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Künstliche Intelligenz in der onkologischen Radiologie

Ein (P)review

Artificial intelligence in oncological radiology

A (p)review

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Zusammenfassung

Hintergrund

Der künstlichen Intelligenz (KI) wird das Potenzial zugeschrieben, die medizinische Arbeitsweise in den kommenden Dekaden nachhaltig zu verändern. Die radiologische Bildgebung stellt hierbei eines der Hauptanwendungsgebiete dar.

Ziel der Arbeit

In diesem Artikel werden bisherige KI-Entwicklungen mit dem Fokus auf die onkologische Radiologie zusammengefasst und an ausgewählten Beispielen mögliche Szenarien für die Entwicklung in den kommenden 10 Jahren abgeleitet.

Material und Methoden

Diese Arbeit basiert auf einer Recherche in verschiedenen Literatur- und Produktdatenbanken, Veröffentlichungen von regulatorischen Behörden und Berichten im Internet.

Schlussfolgerung

Der klinische Einsatz von KI-Anwendungen befindet sich noch in einer frühen Entwicklungsphase. Die große Anzahl an Forschungspublikationen demonstriert das Potenzial dieses Gebiets. Auch stehen den Anwendern bereits erste zertifizierte Produkte zur Verfügung. Für eine flächendeckende Verbreitung von KI-Anwendungen in der klinischen Routine sind jedoch noch einige grundlegende Voraussetzungen zu schaffen. Zu diesen gehören die Generierung von Evidenz für den Einsatz von Algorithmen anhand repräsentativer klinischer Studien, Anpassungen der Rahmenbedingungen für die Zulassung sowie eine gezielte Aus- und Weiterbildung der Anwender. Es ist zu erwarten, dass KI-Methoden künftig zunehmend eingesetzt und damit neue Möglichkeiten für eine bessere Diagnostik und Therapie sowie ein effizienteres Arbeiten geschaffen werden.

Abstract

Background

Artificial intelligence (AI) has the potential to fundamentally change medicine within the coming decades. Radiological imaging is one of the primary fields of its clinical application.

Objectives

In this article, we summarize previous AI developments with a focus on oncological radiology. Based on selected examples, we derive scenarios for developments in the next 10 years.

Materials and methods

This work is based on a review of various literature and product databases, publications by regulatory authorities, reports, and press releases.

Conclusions

The clinical use of AI applications is still in an early stage of development. The large number of research publications shows the potential of the field. Several certified products have already become available to users. However, for a widespread adoption of AI applications in clinical routine, several fundamental prerequisites are still awaited. These include the generation of evidence justifying the use of algorithms through representative clinical studies, adjustments to the framework for approval processes and dedicated education and teaching resources for its users. It is expected that use of AI methods will increase, thus, creating new opportunities for improved diagnostics, therapy, and more efficient workflows.

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Notes

  1. Durch die Autoren dieses Artikels eingeteilt.

Literatur

  1. Reardon S Rise of robot radiologists. In: Sci. Am. https://www.scientificamerican.com/article/rise-of-robot-radiologists/. Zugegriffen: 1. Nov. 2020

  2. Alexander A, Jiang A, Ferreira C, Zurkiya D (2020) An intelligent future for medical imaging: a market outlook on artificial intelligence for medical imaging. J Am Coll Radiol 17:165–170

    Article  Google Scholar 

  3. Ugalmugle S (2019) AI in Healthcare Market size to exceed $13 Bn by 2025. Global Market Insights. https://www.gminsights.com/. Zugegriffen: 1. Nov. 2020

  4. Artificial Intelligence in Healthcare Market with Covid-19 Impact Analysis by Offering (Hardware, Software, Services), Technology (Machine Learning, NLP, Context-Aware Computing, Computer Vision), End-Use Application, End User and Region – Global Forecast to 2026. 246

