Ultraschalldiagnostik des hepatozellulären Karzinoms: In Zukunft nur mit künstlicher Intelligenz?

Introduction

Technological progress and the development of complex mathematical models that allow the analysis of large and partially unstructured data have led to the rapid development of artificial intelligence (AI) since the 2010 s [1]. New AI applications, such as the recent “chatbot” ChatGPT, regularly attract a great deal of media attention with headlines ranging from euphoric to critical. As a result, the population developed specific expectations of the benefits, but also concerns about the potential risks of AI. In a survey published in 2023 by the digital association Bitkom, 73 % of the 1007 people surveyed saw AI as an opportunity [2]. Two thirds wanted AI to be used when it would bring specific benefits, for example in medicine or transportation. 14 % and 10 % of respondents saw AI rather or exclusively as a risk, respectively. The majority of respondents assumed that AI would noticeably change our society in the coming years. These survey results impressively show how much is already expected of AI. And indeed, AI accompanies us consciously or unconsciously in many everyday situations. There are also several examples in clinical medicine, e. g., in the automated evaluation of ECGs, differential blood counts, etc. [1]. Intensive research has been carried out into the possible use of AI in medical imaging since its beginnings over 80 years ago. In addition to approaches for image optimization, this primarily includes automated diagnosis for disease detection and classification as well as therapeutic monitoring. Enormous progress has been made in the field of imaging in recent years through the use of deep learning (DL) technologies. In contrast to classic forms of machine learning (ML), DL is based on neural networks in which several network levels are linked together [3]. Convolutional neural networks (CNNs) are frequently used in the field of image recognition. These are characterized by a hierarchical recognition of image patterns by the different network levels [3]. If initial structures such as corners, edges, or simple shapes are recognized, the linking of these simple structures in the deeper network levels enables the classification of complex structures such as malignancies in clinical imaging. In addition to better predictability, CNNs are more flexible than traditional ML. Furthermore, the time-consuming extraction of diagnostically relevant image information (“feature extraction”), which is necessary with classic ML, is no longer required, as image features are recognized independently by CNNs.

Particularly in radiological imaging (including computed tomography and magnetic resonance imaging), CNNs have achieved outstanding results in clinical diagnostics and therapeutic monitoring. These have already led to various commercially available and approved AI applications in the field of oncological imaging, among others [4] ([Fig. 1]).

Fig. 1 Use of artificial intelligence (AI) in oncological imaging. Previous studies have investigated the possible use of AI in the detection, characterization, and therapeutic monitoring of oncological diseases. Illustration modified according to El Naqa et al., Br J Radiol, 2020 [5].

Compared to radiological imaging, the use of AI in sonographic imaging involves particular challenges [6]. Due to the examiner dependency in sonographic image acquisition, possible image material for training AI generally has a higher variability. The representation of identical findings in different scanning planes can, for example, lead to considerable differences in image interpretation and thus make correct classification by the AI more difficult. Similar effects can be caused by differences in the devices or transducers used.

In the following, we will discuss the extent to which these effects impact the use of AI in sonography, where AI currently stands in sonography, what limitations are to be expected, and what steps are needed next to enable the successful translation of AI into clinical sonography, using the example of the sonographic diagnosis of hepatocellular carcinoma (HCC).

Publication History

Article published online:
01 February 2024

• References

• 1 Haug CJ, Drazen JM. Artificial Intelligence and Machine Learning in Clinical Medicine. N Engl J Med 2023; 388 (13) 1201-1208
• 2 Streim A, Beerlink KP. Drei Viertel sehen Künstliche Intelligenz als Chance. Presseinformation [Web Page] 2023 [cited 2023 11/12/2023]; Available from:. https://www.bitkom.org/Presse/Presseinformation/Drei-Viertel-sehen-Kuenstliche-Intelligenz-Chance
• 3 Kim M, Yun J, Cho Y. et al. Deep Learning in Medical Imaging. Neurospine 2019; 16 (04) 657-668
• 4 Koh DM, Papanikolaou N, Bick U. et al. Artificial intelligence and machine learning in cancer imaging. Commun Med (Lond) 2022; 2: 133
• 5 El Naqa I, Haider MA, Giger ML. et al. Artificial Intelligence: reshaping the practice of radiological sciences in the 21st century. Br J Radiol 2020; 93: 20190855
• 6 Park SH. Artificial intelligence for ultrasonography: unique opportunities and challenges. Ultrasonography 2021; 40 (01) 3-6
• 7 Rinella ME, Lazarus JV, Ratziu V. et al. A multisociety Delphi consensus statement on new fatty liver disease nomenclature. Ann Hepatol 2023; 29 (01) 101133
• 8 Gross S, Bitzer M, Blodt S. et al. Leitlinienreport der S3-Leitlinie Diagnostik und Therapie des Hepatozellulären Karzinoms und biliärer Karzinome – Version 3.0 – Juli 2022, AWMF-Registernummer: 032/053OL. Z Gastroenterol 2023; 61 (04) e157-e171
• 9 European Association for the Study of the Liver. Electronic address, e.e.e. and L. European Association for the Study of the, EASL Clinical Practice Guidelines: Management of hepatocellular carcinoma. J Hepatol 2018; 69 (01) 182-236
• 10 Dietrich CF, Teufel A, Sirlin CB. et al. Surveillance of hepatocellular carcinoma by medical imaging. Quant Imaging Med Surg 2019; 9 (11) 1904-1910
• 11 Tiyarattanachai T, Apiparakoon T, Marukatat S. et al. Development and validation of artificial intelligence to detect and diagnose liver lesions from ultrasound images. PLoS One 2021; 16 (06) e0252882
• 12 Yang Q, Wei J, Hao X. et al. Improving B-mode ultrasound diagnostic performance for focal liver lesions using deep learning: A multicentre study. EBioMedicine 2020; 56: 102777
• 13 Strobel D. Using contrast-enhanced ultrasound in the diagnosis of hepatocellular carcinoma – what we have and have not achieved. Ultraschall in Med 2021; 42 (02) 120-124
• 14 Schellhaas B, Bernatik T, Dirks K. et al. Contrast-Enhanced Ultrasound Patterns for the Non-invasive Diagnosis of Hepatocellular Carcinoma: A Prospective Multicenter Study in Histologically Proven Liver Lesions in a Real-Life Setting Demonstrating the Benefit of Extended Late Phase Observation. Ultrasound Med Biol 2021; 47 (11) 3170-3180
• 15 Schellhaas B, Bernatik T, Bohle W. et al. Contrast-Enhanced Ultrasound Algorithms (CEUS-LIRADS/ESCULAP) for the Noninvasive Diagnosis of Hepatocellular Carcinoma – A Prospective Multicenter DEGUM Study. Ultraschall in Med 2021; 42 (02) 178-186
• 16 Vetter M, Waldner MJ, Zundler S. et al. Artificial intelligence for the classification of focal liver lesions in ultrasound – a systematic review. Ultraschall in Med 2023;
• 17 Kuang M, Hu HT, Li W. et al. Articles That Use Artificial Intelligence for Ultrasound: A Readerʼs Guide. Front Oncol 2021; 11: 631813