Implementation of artificial intelligence in thoracic imaging-a what, how, and why guide from the European Society of Thoracic Imaging (ESTI).
Artificial intelligence
Diagnosis, Computer assisted
Thorax
Journal
European radiology
ISSN: 1432-1084
Titre abrégé: Eur Radiol
Pays: Germany
ID NLM: 9114774
Informations de publication
Date de publication:
Jul 2023
Jul 2023
Historique:
received:
03
07
2022
accepted:
27
12
2022
revised:
29
11
2022
medline:
26
6
2023
pubmed:
3
2
2023
entrez:
2
2
2023
Statut:
ppublish
Résumé
This statement from the European Society of Thoracic imaging (ESTI) explains and summarises the essentials for understanding and implementing Artificial intelligence (AI) in clinical practice in thoracic radiology departments. This document discusses the current AI scientific evidence in thoracic imaging, its potential clinical utility, implementation and costs, training requirements and validation, its' effect on the training of new radiologists, post-implementation issues, and medico-legal and ethical issues. All these issues have to be addressed and overcome, for AI to become implemented clinically in thoracic radiology. KEY POINTS: • Assessing the datasets used for training and validation of the AI system is essential. • A departmental strategy and business plan which includes continuing quality assurance of AI system and a sustainable financial plan is important for successful implementation. • Awareness of the negative effect on training of new radiologists is vital.
Identifiants
pubmed: 36729173
doi: 10.1007/s00330-023-09409-2
pii: 10.1007/s00330-023-09409-2
pmc: PMC9892666
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
5077-5086Informations de copyright
© 2023. The Author(s).
Références
Chartrand G, Cheng PM, Vorontsov E et al (2017) Deep learning: a primer for radiologists. Radiographics 37:2113–2131. https://doi.org/10.1148/rg.2017170077
doi: 10.1148/rg.2017170077
pubmed: 29131760
Hamet P, Tremblay J (2017) Artificial intelligence in medicine. Metabolism 69:S36–S40. https://doi.org/10.1016/j.metabol.2017.01.011
doi: 10.1016/j.metabol.2017.01.011
Erickson BJ, Korfiatis P, Akkus Z, Kline TL (2017) Machine learning for medical imaging. Radiographics 37:505–515. https://doi.org/10.1148/rg.2017160130
doi: 10.1148/rg.2017160130
pubmed: 28212054
Chassagnon G, Vakalopolou M, Paragios N, Revel M-P (2020) Deep learning: definition and perspectives for thoracic imaging. Eur Radiol 30:2021–2030. https://doi.org/10.1007/s00330-019-06564-3
doi: 10.1007/s00330-019-06564-3
pubmed: 31811431
(2022) NHS AI dictionary. https://nhsx.github.io/ai-dictionary
van Leeuwen KG, Schalekamp S, Rutten MJCM et al (2021) Artificial intelligence in radiology: 100 commercially available products and their scientific evidence. Eur Radiol 31:3797–3804. https://doi.org/10.1007/s00330-021-07892-z
doi: 10.1007/s00330-021-07892-z
pubmed: 33856519
pmcid: 8128724
van Leeuwen KG, de Rooij M, Schalekamp S et al (2021) How does artificial intelligence in radiology improve efficiency and health outcomes? Pediatr Radiol. https://doi.org/10.1007/s00247-021-05114-8
doi: 10.1007/s00247-021-05114-8
pubmed: 34471961
pmcid: 8409695
Roberts M, Driggs D, Thorpe M et al (2021) Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nat Mach Intell 3:199–217. https://doi.org/10.1038/s42256-021-00307-0
doi: 10.1038/s42256-021-00307-0
López-Cabrera JD, Orozco-Morales R, Portal-Díaz JA et al (2021) Current limitations to identify covid-19 using artificial intelligence with chest x-ray imaging (part ii). The shortcut learning problem. Health Technol (Berl) 11:1331–1345. https://doi.org/10.1007/s12553-021-00609-8
doi: 10.1007/s12553-021-00609-8
pubmed: 34660166