  5. Columbus L (2020) What’s new in Gartner’s hype cycle for AI, 2020. In: Forbes. https://www.forbes.com/sites/louiscolumbus/2020/10/04/whats-new-in-gartners-hype-cycle-for-ai-2020/#2ad095c8335c. Zugegriffen: 28. Okt. 2020

  6. McCarthy J, Minsky ML, Rochester N, Shannon CE (2006) A proposal for the Dartmouth summer research project on artificial intelligence, August 31, 1955. Al Mag 27:12–12

    Google Scholar 

  7. Roellinger FX, Kahveci AE, Chang JK, Harlow CA, Dwyer SJ, Lodwick GS (1973) Computer analysis of chest radiographs. Comput Graph Image Process 2:232–251

    Article  Google Scholar 

  8. Kruger RP, Thompson WB, Turner AF (1974) Computer diagnosis of pneumoconiosis. IEEE Trans Syst Man Cybern SMC 4:40–49

    Article  Google Scholar 

  9. Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86:2278–2324

    Article  Google Scholar 

  10. Hamilton JG, Genoff Garzon M, Westerman JS et al (2019) “A tool, not a crutch”: patient perspectives about IBM watson for oncology trained by memorial Sloan Kettering. J Oncol Pract 15:e277–e288

    Article  Google Scholar 

  11. Somashekhar SP, Sepúlveda M‑J, Puglielli S et al (2018) Watson for Oncology and breast cancer treatment recommendations: agreement with an expert multidisciplinary tumor board. Ann Oncol 29:418–423

    Article  CAS  Google Scholar 

  12. Zhao X, Zhang Y, Ma X et al (2020) Concordance between treatment recommendations provided by IBM Watson for Oncology and a multidisciplinary tumor board for breast cancer in China. Jpn J Clin Oncol 50:852–858

    Article  Google Scholar 

  13. Strickland E (2019) IBM Watson, heal thyself: how IBM overpromised and underdelivered on AI health care. IEEE Spectr 56:24–31

    Article  Google Scholar 

  14. Schmidt C (2017) M. D. Anderson breaks with IBM watson, raising questions about artificial intelligence in oncology. JNCI J Natl Cancer Inst. https://doi.org/10.1093/jnci/djx113

    Article  PubMed  Google Scholar 

  15. Brown TB, Mann B, Ryder N et al (2020) Language models are few-shot learners. ArXiv200514165Cs

    Google Scholar 

  16. ImageNet Large Scale Visual Recognition Competition 2012 (ILSVRC2012). http://www.image-net.org/challenges/LSVRC/2012/results.html. Zugegriffen: 29 Okt. 2020

  17. Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60:84–90

    Article  Google Scholar 

  18. Treanor J (2015) The 2010 “flash crash”: how it unfolded. The Guardian

  19. The Medical Futurist https://medicalfuturist.com/fda-approved-ai-based-algorithms/. Zugegriffen: 28. Okt. 2020

  20. Benjamens S, Dhunnoo P, Meskó B (2020) The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. Npj Digit Med 3:1–8

    Article  Google Scholar 

  21. AI for radiology. https://grand-challenge.org/aiforradiology/. Zugegriffen: 28 Okt. 2020

  22. FDA Cleared AI Algorithms. https://www.acrdsi.org/DSI-Services/FDA-Cleared-AI-Algorithms. Zugegriffen: 28 Okt. 2020

  23. (2020) Comments of the American College of Radiology on “Public Workshop—Evolving Role of Artificial Intelligence in Radiological Imaging;” Docket No. FDA-2019-N-5592:

  24. FDA Cleared AI Algorithms. https://www.acrdsi.org/DSI-Services/FDA-Cleared-AI-Algorithms. Zugegriffen: 28 Okt. 2020

  25. (2020) Change healthcare artificial intelligence decreases administrative burden of case management. In: Imaging Technol. News. https://www.itnonline.com/content/change-healthcare-artificial-intelligence-decreases-administrative-burden-case-management. Zugegriffen: 31. Okt. 2020

