Reference Citation Analysis: Find an Article, Find a Category, Find a Journal, Find a Scholar

For: Sivanesan U, Wu K, McInnes MDF, Dhindsa K, Salehi F, van der Pol CB. Checklist for Artificial Intelligence in Medical Imaging Reporting Adherence in Peer-Reviewed and Preprint Manuscripts With the Highest Altmetric Attention Scores: A Meta-Research Study. Can Assoc Radiol J 2022;74:334-342. [PMID: 36301600 DOI: 10.1177/08465371221134056] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open

For:	Sivanesan U, Wu K, McInnes MDF, Dhindsa K, Salehi F, van der Pol CB. Checklist for Artificial Intelligence in Medical Imaging Reporting Adherence in Peer-Reviewed and Preprint Manuscripts With the Highest Altmetric Attention Scores: A Meta-Research Study. Can Assoc Radiol J 2022;74:334-342. [PMID: 36301600 DOI: 10.1177/08465371221134056] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open

Number

Cited by Other Article(s)

Tejani AS, Klontzas ME, Gatti AA, Mongan JT, Moy L, Park SH, Kahn CE. Checklist for Artificial Intelligence in Medical Imaging (CLAIM): 2024 Update. Radiol Artif Intell 2024;6:e240300. [PMID: 38809149 DOI: 10.1148/ryai.240300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/30/2024]

Affiliation(s)

Ali S Tejani From the Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (A.S.T.); Department of Radiology, University of Crete School of Medicine, Heraklion, Crete, Greece (M.E.K.); Department of Medical Imaging, University Hospital of Heraklion, Heraklion, Crete, Greece (M.E.K.); Department of Radiology, Stanford University, Stanford, Calif (A.A.G.); Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, Calif (J.T.M.); Department of Radiology, New York University Grossman School of Medicine, New York, NY (L.M.); Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea (S.H.P.); and Department of Radiology and Institute for Biomedical Informatics, University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104-6243 (C.E.K.)
Michail E Klontzas From the Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (A.S.T.); Department of Radiology, University of Crete School of Medicine, Heraklion, Crete, Greece (M.E.K.); Department of Medical Imaging, University Hospital of Heraklion, Heraklion, Crete, Greece (M.E.K.); Department of Radiology, Stanford University, Stanford, Calif (A.A.G.); Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, Calif (J.T.M.); Department of Radiology, New York University Grossman School of Medicine, New York, NY (L.M.); Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea (S.H.P.); and Department of Radiology and Institute for Biomedical Informatics, University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104-6243 (C.E.K.)
Anthony A Gatti From the Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (A.S.T.); Department of Radiology, University of Crete School of Medicine, Heraklion, Crete, Greece (M.E.K.); Department of Medical Imaging, University Hospital of Heraklion, Heraklion, Crete, Greece (M.E.K.); Department of Radiology, Stanford University, Stanford, Calif (A.A.G.); Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, Calif (J.T.M.); Department of Radiology, New York University Grossman School of Medicine, New York, NY (L.M.); Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea (S.H.P.); and Department of Radiology and Institute for Biomedical Informatics, University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104-6243 (C.E.K.)
John T Mongan From the Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (A.S.T.); Department of Radiology, University of Crete School of Medicine, Heraklion, Crete, Greece (M.E.K.); Department of Medical Imaging, University Hospital of Heraklion, Heraklion, Crete, Greece (M.E.K.); Department of Radiology, Stanford University, Stanford, Calif (A.A.G.); Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, Calif (J.T.M.); Department of Radiology, New York University Grossman School of Medicine, New York, NY (L.M.); Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea (S.H.P.); and Department of Radiology and Institute for Biomedical Informatics, University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104-6243 (C.E.K.)
Linda Moy From the Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (A.S.T.); Department of Radiology, University of Crete School of Medicine, Heraklion, Crete, Greece (M.E.K.); Department of Medical Imaging, University Hospital of Heraklion, Heraklion, Crete, Greece (M.E.K.); Department of Radiology, Stanford University, Stanford, Calif (A.A.G.); Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, Calif (J.T.M.); Department of Radiology, New York University Grossman School of Medicine, New York, NY (L.M.); Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea (S.H.P.); and Department of Radiology and Institute for Biomedical Informatics, University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104-6243 (C.E.K.)
Seong Ho Park From the Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (A.S.T.); Department of Radiology, University of Crete School of Medicine, Heraklion, Crete, Greece (M.E.K.); Department of Medical Imaging, University Hospital of Heraklion, Heraklion, Crete, Greece (M.E.K.); Department of Radiology, Stanford University, Stanford, Calif (A.A.G.); Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, Calif (J.T.M.); Department of Radiology, New York University Grossman School of Medicine, New York, NY (L.M.); Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea (S.H.P.); and Department of Radiology and Institute for Biomedical Informatics, University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104-6243 (C.E.K.)
Charles E Kahn From the Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (A.S.T.); Department of Radiology, University of Crete School of Medicine, Heraklion, Crete, Greece (M.E.K.); Department of Medical Imaging, University Hospital of Heraklion, Heraklion, Crete, Greece (M.E.K.); Department of Radiology, Stanford University, Stanford, Calif (A.A.G.); Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, Calif (J.T.M.); Department of Radiology, New York University Grossman School of Medicine, New York, NY (L.M.); Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea (S.H.P.); and Department of Radiology and Institute for Biomedical Informatics, University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104-6243 (C.E.K.)