Laino ME, Ammirabile A, Posa A et al (2021) The applications of artificial intelligence in chest imaging of COVID-19 patients: a literature review. Diagnostics 11:1–30. https://doi.org/10.3390/diagnostics11081317
doi: 10.3390/diagnostics11081317
Luo W, Phung D, Tran T et al (2016) Guidelines for developing and reporting machine learning predictive models in biomedical research: a multidisciplinary view. J Med Internet Res 18:1–10. https://doi.org/10.2196/jmir.5870
doi: 10.2196/jmir.5870
Handelman GS, Kok HK, Chandra RV et al (2019) Peering into the black box of artificial intelligence: evaluation metrics of machine learning methods. AJR Am J Roentgenol 212:38–43. https://doi.org/10.2214/AJR.18.20224
doi: 10.2214/AJR.18.20224
pubmed: 30332290
Park SH, Han K (2018) Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction. Radiology 286:800–809. https://doi.org/10.1148/radiol.2017171920
Mongan J, Moy L, Kahn CE (2020) Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiol Artif Intell 2:e200029. https://doi.org/10.1148/ryai.2020200029
Bluemke DA, Moy L, Bredella MA et al (2020) Assessing radiology research on artificial intelligence: a brief guide for authors, reviewers, and readers-from the Radiology Editorial Board. Radiology 294:487–489. https://doi.org/10.1148/radiol.2019192515
doi: 10.1148/radiol.2019192515
pubmed: 31891322
Sharma P, Suehling M, Flohr T, Comaniciu D (2020) Artificial intelligence in diagnostic imaging. J Thorac Imaging 35:S11–S16. https://doi.org/10.1097/RTI.0000000000000499
doi: 10.1097/RTI.0000000000000499
pubmed: 32205816
Christe A, Peters AA, Drakopoulos D et al (2019) Computer-aided diagnosis of pulmonary fibrosis using deep learning and CT images. Invest Radiol 54:627–632. https://doi.org/10.1097/RLI.0000000000000574
doi: 10.1097/RLI.0000000000000574
pubmed: 31483764
pmcid: 6738634
Christe A, Leidolt L, Huber A et al (2013) Lung cancer screening with CT: evaluation of radiologists anddifferent computer assisted detection software (CAD) as first andsecond readers for lung nodule detection at different dose levels. Eur J Radiol 82:e873–e878. https://doi.org/10.1016/j.ejrad.2013.08.026
doi: 10.1016/j.ejrad.2013.08.026
pubmed: 24074648
Bolte H, Jahnke T, Schäfer FKW et al (2007) Interobserver-variability of lung nodule volumetry considering different segmentation algorithms and observer training levels. Eur J Radiol 64:285–295. https://doi.org/10.1016/j.ejrad.2007.02.031
doi: 10.1016/j.ejrad.2007.02.031
pubmed: 17433595
Martini K, Blüthgen C, Eberhard M et al (2021) Impact of vessel suppressed-CT on diagnostic accuracy in detection of pulmonary metastasis and reading time. Acad Radiol 28:988–994. https://doi.org/10.1016/j.acra.2020.01.014
doi: 10.1016/j.acra.2020.01.014
pubmed: 32037256
Kauczor HU, Baird AM, Blum TG et al (2020) ESR/ERS statement paper on lung cancer screening. Eur Respir J 55:1–18. https://doi.org/10.1183/13993003.00506-2019
doi: 10.1183/13993003.00506-2019
van Winkel SL, Rodríguez-Ruiz A, Appelman L et al (2021) Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study. Eur Radiol 31:8682–8691. https://doi.org/10.1007/s00330-021-07992-w
doi: 10.1007/s00330-021-07992-w
pubmed: 33948701
pmcid: 8523448
Svoboda E (2020) Artificial intelligence is improving the detection of lung cancer. Nature 587:S20–S22. https://doi.org/10.1038/d41586-020-03157-9
doi: 10.1038/d41586-020-03157-9
pubmed: 33208974
NICE (2022) Artificial intelligence for analysing chest X-ray images. Medtech Innov Brief
Goldberg-Stein S, Chernyak V (2019) Adding value in radiology reporting. J Am Coll Radiol 16:1292–1298. https://doi.org/10.1016/j.jacr.2019.05.042
doi: 10.1016/j.jacr.2019.05.042
pubmed: 31492407
Mieloszyk RJ, Rosenbaum JI, Bhargava P, Hall CS (2017) Predictive modeling to identify scheduled radiology appointments resulting in non-attendance in a hospital setting. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, pp 2618–2621