  26. Advanced intelligent Clear-IQ Engine (AiCE) | MRI | Magnetic Resonance Imaging | Canon Medical Systems USA. https://us.medical.canon/products/magnetic-resonance/aice/. Zugegriffen: 1 Nov. 2020

  27. Uetani H, Nakaura T, Kitajima M et al (2020) A preliminary study of deep learning-based reconstruction specialized for denoising in high-frequency domain: usefulness in high-resolution three-dimensional magnetic resonance cisternography of the cerebellopontine angle. Neuroradiology. https://doi.org/10.1007/s00234-020-02513-w

    Article  PubMed  Google Scholar 

  28. Subtle Medical—Deep Learning boosts healthcare. Faster, Safer, Smarter. https://subtlemedical.com/. Zugegriffen: 1 Nov. 2020

  29. Viz Ischemic Stroke Platform. https://www.viz.ai/ischemic-stroke. Zugegriffen: 1 Nov. 2020

  30. Radiology AI | Aidoc Always-on AI. In: Aidoc. https://www.aidoc.com/. Zugegriffen: 1 Nov. 2020

  31. Combinostics, cNeuro® cMRI. Fully automated solution for quantifying MR brain scans. https://www.cneuro.com/cmri/. Zugegriffen: 01. Nov. 2020

  32. Kann BH, Thompson R, Thomas CR, Dicker A, Aneja S (2019) Artificial intelligence in oncology: current applications and future directions. Oncology 33:46–53

    PubMed  Google Scholar 

  33. Dunn WD, Aerts HJWL, Cooper LA et al (2016) Assessing the effects of software platforms on volumetric segmentation of Glioblastoma. J Neuroimaging Psychiatry Neurol 1:64–72

    PubMed  PubMed Central  Google Scholar 

  34. Raghu VK, Zhao W, Pu J, Leader JK, Wang R, Herman J, Yuan J‑M, Benos PV, Wilson DO (2019) Feasibility of lung cancer prediction from low-dose CT scan and smoking factors using causal models. Thorax 74:643–649

    Article  Google Scholar 

  35. Lu MT, Rosman DA, Wu CC, Gilman MD, Harvey HB, Gervais DA, Alkasab TK, Shepard J‑AO, Boland GW, Pandharipande PV (2016) Radiologist point-of-care clinical decision support and adherence to guidelines for incidental lung nodules. J Am Coll Radiol 13:156–162

    Article  Google Scholar 

  36. Quantib Receives FDA Clearance for First-to-Market Prostate Solution | Imaging Technology News. https://www.itnonline.com/content/quantib-receives-fda-clearance-first-market-prostate-solution. Zugegriffen: 1 Nov. 2020

  37. Ezra Receives FDA Clearance for Prostate Cancer Artificial Intelligence. https://www.prnewswire.com/news-releases/ezra-receives-fda-clearance-for-prostate-cancer-artificial-intelligence-301154585.html. Zugegriffen: 31 Okt. 2020

  38. Magazine ICE (2020) FDA clears transpara 3D

    Google Scholar 

  39. Salim M, Wåhlin E, Dembrower K, Azavedo E, Foukakis T, Liu Y, Smith K, Eklund M, Strand F (2020) External evaluation of 3 commercial artificial intelligence algorithms for independent assessment of screening mammograms. JAMA Oncol 6:1581–1588

    Article  Google Scholar 

  40. cmTriage FDA Cleared—CureMetrix—2D Mammogram. CureMetrix

  41. Inform AI | Advancing Healthcare Through Analytics. https://www.informai.com/. Zugegriffen: 1 Nov. 2020

  42. Arterys Marketplace. https://marketplace.arterys.com/clinicalApp/lungapp. Zugegriffen: 1 Nov. 2020

  43. Fujifilm’s New AI-based Technology for Lung Nodule Detection Now Approved for Use in Japan. In: Fujifilm Glob. https://www.fujifilm.com/news/n200527_01.html. Zugegriffen: 1 Nov. 2020

