Interpretation of checklist for artificial intelligence in medical imaging (CLAIM)_Chinese Journal of Evidence-Based Medicine

Authors：

ZHU Yujia ^1,2 , HAN Hui ¹ , WEI Jianhua ³ ,  SHEN Jiantong ² , HUA Yuting ² , MO Hangfeng ² , CAI Tingting ²

1. The First Affiliated Hospital of Huzhou University, Huzhou 313000, P. R. China;
2. Department of Huzhou University, School of Nursing Science, Huzhou 313000, P. R. China;
3. The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310000, P. R. China;

Corresponding author：

SHEN Jiantong, Email: sjiantong@163.com

Keywords：

Artificial intelligence; Medical imaging; CLAIM; Report quality

DOI：

10.7507/1672-2531.202303152

Video：

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Currently, the medical imaging methods based on artificial intelligence are developing rapidly, and the related literature reports are increasing year by year. However, there is no special reporting standard, and the reporting of the results is not standardized. In order to improve the report quality of this kind of research and help readers and evaluators evaluate the quality of this kind of research more scientifically, a checklist for artificial intelligence in medical imaging (CLAIM) was put forward abroad. This paper introduces the content of CLAIM and explains its items.

Citation： ZHU Yujia, HAN Hui, WEI Jianhua, SHEN Jiantong, HUA Yuting, MO Hangfeng, CAI Tingting. Interpretation of checklist for artificial intelligence in medical imaging (CLAIM). Chinese Journal of Evidence-Based Medicine, 2023, 23(12): 1478-1484. doi: 10.7507/1672-2531.202303152 Copy

1.	吴绯红, 赵煌旋, 杨帆, 等. 医学影像+人工智能的发展、现状与未来. 临床放射学杂志, 2022, 41(4): 764-767.
2.	金征宇. 人工智能医学影像应用: 现实与挑战. 放射学实践, 2018, 33(10): 989-991.
3.	付姣慧, 常晓丹, 沙俏丽, 等. 2011年—2020年深度学习用于医学影像学研究文献分析. 中国介入影像与治疗学, 2022, 19(1): 53-57.
4.	Mongan J, Moy L, Kahn CE. Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiol Artif Intell, 2020, 2(2): e200029.
5.	李子孝, 熊云云, 丁玲玲, 等. 人工智能干预性临床试验报告指南: CONSORT-AI扩展. 中国卒中杂志, 2020, 15(12): 1327-1336.
6.	朱一丹, 李会娟, 武阳丰. 诊断准确性研究报告规范(STARD) 2015介绍与解读. 中国循证医学杂志, 2016, 16(6): 730-735.
7.	王波, 詹思延. 如何撰写高质量的流行病学研究论文第一讲观察性流行病学研究报告规范—STROBE介绍. 中华流行病学杂志, 2006, (6): 547-549.
8.	David M, Kenneth FS, Douglas GA, 等. CONSORT声明: 提高平行随机试验报告质量的修订建议. 中国循证医学杂志, 2005, 5(9): 702-707.
9.	Si L, Zhong J, Huo J, et al. Deep learning in knee imaging: a systematic review utilizing a checklist for artificial intelligence in medical imaging (CLAIM). Eur Radiol, 2022, 32(2): 1353-1361.
10.	Luo W, Phung D, Tran T, et al. Guidelines for developing and reporting machine learning predictive models in biomedical research: a multidisciplinary view. J Med Internet Res, 2016, 18(12): e323.
11.	Handelman GS, Kok HK, Chandra RV, et al. Peering into the black box of artificial intelligence: evaluation metrics of machine learning methods. AJR Am J Roentgenol, 2019, 212(1): 38-43.
12.	Park SH, Han K. Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction. Radiology, 2018, 286(3): 800-809.
13.	Bluemke DA, Moy L, Bredella MA, et al. Assessing radiology research on artificial intelligence: a brief guide for authors, reviewers, and readers-from the Radiology Editorial Board. Radiology, 2020, 294(3): 487-489.
14.	Hazlett HC, Gu H, Munsell BC, et al. Early brain development in infants at high risk for autism spectrum disorder. Nature, 2017, 542(7641): 348-351.
15.	Bossuyt PM, Reitsma JB, Bruns DE, et al. STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. Radiology, 2015, 277(3): 826-832.
16.	Geis JR, Brady AP, Wu CC, et al. Ethics of artificial intelligence in radiology: summary of the Joint European and North American Multisociety Statement. Radiology, 2019, 293(2): 436-440.
17.	Willemink MJ, Koszek WA, Hardell C, et al. Preparing medical imaging data for machine learning. Radiology, 2020, 295(1): 4-15.
18.	Harvey H, Glocker B. A standardised approach for preparing imaging data for machine learning tasks in radiology. In: Ranschaert ER, Morozov S, Algra PR, et al. Artificial Intelligence in medical imaging: opportunities, applications and risks. New York: Springer International, 2019.
19.	Rubin DL, Kahn CE. Common data elements in radiology. Radiology, 2017, 283(3): 837-844.
20.	Kohli M, Alkasab T, Wang K, et al. Bending the artificial intelligence curve for radiology: informatics tools from ACR and RSNA. J Am Coll Radiol, 2019, 16(10): 1464-1470.
21.	Radiological Society of North America, American College of Radiology. RadElement: common data elements. 2020.
22.	Sheehan J, Hirschfeld S, Foster E, et al. Improving the value of clinical research through the use of Common Data Elements. Clin Trials, 2016, 13(6): 671-676.
23.	National Institutes of Health. NIH common data elements (CDE) repository. 2020.
24.	Lakhani P, Kim W, Langlotz CP. Automated extraction of critical test values and communications from unstructured radiology reports: an analysis of 9. 3 million reports from 1990 to 2011. Radiology, 2012, 265(3): 809-818.
25.	Lipton ZC, Berkowitz J, Elkan C. A critical review of recurrent neural networks for sequence learning. 2020.
26.	Eng J. Sample size estimation: how many individuals should be studied. Radiology, 2003, 227(2): 309-313.
27.	International Committee of Medical Journal Editors. Clinical Trials. 2020.
28.	Cohen JF, Korevaar DA, Altman DG, et al. STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration. BMJ Open, 2016, 6(11): e012799.