Fayad LM, Parekh VS, de Castro LR et al (2021) A deep learning system for synthetic knee magnetic resonance imaging. Invest Radiol 56:357–368. https://doi.org/10.1097/RLI.0000000000000751
doi: 10.1097/RLI.0000000000000751
pubmed: 33350717
pmcid: 8087629
Arbabshirani MR, Fornwalt BK, Mongelluzzo GJ, et al (2018) Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration. NPJ Digit Med 1:9. https://doi.org/10.1038/s41746-017-0015-z
Weikert T, Nesic I, Cyriac J, et al (2020) Towards automated generation of curated datasets in radiology: application of natural language processing to unstructured reports exemplified on CT for pulmonary embolism. Eur J Radiol 125:108862. https://doi.org/10.1016/j.ejrad.2020.108862
Greenhalgh T, Wherton J, Papoutsi C, et al (2017) Beyond adoption: a new framework for theorizing and evaluating nonadoption, abandonment, and challenges to the scale-up, spread, and sustainability of health and care technologies. J Med Internet Res 19:. https://doi.org/10.2196/jmir.8775
Strohm L, Hehakaya C, Ranschaert ER et al (2020) Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors. Eur Radiol 30:5525–5532. https://doi.org/10.1007/s00330-020-06946-y
doi: 10.1007/s00330-020-06946-y
pubmed: 32458173
pmcid: 7476917
Coppola F, Faggioni L, Regge D et al (2021) Artificial intelligence: radiologists’ expectations and opinions gleaned from a nationwide online survey. Radiol Med 126:63–71. https://doi.org/10.1007/s11547-020-01205-y
doi: 10.1007/s11547-020-01205-y
pubmed: 32350797
Tamm EP, Zelitt D, Dinwiddie S (2000) Implementation and day-to-day usage of a client-server-based radiology information system. J Digit Imaging 13:213–214. https://doi.org/10.1007/bf03167668
doi: 10.1007/bf03167668
pubmed: 10847406
pmcid: 3453235
Lu Z, xia, Qian P, Bi D, et al (2021) Application of AI and IoT in clinical medicine: summary and challenges. Curr Med Sci 41:1134–1150. https://doi.org/10.1007/s11596-021-2486-z
doi: 10.1007/s11596-021-2486-z
pubmed: 34939144
pmcid: 8693843
Silva JM, Pinho E, Monteiro E et al (2018) Controlled searching in reversibly de-identified medical imaging archives. J Biomed Inform 77:81–90. https://doi.org/10.1016/j.jbi.2017.12.002
doi: 10.1016/j.jbi.2017.12.002
pubmed: 29224856
Kelly CJ, Karthikesalingam A, Suleyman M et al (2019) Key challenges for delivering clinical impact with artificial intelligence. BMC Med 17:1–9. https://doi.org/10.1186/s12916-019-1426-2
doi: 10.1186/s12916-019-1426-2
Rodríguez-Ruiz A, Krupinski E, Mordang JJ et al (2019) Detection of breast cancer with mammography: effect of an artificial intelligence support system. Radiology 290:305–314. https://doi.org/10.1148/radiol.2018181371
doi: 10.1148/radiol.2018181371
pubmed: 30457482
Matsumoto S, Ohno Y, Aoki T et al (2013) Computer-aided detection of lung nodules on multidetector CT in concurrent-reader and second-reader modes: a comparative study. Eur J Radiol 82:1332–1337. https://doi.org/10.1016/j.ejrad.2013.02.005
doi: 10.1016/j.ejrad.2013.02.005
pubmed: 23480965
Beyer F, Zierott L, Fallenberg EM et al (2007) Comparison of sensitivity and reading time for the use of computer-aided detection (CAD) of pulmonary nodules at MDCT as concurrent or second reader. Eur Radiol 17:2941–2947. https://doi.org/10.1007/s00330-007-0667-1
doi: 10.1007/s00330-007-0667-1
pubmed: 17929026
Nair A, Screaton NJ, Holemans JA et al (2018) The impact of trained radiographers as concurrent readers on performance and reading time of experienced radiologists in the UK Lung Cancer Screening (UKLS) trial. Eur Radiol 28:226–234. https://doi.org/10.1007/s00330-017-4903-z
doi: 10.1007/s00330-017-4903-z
pubmed: 28643093