Collapse

Walston SL, Tatekawa H, Takita H, Miki Y, Ueda D. Evaluating Biases and Quality Issues in Intermodality Image Translation Studies for Neuroradiology: A Systematic Review. AJNR Am J Neuroradiol 2024;45:826-832. [PMID: 38663993 PMCID: PMC11288590 DOI: 10.3174/ajnr.a8211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 01/27/2024] [Indexed: 06/09/2024]

Abstract

BACKGROUND

Intermodality image-to-image translation is an artificial intelligence technique for generating one technique from another.

PURPOSE

This review was designed to systematically identify and quantify biases and quality issues preventing validation and clinical application of artificial intelligence models for intermodality image-to-image translation of brain imaging.

DATA SOURCES

PubMed, Scopus, and IEEE Xplore were searched through August 2, 2023, for artificial intelligence-based image translation models of radiologic brain images.

STUDY SELECTION

This review collected 102 works published between April 2017 and August 2023.

DATA ANALYSIS

Eligible studies were evaluated for quality using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) and for bias using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Medically-focused article adherence was compared with that of engineering-focused articles overall with the Mann-Whitney U test and for each criterion using the Fisher exact test.

DATA SYNTHESIS

Median adherence to the relevant CLAIM criteria was 69% and 38% for PROBAST questions. CLAIM adherence was lower for engineering-focused articles compared with medically-focused articles (65% versus 73%, P < .001). Engineering-focused studies had higher adherence for model description criteria, and medically-focused studies had higher adherence for data set and evaluation descriptions.

LIMITATIONS

Our review is limited by the study design and model heterogeneity.

CONCLUSIONS

Nearly all studies revealed critical issues preventing clinical application, with engineering-focused studies showing higher adherence for the technical model description but significantly lower overall adherence than medically-focused studies. The pursuit of clinical application requires collaboration from both fields to improve reporting.

Collapse

Zhong J, Xing Y, Lu J, Zhang G, Mao S, Chen H, Yin Q, Cen Q, Jiang R, Hu Y, Ding D, Ge X, Zhang H, Yao W. The endorsement of general and artificial intelligence reporting guidelines in radiological journals: a meta-research study. BMC Med Res Methodol 2023;23:292. [PMID: 38093215 PMCID: PMC10717715 DOI: 10.1186/s12874-023-02117-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 12/01/2023] [Indexed: 12/17/2023] Open

Abstract

BACKGROUND

Complete reporting is essential for clinical research. However, the endorsement of reporting guidelines in radiological journals is still unclear. Further, as a field extensively utilizing artificial intelligence (AI), the adoption of both general and AI reporting guidelines would be necessary for enhancing quality and transparency of radiological research. This study aims to investigate the endorsement of general reporting guidelines and those for AI applications in medical imaging in radiological journals, and explore associated journal characteristic variables.

METHODS

This meta-research study screened journals from the Radiology, Nuclear Medicine & Medical Imaging category, Science Citation Index Expanded of the 2022 Journal Citation Reports, and excluded journals not publishing original research, in non-English languages, and instructions for authors unavailable. The endorsement of fifteen general reporting guidelines and ten AI reporting guidelines was rated using a five-level tool: "active strong", "active weak", "passive moderate", "passive weak", and "none". The association between endorsement and journal characteristic variables was evaluated by logistic regression analysis.

RESULTS

We included 117 journals. The top-five endorsed reporting guidelines were CONSORT (Consolidated Standards of Reporting Trials, 58.1%, 68/117), PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses, 54.7%, 64/117), STROBE (STrengthening the Reporting of Observational Studies in Epidemiology, 51.3%, 60/117), STARD (Standards for Reporting of Diagnostic Accuracy, 50.4%, 59/117), and ARRIVE (Animal Research Reporting of In Vivo Experiments, 35.9%, 42/117). The most implemented AI reporting guideline was CLAIM (Checklist for Artificial Intelligence in Medical Imaging, 1.7%, 2/117), while other nine AI reporting guidelines were not mentioned. The Journal Impact Factor quartile and publisher were associated with endorsement of reporting guidelines in radiological journals.

CONCLUSIONS

The general reporting guideline endorsement was suboptimal in radiological journals. The implementation of reporting guidelines for AI applications in medical imaging was extremely low. Their adoption should be strengthened to facilitate quality and transparency of radiological study reporting.

Collapse

Affiliation(s)

Jingyu Zhong Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
Yue Xing Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
Junjie Lu Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, 94305, USA
Guangcheng Zhang Department of Orthopedics, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
Shiqi Mao Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, 200433, China
Haoda Chen Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
Qian Yin Department of Pathology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
Qingqing Cen Department of Dermatology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China
Run Jiang Department of Pharmacovigilance, Shanghai Hansoh BioMedical Co., Ltd., Shanghai, 201203, China
Yangfan Hu Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
Defang Ding Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
Xiang Ge Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
Huan Zhang Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
Weiwu Yao Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China.

Collapse

Kocak B, Bulut E, Bayrak ON, Okumus AA, Altun O, Borekci Arvas Z, Kavukoglu I. NEgatiVE results in Radiomics research (NEVER): A meta-research study of publication bias in leading radiology journals. Eur J Radiol 2023;163:110830. [PMID: 37119709 DOI: 10.1016/j.ejrad.2023.110830] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 04/03/2023] [Accepted: 04/05/2023] [Indexed: 05/01/2023]