  44. Lo SB, Freedman MT, Gillis LB, White CS, Mun SK (2018) JOURNAL CLUB: computer-aided detection of lung nodules on CT with a computerized pulmonary vessel suppressed function. Am J Roentgenol 210:480–488

    Article  Google Scholar 

  45. Lung AI. Arterys Marketplace. https://marketplace.arterys.com/clinicalApp/lungapp/. Zugegriffen: 1. Nov. 2020

  46. AI Radiology Solutions Request a Demo Today! In: Oxipit. https://oxipit.ai/. Zugegriffen: 1. Nov. 2020

  47. Liang C‑H, Liu Y‑C, Wu M‑T, Garcia-Castro F, Alberich-Bayarri A, Wu F‑Z (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

    Article  Google Scholar 

  48. Tandel GS, Biswas M, Kakde OG et al (2019) A review on a deep learning perspective in brain cancer classification. Cancers 11:111

    Article  Google Scholar 

  49. Geras KJ, Mann RM, Moy L (2019) Artificial intelligence for mammography and digital breast tomosynthesis: current concepts and future perspectives. Radiology 293:246–259

    Article  Google Scholar 

  50. Houssami N, Kirkpatrick-Jones G, Noguchi N, Lee CI (2019) Artificial Intelligence (AI) for the early detection of breast cancer: a scoping review to assess AI’s potential in breast screening practice. Expert Rev Med Devices 16:351–362

    Article  CAS  Google Scholar 

  51. Bardis MD, Houshyar R, Chang PD, Ushinsky A, Glavis-Bloom J, Chahine C, Bui T‑L, Rupasinghe M, Filippi CG, Chow DS (2020) Applications of artificial intelligence to prostate multiparametric MRI (mpMRI): current and emerging trends. Cancers. https://doi.org/10.3390/cancers12051204

    Article  PubMed  PubMed Central  Google Scholar 

  52. Chassagnon G, Vakalopoulou M, Paragios N, Revel M‑P (2020) Artificial intelligence applications for thoracic imaging. Eur J Radiol 123:108774

    Article  Google Scholar 

  53. Hamm CA, Wang CJ, Savic LJ et al (2019) Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI. Eur Radiol 29:3338–3347

    Article  Google Scholar 

  54. Wang CJ, Hamm CA, Savic LJ et al (2019) Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features. Eur Radiol 29:3348–3357

    Article  Google Scholar 

  55. Nagendran M, Chen Y, Lovejoy CA, Gordon AC, Komorowski M, Harvey H, Topol EJ, Ioannidis JPA, Collins GS, Maruthappu M (2020) Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. BMJ. https://doi.org/10.1136/bmj.m689

    Article  PubMed  PubMed Central  Google Scholar 

  56. Breast Ultrasound Image Reviewed With Assistance of Deep Learning Algorithms—Full Text View - ClinicalTrials.gov. https://clinicaltrials.gov/ct2/show/NCT03706534. Zugegriffen: 29 Okt. 2020

  57. 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

    Article  Google Scholar 

  58. The SPIRIT-AI and CONSORT-AI Working Group, Liu X, Rivera CS, Moher D, Calvert MJ, Denniston AK (2020) Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat Med 26:1364–1374

    Article  Google Scholar 

  59. Pianykh OS, Langs G, Dewey M, Enzmann DR, Herold CJ, Schoenberg SO, Brink JA (2020) Continuous learning AI in radiology: implementation principles and early applications. Radiology 297:6–14

    Article  Google Scholar 

  60. Health C for D and R (2020) Digital Health Software Precertification (Pre-Cert) Program. In: FDA. https://www.fda.gov/medical-devices/digital-health-center-excellence/digital-health-software-precertification-pre-cert-program. Zugegriffen: 15. Nov. 2020