Wittenberg R, Peters JF, van den Berk IAH et al (2013) Computed tomography pulmonary angiography in acute pulmonary embolism. J Thorac Imaging 28:315–321. https://doi.org/10.1097/RTI.0b013e3182870b97
doi: 10.1097/RTI.0b013e3182870b97
pubmed: 23486230
Rubin GD (2015) Lung nodule and cancer detection in computed tomography screening. J Thorac Imaging 30:130–138. https://doi.org/10.1097/RTI.0000000000000140
doi: 10.1097/RTI.0000000000000140
pubmed: 25658477
pmcid: 4654704
Hsu H-H, Ko K-H, Chou Y-C et al (2021) Performance and reading time of lung nodule identification on multidetector CT with or without an artificial intelligence-powered computer-aided detection system. Clin Radiol 76:626.e23-626.e32. https://doi.org/10.1016/j.crad.2021.04.006
doi: 10.1016/j.crad.2021.04.006
pubmed: 34023068
Müller FC, Raaschou H, Akhtar N et al (2021) Impact of concurrent use of artificial intelligence tools on radiologists reading time: a prospective feasibility study. Acad Radiol. https://doi.org/10.1016/j.acra.2021.10.008
doi: 10.1016/j.acra.2021.10.008
pubmed: 34801345
Neri E, de Souza N, Brady A, et al (2019) What the radiologist should know about artificial intelligence – an ESR white paper. Insights Imaging 10:. https://doi.org/10.1186/s13244-019-0738-2
van Assen M, Lee SJ, De Cecco CN (2020) Artificial intelligence from A to Z: from neural network to legal framework. Eur J Radiol 129:109083. https://doi.org/10.1016/j.ejrad.2020.109083
Candemir S, Nguyen X V., Folio LR, Prevedello LM (2021) Training strategies for radiology deep learning models in data-limited scenarios. Radiol Artif Intell 3:. https://doi.org/10.1148/ryai.2021210014
Sheller MJ, Reina GA, Edwards B, et al (2019) Multi-institutional deep learning modeling without sharing patient data: a feasibility study on brain tumor segmentation. pp 92–104
Crimi A, Bakas S, Kuijf H, Keyvan F, Reyes M van WT (2019) Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries, Vol 11383, Lecture notes in computer science, 1st es. Cham, Switzerland
Langlotz CP, Allen B, Erickson BJ et al (2019) A roadmap for foundational research on artificial intelligence in medical imaging: from the 2018 NIH/RSNA/ACR/The Academy Workshop. Radiology 291:781–791. https://doi.org/10.1148/radiol.2019190613
doi: 10.1148/radiol.2019190613
pubmed: 30990384
Liang C-H, Liu Y-C, Wu M-T et al (2020) Identifying pulmonary nodules or masses on chest radiography using deep learning: external validation and strategies to improve clinical practice. Clin Radiol 75:38–45. https://doi.org/10.1016/j.crad.2019.08.005
doi: 10.1016/j.crad.2019.08.005
pubmed: 31521323
Yoo H, Kim KH, Singh R et al (2020) Validation of a deep learning algorithm for the detection of malignant pulmonary nodules in chest radiographs. JAMA Netw Open 3:1–14. https://doi.org/10.1001/jamanetworkopen.2020.17135
doi: 10.1001/jamanetworkopen.2020.17135
Sjoding MW, Taylor D, Motyka J et al (2021) Deep learning to detect acute respiratory distress syndrome on chest radiographs: a retrospective study with external validation. Lancet Digit Heal 3:e340–e348. https://doi.org/10.1016/S2589-7500(21)00056-X
doi: 10.1016/S2589-7500(21)00056-X
Zhang Y, Liu M, Hu S et al (2021) Development and multicenter validation of chest X-ray radiography interpretations based on natural language processing. Commun Med 1:1–12. https://doi.org/10.1038/s43856-021-00043-x
doi: 10.1038/s43856-021-00043-x
Ueda D, Yamamoto A, Shimazaki A et al (2021) Artificial intelligence-supported lung cancer detection by multi-institutional readers with multi-vendor chest radiographs: a retrospective clinical validation study. BMC Cancer 21:1120. https://doi.org/10.1186/s12885-021-08847-9
doi: 10.1186/s12885-021-08847-9
pubmed: 34663260
pmcid: 8524996