1. 吴绯红, 赵煌旋, 杨帆, 等. 医学影像+人工智能的发展、现状与未来. 临床放射学杂志, 2022, 41(4): 764-767.
2. 金征宇. 人工智能医学影像应用: 现实与挑战. 放射学实践, 2018, 33(10): 989-991.
3. 付姣慧, 常晓丹, 沙俏丽, 等. 2011年—2020年深度学习用于医学影像学研究文献分析. 中国介入影像与治疗学, 2022, 19(1): 53-57.
4. Mongan J, Moy L, Kahn CE. Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiol Artif Intell, 2020, 2(2): e200029.
5. 李子孝, 熊云云, 丁玲玲, 等. 人工智能干预性临床试验报告指南: CONSORT-AI扩展. 中国卒中杂志, 2020, 15(12): 1327-1336.
6. 朱一丹, 李会娟, 武阳丰. 诊断准确性研究报告规范(STARD) 2015介绍与解读. 中国循证医学杂志, 2016, 16(6): 730-735.
7. 王波, 詹思延. 如何撰写高质量的流行病学研究论文第一讲观察性流行病学研究报告规范—STROBE介绍. 中华流行病学杂志, 2006, (6): 547-549.
8. David M, Kenneth FS, Douglas GA, 等. CONSORT声明: 提高平行随机试验报告质量的修订建议. 中国循证医学杂志, 2005, 5(9): 702-707.
9. Si L, Zhong J, Huo J, et al. Deep learning in knee imaging: a systematic review utilizing a checklist for artificial intelligence in medical imaging (CLAIM). Eur Radiol, 2022, 32(2): 1353-1361.
10. Luo W, Phung D, Tran T, et al. Guidelines for developing and reporting machine learning predictive models in biomedical research: a multidisciplinary view. J Med Internet Res, 2016, 18(12): e323.
11. Handelman GS, Kok HK, Chandra RV, et al. Peering into the black box of artificial intelligence: evaluation metrics of machine learning methods. AJR Am J Roentgenol, 2019, 212(1): 38-43.
12. Park SH, Han K. Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction. Radiology, 2018, 286(3): 800-809.
13. Bluemke DA, Moy L, Bredella MA, et al. Assessing radiology research on artificial intelligence: a brief guide for authors, reviewers, and readers-from the Radiology Editorial Board. Radiology, 2020, 294(3): 487-489.
14. Hazlett HC, Gu H, Munsell BC, et al. Early brain development in infants at high risk for autism spectrum disorder. Nature, 2017, 542(7641): 348-351.
15. Bossuyt PM, Reitsma JB, Bruns DE, et al. STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. Radiology, 2015, 277(3): 826-832.
16. Geis JR, Brady AP, Wu CC, et al. Ethics of artificial intelligence in radiology: summary of the Joint European and North American Multisociety Statement. Radiology, 2019, 293(2): 436-440.
17. Willemink MJ, Koszek WA, Hardell C, et al. Preparing medical imaging data for machine learning. Radiology, 2020, 295(1): 4-15.
18. Harvey H, Glocker B. A standardised approach for preparing imaging data for machine learning tasks in radiology. In: Ranschaert ER, Morozov S, Algra PR, et al. Artificial Intelligence in medical imaging: opportunities, applications and risks. New York: Springer International, 2019.
19. Rubin DL, Kahn CE. Common data elements in radiology. Radiology, 2017, 283(3): 837-844.
20. Kohli M, Alkasab T, Wang K, et al. Bending the artificial intelligence curve for radiology: informatics tools from ACR and RSNA. J Am Coll Radiol, 2019, 16(10): 1464-1470.
21. Radiological Society of North America, American College of Radiology. RadElement: common data elements. 2020.
22. Sheehan J, Hirschfeld S, Foster E, et al. Improving the value of clinical research through the use of Common Data Elements. Clin Trials, 2016, 13(6): 671-676.
23. National Institutes of Health. NIH common data elements (CDE) repository. 2020.
24. Lakhani P, Kim W, Langlotz CP. Automated extraction of critical test values and communications from unstructured radiology reports: an analysis of 9. 3 million reports from 1990 to 2011. Radiology, 2012, 265(3): 809-818.
25. Lipton ZC, Berkowitz J, Elkan C. A critical review of recurrent neural networks for sequence learning. 2020.
26. Eng J. Sample size estimation: how many individuals should be studied. Radiology, 2003, 227(2): 309-313.
27. International Committee of Medical Journal Editors. Clinical Trials. 2020.
28. Cohen JF, Korevaar DA, Altman DG, et al. STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration. BMJ Open, 2016, 6(11): e012799.

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