  61. Geis JR, Brady AP, Wu CC et al (2019) Ethics of artificial intelligence in radiology: summary of the joint European and north American Multisociety statement. Radiology 293:436–440

    Article  Google Scholar 

  62. Magrabi F, Ammenwerth E, McNair JB et al (2019) Artificial intelligence in clinical decision support: challenges for evaluating AI and practical implications: a position paper from the IMIA technology assessment & quality development in health Informatics working group and the EFMI working group for assessment of health information systems. Yearb Med Inform 28:128–134

    Article  Google Scholar 

  63. Codari M, Melazzini L, Morozov SP, van Kuijk CC, Sconfienza LM, Sardanelli F, European Society of Radiology (2019) Impact of artificial intelligence on radiology: a EuroAIM survey among members of the European Society of Radiology. Insights Imaging 10:105

    Article  Google Scholar 

  64. Kohli A, Jha S (2018) Why CAD failed in mammography. J Am Coll Radiol 15:535–537

    Article  Google Scholar 

  65. Lehman CD, Wellman RD, Buist DSM, Kerlikowske K, Tosteson ANA, Miglioretti DL (2015) Diagnostic accuracy of digital screening mammography with and without computer-aided detection. JAMA Intern Med 175:1828

    Article  Google Scholar 

  66. Commissioner O of the (2020) FDA permits marketing of artificial intelligence-based device to detect certain diabetes-related eye problems. In: FDA. https://www.fda.gov/news-events/press-announcements/fda-permits-marketing-artificial-intelligence-based-device-detect-certain-diabetes-related-eye. Zugegriffen: 31. Okt. 2020

  67. Viz.ai Viz.ai Granted Medicare New Technology Add-on Payment. https://www.prnewswire.com/news-releases/vizai-granted-medicare-new-technology-add-on-payment-301123603.html. Zugegriffen: 31 Okt. 2020

  68. Hassan AE, Ringheanu VM, Rabah RR, Preston L, Tekle WG, Qureshi AI (2020) Early experience utilizing artificial intelligence shows significant reduction in transfer times and length of stay in a hub and spoke model. Interv Neuroradiol 26:615–622

    Article  Google Scholar 

  69. https://kurzweilai.net Artificial intelligence, human brain to merge in 2030s, says futurist Kurzweil Kurzweil. Zugegriffen: 1. Nov. 2020

  70. Dewey M (2018) The future of radiology: adding value to clinical care. Lancet 392:472–473

    Article  Google Scholar 

  71. Huang S‑C, Pareek A, Seyyedi S, Banerjee I, Lungren MP (2020) Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines. Npj Digit Med 3:136

    Article  Google Scholar 

  72. Arimura H, Soufi M, Kamezawa H, Ninomiya K, Yamada M (2019) Radiomics with artificial intelligence for precision medicine in radiation therapy. J Radiat Res 60:150–157

    Article  Google Scholar 

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Authors and Affiliations

  1. Institut für Diagnostische und Interventionelle Radiologie, Universitätsklinikum Frankfurt am Main, Theodor-Stern Kai 7, 60590, Frankfurt am Main, Deutschland

    Andreas M. Bucher

  2. Translationale bildgestützte Onkologie, Institut für KI in der Medizin (IKIM), Universitätsmedizin Essen, Essen, Deutschland

    Jens Kleesiek

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  1. Andreas M. Bucher
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Correspondence to Andreas M. Bucher.

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Interessenkonflikt

A.M. Bucher und J. Kleesiek geben an, dass kein Interessenkonflikt besteht.

Für diesen Beitrag wurden von den Autoren keine Studien an Menschen oder Tieren durchgeführt. Für die aufgeführten Studien gelten die jeweils dort angegebenen ethischen Richtlinien.

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Bucher, A.M., Kleesiek, J. Künstliche Intelligenz in der onkologischen Radiologie. Radiologe 61, 52–59 (2021). https://doi.org/10.1007/s00117-020-00787-y

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