Hwang EJ, Park S, Jin KN, et al (2019) Development and validation of a deep learning-based automated detection algorithm for major thoracic diseases on chest radiographs. JAMA Netw open 2:e191095. https://doi.org/10.1001/jamanetworkopen.2019.1095
Nam JG, Kim M, Park J, et al (2021) Development and validation of a deep learning algorithm detecting 10 common abnormalities on chest radiographs. Eur Respir J 57:. https://doi.org/10.1183/13993003.03061-2020
Seah JCY, Tang CHM, Buchlak QD et al (2021) Effect of a comprehensive deep-learning model on the accuracy of chest x-ray interpretation by radiologists: a retrospective, multireader multicase study. Lancet Digit Heal 3:e496–e506. https://doi.org/10.1016/S2589-7500(21)00106-0
doi: 10.1016/S2589-7500(21)00106-0
Jones CM, Danaher L, Milne MR et al (2021) Assessment of the effect of a comprehensive chest radiograph deep learning model on radiologist reports and patient outcomes: a real-world observational study. BMJ Open 11:1–11. https://doi.org/10.1136/bmjopen-2021-052902
doi: 10.1136/bmjopen-2021-052902
Homayounieh F, Digumarthy S, Ebrahimian S et al (2021) An artificial intelligence-based chest X-ray model on human nodule detection accuracy from a multicenter study. JAMA Netw Open 4:1–11. https://doi.org/10.1001/jamanetworkopen.2021.41096
doi: 10.1001/jamanetworkopen.2021.41096
Cho J, Lee K, Shin E, et al (2015) How much data is needed to train a medical image deep learning system to achieve necessary high accuracy? https://doi.org/10.48550/arXiv.1511.06348
Simpson SA, Cook TS (2020) Artificial intelligence and the trainee experience in radiology. J Am Coll Radiol 17:1388–1393. https://doi.org/10.1016/j.jacr.2020.09.028
doi: 10.1016/j.jacr.2020.09.028
pubmed: 33010211
Omoumi P, Ducarouge A, Tournier A et al (2021) To buy or not to buy—evaluating commercial AI solutions in radiology (the ECLAIR guidelines). Eur Radiol 31:3786–3796. https://doi.org/10.1007/s00330-020-07684-x
doi: 10.1007/s00330-020-07684-x
pubmed: 33666696
pmcid: 8128726
Laptev VA, Ershova IV, Feyzrakhmanova DR (2021) Medical applications of artificial intelligence (legal aspects and future prospects). Laws 11:3. https://doi.org/10.3390/laws11010003
doi: 10.3390/laws11010003
Muehlematter UJ, Daniore P, Vokinger KN (2021) Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015–20): a comparative analysis. Lancet Digit Heal 3:e195–e203. https://doi.org/10.1016/S2589-7500(20)30292-2
doi: 10.1016/S2589-7500(20)30292-2
Mezrich JL (2022) Is Artificial intelligence (AI) a pipe dream? Why legal issues present significant hurdles to AI autonomy. AJR Am J Roentgenol. https://doi.org/10.2214/ajr.21.27224
European Society of Radiology (2013) European Society of Radiology Code of Ethics. 1–13
Geis JR, Brady A, Wu CC, et al (2019) Ethics of artificial intelligence in radiology: summary of the joint European and North American multisociety statement. Insights Imaging 10:. https://doi.org/10.1186/s13244-019-0785-8
Yang CC (2022) Explainable artificial intelligence for predictive modeling in healthcare. J Healthc Informatics Res. https://doi.org/10.1007/s41666-022-00114-1
doi: 10.1007/s41666-022-00114-1
Levin DC, Rao VM (2017) Reducing inappropriate use of diagnostic imaging through the choosing wisely initiative. J Am Coll Radiol 14:1245–1252. https://doi.org/10.1016/j.jacr.2017.03.012
doi: 10.1016/j.jacr.2017.03.012
pubmed: 28457815
Hong W, Hwang EJ, Lee JH et al (2022) Deep learning for detecting pneumothorax on chest radiographs after needle biopsy: clinical implementation. Radiology. https://doi.org/10.1148/radiol.211706
doi: 10.1148/radiol.211706
pubmed: 36283113
pmcid: 9344212
MacDuff A, Arnold A, Harvey J (2010) Management of spontaneous pneumothorax: British Thoracic Society pleural disease guideline 2010. Thorax 65:. https://doi.org/10.1136/thx.2010.136986