McCarthy
- Department of Nuclear Medicine, Fiona Stanley Hospital, Murdoch, Western Australia, Australia
| | - Sweeka Alexander
- Department of Nuclear Medicine, Fiona Stanley Hospital, Murdoch, Western Australia, Australia
| | - Martin A Ebert
- School of Physics, Mathematics and Computing, The University of Western Australia, Perth, Western Australia, Australia
- Centre for Advanced Technologies in Cancer Research (CATCR), Perth, Western Australia, Australia
- Department of Radiation Oncology, Sir Charles Gairdner Hospital, Perth, Western Australia, Australia
- 5D Clinics, Claremont, Western Australia, Australia
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21
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Hong GS, Jang M, Kyung S, Cho K, Jeong J, Lee GY, Shin K, Kim KD, Ryu SM, Seo JB, Lee SM, Kim N. Overcoming the Challenges in the Development and Implementation of Artificial Intelligence in Radiology: A Comprehensive Review of Solutions Beyond Supervised Learning. Korean J Radiol 2023; 24:1061-1080. [PMID: 37724586 PMCID: PMC10613849 DOI: 10.3348/kjr.2023.0393] [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: 04/27/2023] [Revised: 07/01/2023] [Accepted: 07/30/2023] [Indexed: 09/21/2023] Open
Abstract
Artificial intelligence (AI) in radiology is a rapidly developing field with several prospective clinical studies demonstrating its benefits in clinical practice. In 2022, the Korean Society of Radiology held a forum to discuss the challenges and drawbacks in AI development and implementation. Various barriers hinder the successful application and widespread adoption of AI in radiology, such as limited annotated data, data privacy and security, data heterogeneity, imbalanced data, model interpretability, overfitting, and integration with clinical workflows. In this review, some of the various possible solutions to these challenges are presented and discussed; these include training with longitudinal and multimodal datasets, dense training with multitask learning and multimodal learning, self-supervised contrastive learning, various image modifications and syntheses using generative models, explainable AI, causal learning, federated learning with large data models, and digital twins.
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Affiliation(s)
- Gil-Sun Hong
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Miso Jang
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Sunggu Kyung
- Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Kyungjin Cho
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
- Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Jiheon Jeong
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Grace Yoojin Lee
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Keewon Shin
- Laboratory for Biosignal Analysis and Perioperative Outcome Research, Biomedical Engineering Center, Asan Institute of Lifesciences, Asan Medical Center, Seoul, Republic of Korea
| | - Ki Duk Kim
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Seung Min Ryu
- Department of Orthopedic Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Joon Beom Seo
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Sang Min Lee
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
| | - Namkug Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
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22
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Lin M, Zhou Q, Lei T, Shang N, Zheng Q, He X, Wang N, Xie H. Deep learning system improved detection efficacy of fetal intracranial malformations in a randomized controlled trial. NPJ Digit Med 2023; 6:191. [PMID: 37833395 PMCID: PMC10575919 DOI: 10.1038/s41746-023-00932-6] [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: 02/27/2023] [Accepted: 09/25/2023] [Indexed: 10/15/2023] Open
Abstract
Congenital malformations of the central nervous system are among the most common major congenital malformations. Deep learning systems have come to the fore in prenatal diagnosis of congenital malformation, but the impact of deep learning-assisted detection of congenital intracranial malformations from fetal neurosonographic images has not been evaluated. Here we report a three-way crossover, randomized control trial (Trial Registration: ChiCTR2100048233) that assesses the efficacy of a deep learning system, the Prenatal Ultrasound Diagnosis Artificial Intelligence Conduct System (PAICS), in assisting fetal intracranial malformation detection. A total of 709 fetal neurosonographic images/videos are read interactively by 36 sonologists of different expertise levels in three reading modes: unassisted mode (without PAICS assistance), concurrent mode (using PAICS at the beginning of the assessment) and second mode (using PAICS after a fully unaided interpretation). Aided by PAICS, the average accuracy of the unassisted mode (73%) is increased by the concurrent mode (80%; P < 0.001) and the second mode (82%; P < 0.001). Correspondingly, the AUC is increased from 0.85 to 0.89 and to 0.90, respectively (P < 0.001 for all). The median read time per data is slightly increased in concurrent mode but substantially prolonged in the second mode, from 6 s to 7 s and to 11 s (P < 0.001 for all). In conclusion, PAICS in both concurrent and second modes has the potential to improve sonologists' performance in detecting fetal intracranial malformations from neurosonographic data. PAICS is more efficient when used concurrently for all readers.
