Evaluating Biases and Quality Issues in Intermodality Image Translation Studies for Neuroradiology: A Systematic Review

References

1. 1.↵
   1. Wolterink JM,
   2. Mukhopadhyay A,
   3. Leiner T, et al

   . Generative adversarial networks: a primer for radiologists. Radiographics 2021;41:840–57 doi:10.1148/rg.2021200151 pmid:33891522

   CrossRefPubMed
2. 2.↵
   1. Ueda D,
   2. Shimazaki A,
   3. Miki Y

   . Technical and clinical overview of deep learning in radiology. Jpn J Radiol 2019;37:15–33 doi:10.1007/s11604-018-0795-3 pmid:30506448

   CrossRefPubMed
3. 3.↵
   1. Matheny ME,
   2. Whicher D,
   3. Thadaney Israni S

   . Artificial Intelligence in Health Care: A Report From the National Academy of Medicine. JAMA 2020;323:509–10 doi:10.1001/jama.2019.21579 pmid:31845963

   CrossRefPubMed
4. 4.↵
   1. Laino ME,
   2. Cancian P,
   3. Politi LS, et al

   . Generative adversarial networks in brain imaging: a narrative review. J Imaging 2022;8:83 doi:10.3390/jimaging8040083 pmid:35448210

   CrossRefPubMed
5. 5.↵
   1. Seymour ZA,
   2. Fogh SE,
   3. Westcott SK, et al

   . Interval from imaging to treatment delivery in the radiation surgery age: how long is too long? Int J Radiat Oncol Biol Phys 2015;93:126–32 doi:10.1016/j.ijrobp.2015.05.001 pmid:26279030

   CrossRefPubMed
6. 6.↵
   1. Sorin V,
   2. Barash Y,
   3. Konen E, et al

   . Creating artificial images for radiology applications using generative adversarial networks (GANs): a systematic review. Acad Radiol 2020;27:1175–85 doi:10.1016/j.acra.2019.12.024 pmid:32035758

   CrossRefPubMed
7. 7.↵
   1. Feng E,
   2. Qin P,
   3. Chai R, et al

   . MRI generated from CT for acute ischemic stroke combining radiomics and generative adversarial networks. IEEE J Biomed Health Inform 2022;26:6047–57 doi:10.1109/JBHI.2022.3205961 pmid:36094997

   CrossRefPubMed
8. 8.↵
   1. Garzon G,
   2. Gomez S,
   3. Mantilla D, et al

   . A deep CT to MRI unpaired translation that preserve ischemic stroke lesions. Conf Proc IEEE Eng Med Biol Soc 2022;2022:2708–11 doi:10.1109/EMBC48229.2022.9871154 pmid:36086325

   CrossRefPubMed
9. 9.↵
   1. Gutierrez A,
   2. Tuladhar A,
   3. Wilms M, et al

   . Lesion-preserving unpaired image-to-image translation between MRI and CT from ischemic stroke patients. Int J Comput Assist Radiol Surg 2023;18:827–36 doi:10.1007/s11548-022-02828-4 pmid:36607506

   CrossRefPubMed
10. 10.↵
    1. Rubin J,
    2. Abulnaga SM

    . CT-to-MR conditional generative adversarial networks for ischemic stroke lesion segmentation. In: 2019 IEEE International Conference on Healthcare Informatics (ICHI), June 10–13, 2019. Xi’an, China doi:10.1109/ICHI.2019.8904574

    CrossRef
11. 11.↵
    1. Bontempi D,
    2. Nuernberg L,
    3. Krishnaswamy D, et al

    . Transparent and Reproducible AI-based Medical Imaging Pipelines Using the Cloud. Research Square. https://doi.org/10.21203/rs.3.rs-3142996/v1. Accessed October 1, 2023
12. 12.↵
    1. Kim DW,
    2. Jang HY,
    3. Kim KW, et al

    . Design characteristics of studies reporting the performance of artificial intelligence algorithms for diagnostic analysis of medical images: results from recently published papers. Korean J Radiol 2019;20:405–10 doi:10.3348/kjr.2019.0025 pmid:30799571

    CrossRefPubMed
13. 13.↵
    1. England JR,
    2. Cheng PM

    . Artificial intelligence for medical image analysis: a guide for authors and reviewers. AJR Am J Roentgenol 2019;212:513–19 doi:10.2214/AJR.18.20490 pmid:30557049

    CrossRefPubMed
14. 14.↵
    1. Sivanesan U,
    2. Wu K,
    3. McInnes MD, et al

    . 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 2023;74:334–42 doi:10.1177/08465371221134056 pmid:36301600

    CrossRefPubMed
15. 15.↵
    1. Mongan J,
    2. Moy L,
    3. Kahn CE Jr.

    . Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiol Artif Intell 2020;2:e200029 doi:10.1148/ryai.2020200029 pmid:33937821

    CrossRefPubMed
16. 16.↵
    1. Wolff RF,
    2. Moons KG,
    3. Riley RD, et al

    ; PROBAST Group. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med 2019;170:51–58 doi:10.7326/M18-1376 pmid:30596875

    CrossRefPubMed
17. 17.↵
    1. Klontzas ME,
    2. Gatti AA,
    3. Tejani AS, et al

    . AI reporting guidelines: how to select the best one for your research. RadiolArtif Intell 2023;5:e230055 doi:10.1148/ryai.230055 pmid:37293341

    CrossRefPubMed
18. 18.↵
    1. Ueda D,
    2. Kakinuma T,
    3. Fujita S, et al

    . Fairness of artificial intelligence in healthcare: review and recommendations. Jpn J Radiol 2024;42:3–15 doi:10.1007/s11604-023-01474-3 pmid:37540463

    CrossRefPubMed
19. 19.↵
    1. Page MJ,
    2. Moher D,
    3. Bossuyt PM, et al

    . PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. BMJ 2021;372:n160 doi:10.1136/bmj.n160 pmid:33781993

    FREE Full Text
20. 20.↵
    1. Kuo RY,
    2. Harrison C,
    3. Curran T-A, et al

    . Artificial intelligence in fracture detection: a systematic review and meta-analysis. Radiology 2022;304:50–62 doi:10.1148/radiol.211785 pmid:35348381

    CrossRefPubMed
21. 21.↵
    1. Nagendran M,
    2. Chen Y,
    3. Lovejoy CA, et al

    . Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. BMJ 2020;368:m689 doi:10.1136/bmj.m689 pmid:32213531

    Abstract/FREE Full Text
22. 22.↵
    1. Patterson D,
    2. Snyder L,
    3. Ullman J

    . Evaluating computer scientists and engineers for promotion and tenure. Computer Research Association 1999 Aug 1. [Epub ahead of print]
23. 23.↵
    1. Vardi MY

    . Conferences vs. journals in computing research. Communications of ACM 2009;52:5 doi:10.1145/1506409.1506410

    CrossRef
24. 24.↵
    1. Fortnow L

    . Viewpoint: Time for computer science to grow up. Communications of ACM 2009;52:33–35 doi:10.1145/1536616.1536631

    CrossRef
25. 25.↵
    1. Choi H,
    2. Lee DS

    , Alzheimer’s Disease Neuroimaging Initiative. Generation of structural MR images from amyloid PET: application to MR-less quantification. J Nucl Med 2018;59:1111–17 doi:10.2967/jnumed.117.199414 pmid:29217736

    Abstract/FREE Full Text
26. 26.↵
    1. Pan Y,
    2. Liu M,
    3. Lian C, et al

    . Synthesizing missing PET from MRI with cycle-consistent generative adversarial networks for Alzheimer’s disease diagnosis. Med Image Comput Comput Assist Interv 2018;11072:455–63 doi:10.1007/978-3-030-00931-1_52 pmid:34355223

    CrossRefPubMed
27. 27.↵
    1. Takita H,
    2. Matsumoto T,
    3. Tatekawa H, et al

    . AI-based virtual synthesis of methionine PET from contrast-enhanced MRI: development and external validation study. Radiology 2023;308:e223016 doi:10.1148/radiol.223016 pmid:37526545

    CrossRefPubMed
28. 28.↵
    1. Park SH,
    2. Han K

    . Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction. Radiology 2018;286:800–09 doi:10.1148/radiol.2017171920 pmid:29309734

    CrossRefPubMed
29. 29.↵
    1. Collins GS,
    2. Dhiman P,
    3. Andaur Navarro CL, et al

    . Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open 2021;11:e048008 doi:10.1136/bmjopen-2020-048008 pmid:34244270

    Abstract/FREE Full Text
30. 30.↵
    1. Bossuyt PM,
    2. Reitsma JB,
    3. Bruns DE, et al

    ; STARD Group. STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. BMJ 2015;351:h5527 doi:10.1136/bmj.h5527 pmid:26511519

    FREE Full Text
31. 31.↵
    1. Moons KG,
    2. Altman DG,
    3. Reitsma JB, et al

    ; Transparent Reporting of a Multivariate Prediction Model for Individual Prognosis or Development Initiative. New guideline for the reporting of studies developing, validating, or updating a multivariable clinical prediction model: the tripod statement. Adv Anat Pathol 2015;22:303–05 doi:10.1097/PAP.0000000000000072 pmid:26262512

    CrossRefPubMed
32. 32.↵
    1. Willemink MJ,
    2. Koszek WA,
    3. Hardell C, et al

    . Preparing medical imaging data for machine learning. Radiology 2020;295:4–15 doi:10.1148/radiol.2020192224 pmid:32068507

    CrossRefPubMed
33. 33.↵
    1. Wang T,
    2. Lei Y,
    3. Fu Y, et al

    . A review on medical imaging synthesis using deep learning and its clinical applications. J Appl Clin Med Phys 2021;22:11–36 doi:10.1002/acm2.13121 pmid:33305538

    CrossRefPubMed
34. 34.↵
    1. Yonezawa H,
    2. Ueda D,
    3. Yamamoto A, et al

    . Maskless 2-dimensional digital subtraction angiography generation model for abdominal vasculature using deep learning. J Vasc Interv Radiol 2022;33:845–51.e8 doi:10.1016/j.jvir.2022.03.010 pmid:35311665

    CrossRefPubMed
35. 35.↵
    1. Tejani AS,
    2. Klontzas ME,
    3. Gatti AA, et al

    . Updating the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) for reporting AI research. Nat Mach Intell 2023;5:950–51 doi:10.1038/s42256-023-00717-2

    CrossRef
36. 36.↵
    1. Sounderajah V,
    2. Ashrafian H,
    3. Golub RM, et al

    ; STARD-AI Steering Committee. Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol. BMJ Open 2021;11:e047709 doi:10.1136/bmjopen-2020-047709 pmid:34183345

    Abstract/FREE Full Text