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Affiliation(s)
- Meifang Lin
- Department of Ultrasonic Medicine, Fetal Medical Center, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Qian Zhou
- Department of Medical Statistics, Clinical Trials Unit, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China and Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Ting Lei
- Department of Ultrasonic Medicine, Fetal Medical Center, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Ning Shang
- Department of Ultrasound, Guangdong Women and Children Hospital, Guangzhou, Guangdong, China
| | - Qiao Zheng
- Department of Ultrasonic Medicine, Fetal Medical Center, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Xiaoqin He
- Department of Ultrasound, Women and Children's Hospital affiliated to Xiamen University, Xiamen, Fujian, China
| | - Nan Wang
- Guangzhou Aiyunji Information Technology co., Ltd, Guangzhou, Guangdong, China.
| | - Hongning Xie
- Department of Ultrasonic Medicine, Fetal Medical Center, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China.
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23
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Liu T, Liu G, Jiang T, Li H, Sun C. Curve Similarity Analysis for Reducing the Temperature Uncertainty of Optical Sensor for Oil-Tank Ground Settlement Monitoring. SENSORS (BASEL, SWITZERLAND) 2023; 23:8287. [PMID: 37837117 PMCID: PMC10574854 DOI: 10.3390/s23198287] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 09/29/2023] [Accepted: 10/03/2023] [Indexed: 10/15/2023]
Abstract
A nonuniform temperature field can deteriorate the performance of sensors, especially those working in the field, such as an optical sensor for oil-tank ground settlement (GS) monitoring. In this case, the GS monitoring employs hydraulic-level-based sensors (HLBS), which are uniformly installed along with the oil-tank basement perimeter and are all connected by hydraulic tubes. Then, the cylinder structure of the oil tank itself can create a strong temperature difference between the sensors installed in the sunlit front and those in the shadow. Practically, this sunlight-dependent difference can be over 30 °C, by which the thermal expansion of the measuring liquid inside the connecting hydraulic tubes keeps on driving a movement and, thereby, leads to fluctuations in the final result of the oil-tank GS monitoring system. Now, this system can work well at night when the temperature difference becomes negligible. However, temperature uncertainty is generated in the GS sensors due to the large temperature difference between the sensors in the daytime. In this paper, we measured the temperature where the sensor was located. Then, we compared the results of the GS sensors with their corresponding temperatures and fitted them with two separate curves, respectively. After observing the similarity in the tendency of the two curves, we found that there was a qualitative correlative relationship between the change in temperature and the uncertainty in the sensor results. Then, a curve similarity analysis (CSA) principle based on the minimum mean square error (MMSE) criteria was employed to establish an algorithm, by which the temperature uncertainty in the GS sensors was reduced. A practical test proved that the standard deviation was improved by 73.4% by the algorithm. This work could be an example for reducing the temperature uncertainty from in-field sensors through the CSA method.
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Affiliation(s)
| | | | | | | | - Changsen Sun
- College of Optoelectronic Engineering and Instrumentation Science, Dalian University of Technology, Dalian 116024, China; (T.L.); (G.L.); (T.J.); (H.L.)
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Kim D, Lee JH, Jang MJ, Park J, Hong W, Lee CS, Yang SY, Park CM. The Performance of a Deep Learning-Based Automatic Measurement Model for Measuring the Cardiothoracic Ratio on Chest Radiographs. Bioengineering (Basel) 2023; 10:1077. [PMID: 37760179 PMCID: PMC10525628 DOI: 10.3390/bioengineering10091077] [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: 07/03/2023] [Revised: 08/28/2023] [Accepted: 09/07/2023] [Indexed: 09/29/2023] Open
Abstract
OBJECTIVE Prior studies on models based on deep learning (DL) and measuring the cardiothoracic ratio (CTR) on chest radiographs have lacked rigorous agreement analyses with radiologists or reader tests. We validated the performance of a commercially available DL-based CTR measurement model with various thoracic pathologies, and performed agreement analyses with thoracic radiologists and reader tests using a probabilistic-based reference. MATERIALS AND METHODS This study included 160 posteroanterior view chest radiographs (no lung or pleural abnormalities, pneumothorax, pleural effusion, consolidation, and n = 40 in each category) to externally test a DL-based CTR measurement model. To assess the agreement between the model and experts, intraclass or interclass correlation coefficients (ICCs) were compared between the model and two thoracic radiologists. In the reader tests with a probabilistic-based reference standard (Dawid-Skene consensus), we compared diagnostic measures-including sensitivity and negative predictive value (NPV)-for cardiomegaly between the model and five other radiologists using the non-inferiority test. RESULTS For the 160 chest radiographs, the model measured a median CTR of 0.521 (interquartile range, 0.446-0.59) and a mean CTR of 0.522 ± 0.095. The ICC between the two thoracic radiologists and between the model and two thoracic radiologists was not significantly different (0.972 versus 0.959, p = 0.192), even across various pathologies (all p-values > 0.05). The model showed non-inferior diagnostic performance, including sensitivity (96.3% versus 97.8%) and NPV (95.6% versus 97.4%) (p < 0.001 in both), compared with the radiologists for all 160 chest radiographs. However, it showed inferior sensitivity in chest radiographs with consolidation (95.5% versus 99.9%; p = 0.082) and NPV in chest radiographs with pleural effusion (92.9% versus 94.6%; p = 0.079) and consolidation (94.1% versus 98.7%; p = 0.173). CONCLUSION While the sensitivity and NPV of this model for diagnosing cardiomegaly in chest radiographs with consolidation or pleural effusion were not as high as those of the radiologists, it demonstrated good agreement with the thoracic radiologists in measuring the CTR across various pathologies.
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Affiliation(s)
- Donguk Kim
- Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University, 101, Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea;
| | - Jong Hyuk Lee
- Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea
| | - Myoung-jin Jang
- Medical Research Collaborating Center, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea
| | - Jongsoo Park
- Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea
- Department of Radiology, College of Medicine, Yeungnam University 170, Hyeonchung-ro, Nam-gu, Daegu 42415, Republic of Korea
| | - Wonju Hong
- Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea
- Department of Radiology, Hallym University Sacred Heart Hospital, Anyang-si, Gyeonggi-do 14068, Republic of Korea
| | - Chan Su Lee
- Center for Artificial Intelligence in Medicine and Imaging, HealthHub Co. Ltd., 623, Gangnam-daero, Seocho-gu, Seoul 06524, Republic of Korea
| | - Si Yeong Yang
- Center for Artificial Intelligence in Medicine and Imaging, HealthHub Co. Ltd., 623, Gangnam-daero, Seocho-gu, Seoul 06524, Republic of Korea
| | - Chang Min Park
- Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University, 101, Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea;
- Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, 101, Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea
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Park SH, Sul AR, Ko Y, Jang HY, Lee JG. Radiologist's Guide to Evaluating Publications of Clinical Research on AI: How We Do It. Radiology 2023; 308:e230288. [PMID: 37750772 DOI: 10.1148/radiol.230288] [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: 09/27/2023]
Abstract
Literacy in research studies of artificial intelligence (AI) has become an important skill for radiologists. It is required to make a proper assessment of the validity, reproducibility, and clinical applicability of AI studies. However, AI studies are generally perceived to be more difficult for clinician readers to evaluate than traditional clinical research studies. This special report-as an effective, concise guide for readers-aims to assist clinical radiologists in critically evaluating different types of clinical research articles involving AI. It does not intend to be a comprehensive checklist or methodological summary for complete clinical evaluation of AI or a reporting guideline. Ten key items for readers to check are described, regarding study purpose, function and clinical context of AI, training data, data preprocessing, AI modeling techniques, test data, AI performance, helpfulness and value of AI, interpretability of AI, and code sharing. The important aspects of each item are explained for readers to consider when reading publications on AI clinical research. Evaluating each item can help radiologists assess the validity, reproducibility, and clinical applicability of clinical research articles involving AI.
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Affiliation(s)
- Seong Ho Park
- From the Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea (S.H.P., Y.K., H.Y.J.); Division of Healthcare Research Outcomes Research, National Evidence-based Healthcare Collaborating Agency, Seoul, South Korea (A.R.S.); and Biomedical Engineering Research Center, Asan Institute for Life Sciences, University of Ulsan College of Medicine, Seoul, South Korea (J.G.L.)
| | - Ah-Ram Sul
- From the Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea (S.H.P., Y.K., H.Y.J.); Division of Healthcare Research Outcomes Research, National Evidence-based Healthcare Collaborating Agency, Seoul, South Korea (A.R.S.); and Biomedical Engineering Research Center, Asan Institute for Life Sciences, University of Ulsan College of Medicine, Seoul, South Korea (J.G.L.)
| | - Yousun Ko
- From the Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea (S.H.P., Y.K., H.Y.J.); Division of Healthcare Research Outcomes Research, National Evidence-based Healthcare Collaborating Agency, Seoul, South Korea (A.R.S.); and Biomedical Engineering Research Center, Asan Institute for Life Sciences, University of Ulsan College of Medicine, Seoul, South Korea (J.G.L.)
| | - Hye Young Jang
- From the Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea (S.H.P., Y.K., H.Y.J.); Division of Healthcare Research Outcomes Research, National Evidence-based Healthcare Collaborating Agency, Seoul, South Korea (A.R.S.); and Biomedical Engineering Research Center, Asan Institute for Life Sciences, University of Ulsan College of Medicine, Seoul, South Korea (J.G.L.)
| | - June-Goo Lee
- From the Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea (S.H.P., Y.K., H.Y.J.); Division of Healthcare Research Outcomes Research, National Evidence-based Healthcare Collaborating Agency, Seoul, South Korea (A.R.S.); and Biomedical Engineering Research Center, Asan Institute for Life Sciences, University of Ulsan College of Medicine, Seoul, South Korea (J.G.L.)
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Shin H, Park JE, Jun Y, Eo T, Lee J, Kim JE, Lee DH, Moon HH, Park SI, Kim S, Hwang D, Kim HS. Deep learning referral suggestion and tumour discrimination using explainable artificial intelligence applied to multiparametric MRI. Eur Radiol 2023; 33:5859-5870. [PMID: 37150781 DOI: 10.1007/s00330-023-09710-0] [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: 09/07/2022] [Revised: 01/31/2023] [Accepted: 03/06/2023] [Indexed: 05/09/2023]
Abstract
OBJECTIVES An appropriate and fast clinical referral suggestion is important for intra-axial mass-like lesions (IMLLs) in the emergency setting. We aimed to apply an interpretable deep learning (DL) system to multiparametric MRI to obtain clinical referral suggestion for IMLLs, and to validate it in the setting of nontraumatic emergency neuroradiology. METHODS A DL system was developed in 747 patients with IMLLs ranging 30 diseases who underwent pre- and post-contrast T1-weighted (T1CE), FLAIR, and diffusion-weighted imaging (DWI). A DL system that segments IMLLs, classifies tumourous conditions, and suggests clinical referral among surgery, systematic work-up, medical treatment, and conservative treatment, was developed. The system was validated in an independent cohort of 130 emergency patients, and performance in referral suggestion and tumour discrimination was compared with that of radiologists using receiver operating characteristics curve, precision-recall curve analysis, and confusion matrices. Multiparametric interpretable visualisation of high-relevance regions from layer-wise relevance propagation overlaid on contrast-enhanced T1WI and DWI was analysed. RESULTS The DL system provided correct referral suggestions in 94 of 130 patients (72.3%) and performed comparably to radiologists (accuracy 72.6%, McNemar test; p = .942). For distinguishing tumours from non-tumourous conditions, the DL system (AUC, 0.90 and AUPRC, 0.94) performed similarly to human readers (AUC, 0.81~0.92, and AUPRC, 0.88~0.95). Solid portions of tumours showed a high overlap of relevance, but non-tumours did not (Dice coefficient 0.77 vs. 0.33, p < .001), demonstrating the DL's decision. CONCLUSIONS Our DL system could appropriately triage patients using multiparametric MRI and provide interpretability through multiparametric heatmaps, and may thereby aid neuroradiologic diagnoses in emergency settings. CLINICAL RELEVANCE STATEMENT Our AI triages patients with raw MRI images to clinical referral pathways in brain intra-axial mass-like lesions. We demonstrate that the decision is based on the relative relevance between contrast-enhanced T1-weighted and diffusion-weighted images, providing explainability across multiparametric MRI data. KEY POINTS • A deep learning (DL) system using multiparametric MRI suggested clinical referral to patients with intra-axial mass-like lesions (IMLLs) similar to radiologists (accuracy 72.3% vs. 72.6%). • In the differentiation of tumourous and non-tumourous conditions, the DL system (AUC, 0.90) performed similar with radiologists (AUC, 0.81-0.92). • The DL's decision basis for differentiating tumours from non-tumours can be quantified using multiparametric heatmaps obtained via the layer-wise relevance propagation method.
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Affiliation(s)
- Hyungseob Shin
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea
| | - Ji Eun Park
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Yohan Jun
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Taejoon Eo
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea
| | - Jeongryong Lee
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea
| | - Ji Eun Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Da Hyun Lee
- Department of Radiology, Ajou University School of Medicine, Gyeonggi-Do, Korea
| | - Hye Hyeon Moon
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Sang Ik Park
- Department of Radiology, Chung-Ang University Hospital, Seoul, Korea
| | - Seonok Kim
- Department of Clinical Epidemiology and Biostatistics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Dosik Hwang
- Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea.
- Center for Healthcare Robotics, Korea Institute of Science and Technology, 5, Hwarang-Ro 14-Gil, Seongbuk-Gu, Seoul, 02792, Korea.
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Korea.
- Department of Radiology and Center for Clinical Imaging Data Science (CCIDS), Yonsei University College of Medicine, Seoul, Korea.
| | - Ho Sung Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
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Park SH, Sul AR, Han K, Sung YS. How to Determine If One Diagnostic Method, Such as an Artificial Intelligence Model, is Superior to Another: Beyond Performance Metrics. Korean J Radiol 2023; 24:601-605. [PMID: 37404103 DOI: 10.3348/kjr.2023.0448] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 06/08/2023] [Accepted: 06/09/2023] [Indexed: 07/06/2023] Open
Affiliation(s)
- Seong Ho Park
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
| | - Ah-Ram Sul
- Division of Healthcare Research Outcomes Research, National Evidence-based Healthcare Collaborating Agency, Seoul, Korea
| | - Kyunghwa Han
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Yu Sub Sung
- Clinical Research Center, Asan Medical Center, Seoul, Korea
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Shin HJ, Kim MH, Son NH, Han K, Kim EK, Kim YC, Park YS, Lee EH, Kyong T. Clinical Implication and Prognostic Value of Artificial-Intelligence-Based Results of Chest Radiographs for Assessing Clinical Outcomes of COVID-19 Patients. Diagnostics (Basel) 2023; 13:2090. [PMID: 37370985 DOI: 10.3390/diagnostics13122090] [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: 05/20/2023] [Revised: 06/15/2023] [Accepted: 06/15/2023] [Indexed: 06/29/2023] Open
Abstract
This study aimed to investigate the clinical implications and prognostic value of artificial intelligence (AI)-based results for chest radiographs (CXR) in coronavirus disease 2019 (COVID-19) patients. Patients who were admitted due to COVID-19 from September 2021 to March 2022 were retrospectively included. A commercial AI-based software was used to assess CXR data for consolidation and pleural effusion scores. Clinical data, including laboratory results, were analyzed for possible prognostic factors. Total O2 supply period, the last SpO2 result, and deterioration were evaluated as prognostic indicators of treatment outcome. Generalized linear mixed model and regression tests were used to examine the prognostic value of CXR results. Among a total of 228 patients (mean 59.9 ± 18.8 years old), consolidation scores had a significant association with erythrocyte sedimentation rate and C-reactive protein changes, and initial consolidation scores were associated with the last SpO2 result (estimate -0.018, p = 0.024). All consolidation scores during admission showed significant association with the total O2 supply period and the last SpO2 result. Early changing degree of consolidation score showed an association with deterioration (odds ratio 1.017, 95% confidence interval 1.005-1.03). In conclusion, AI-based CXR results for consolidation have potential prognostic value for predicting treatment outcomes in COVID-19 patients.
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Affiliation(s)
- Hyun Joo Shin
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin-si 16995, Republic of Korea
- Center for Digital Health, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin-si 16995, Republic of Korea
| | - Min Hyung Kim
- Division of Infectious Diseases, Department of Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin-si 16995, Republic of Korea
| | - Nak-Hoon Son
- Department of Statistics, Keimyung University, Daegu 42601, Republic of Korea
| | - Kyunghwa Han
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Eun-Kyung Kim
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin-si 16995, Republic of Korea
| | - Yong Chan Kim
- Division of Infectious Diseases, Department of Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin-si 16995, Republic of Korea
| | - Yoon Soo Park
- Division of Infectious Diseases, Department of Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin-si 16995, Republic of Korea
| | - Eun Hye Lee
- Division of Pulmonology, Allergy and Critical Care Medicine, Department of Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin-si 16995, Republic of Korea
| | - Taeyoung Kyong
- Department of Hospital Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin-si 16995, Republic of Korea
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29
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Langius-Wiffen E, de Jong PA, Hoesein FAM, Dekker L, van den Hoven AF, Nijholt IM, Boomsma MF, Veldhuis WB. Retrospective batch analysis to evaluate the diagnostic accuracy of a clinically deployed AI algorithm for the detection of acute pulmonary embolism on CTPA. Insights Imaging 2023; 14:102. [PMID: 37278961 PMCID: PMC10244304 DOI: 10.1186/s13244-023-01454-1] [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: 01/09/2023] [Accepted: 05/17/2023] [Indexed: 06/07/2023] Open
Abstract
PURPOSE To generate and extend the evidence on the clinical validity of an artificial intelligence (AI) algorithm to detect acute pulmonary embolism (PE) on CT pulmonary angiography (CTPA) of patients suspected of PE and to evaluate the possibility of reducing the risk of missed findings in clinical practice with AI-assisted reporting. METHODS Consecutive CTPA scan data of 3316 patients referred because of suspected PE between 24-2-2018 and 31-12-2020 were retrospectively analysed by a CE-certified and FDA-approved AI algorithm. The output of the AI was compared with the attending radiologists' report. To define the reference standard, discordant findings were independently evaluated by two readers. In case of disagreement, an experienced cardiothoracic radiologist adjudicated. RESULTS According to the reference standard, PE was present in 717 patients (21.6%). PE was missed by the AI in 23 patients, while the attending radiologist missed 60 PE. The AI detected 2 false positives and the attending radiologist 9. The sensitivity for the detection of PE by the AI algorithm was significantly higher compared to the radiology report (96.8% vs. 91.6%, p < 0.001). Specificity of the AI was also significantly higher (99.9% vs. 99.7%, p = 0.035). NPV and PPV of the AI were also significantly higher than the radiology report. CONCLUSION The AI algorithm showed a significantly higher diagnostic accuracy for the detection of PE on CTPA compared to the report of the attending radiologist. This finding indicates that missed positive findings could be prevented with the implementation of AI-assisted reporting in daily clinical practice. CRITICAL RELEVANCE STATEMENT Missed positive findings on CTPA of patients suspected of pulmonary embolism can be prevented with the implementation of AI-assisted care. KEY POINTS The AI algorithm showed excellent diagnostic accuracy detecting PE on CTPA. Accuracy of the AI was significantly higher compared to the attending radiologist. Highest diagnostic accuracy can likely be achieved by radiologists supported by AI. Our results indicate that implementation of AI-assisted reporting could reduce the number of missed positive findings.
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Affiliation(s)
- Eline Langius-Wiffen
- Department of Radiology, Isala Hospital, Dr. van Heesweg 2, 8025 AB, Zwolle, The Netherlands.
| | - Pim A de Jong
- Department of Radiology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | | | - Lisette Dekker
- Department of Radiology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Andor F van den Hoven
- Department of Radiology, University Medical Centre Utrecht, Utrecht, The Netherlands
- Department of Nuclear Medicine, St. Antonius Hospital, Nieuwegein, The Netherlands
| | - Ingrid M Nijholt
- Department of Radiology, Isala Hospital, Dr. van Heesweg 2, 8025 AB, Zwolle, The Netherlands
| | - Martijn F Boomsma
- Department of Radiology, Isala Hospital, Dr. van Heesweg 2, 8025 AB, Zwolle, The Netherlands
- Division of Imaging and Oncology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Wouter B Veldhuis
- Department of Radiology, University Medical Centre Utrecht, Utrecht, The Netherlands
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Herpe G, Feydy A, D'Assignies G. Efficacy versus Effectiveness in Clinical Evaluation of Artificial Intelligence Algorithms for Medical Diagnosis: The Award Goes to Effectiveness. Radiology 2023; 307:e223132. [PMID: 37158721 DOI: 10.1148/radiol.223132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Affiliation(s)
- Guillaume Herpe
- Department of Radiology, University Hospital of Poitiers, 2 rue de la Milétrie, 86021 Poitiers, France
- Dactim Mis, Poitiers, France
- Incepto Medical, Paris, France
| | - Antoine Feydy
- Department of Radiology, Cochin Hospital, Assistance Publique des Hopitaux de Paris, Paris, France
| | - Gaspard D'Assignies
- Incepto Medical, Paris, France
- Department of Radiology, Le Havre Hospital, Le Havre, France
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31
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Chapiro J. Explainable AI for Prostate MRI: Don't Trust, Verify. Radiology 2023; 307:e230574. [PMID: 37039689 PMCID: PMC10323286 DOI: 10.1148/radiol.230574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 03/10/2023] [Accepted: 03/14/2023] [Indexed: 04/12/2023]
Affiliation(s)
- Julius Chapiro
- From the Department of Radiology and Biomedical Imaging, Yale
University School of Medicine, 789 Howard Ave, CB363H, New Haven, CT
06519
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