Exploring the Limit of Image Resolution for Human Expert Classification of Vascular Ultrasound Images in Giant Cell Arteritis and Healthy Subjects: The GCA-US-AI Project

Valentin Sebastian Schäfer¹, Stavros Chrysidis², Christian Dejaco³, Sara Monti⁴, Matthew Koster⁵, Pantelis Karakostas¹, Wolfgang Schmidt⁶, Eugenio De Miguel⁷, Christina Duftner⁸, Alojzija Hocevar⁹, Annamaria Iagnocco¹⁰, Marcin Milchert¹¹, Chetan Mukhtyar¹², Cristina Ponte¹³, Lene Terslev¹⁴, Tanaz Kermani¹⁵, Uffe Møller Døhn¹⁶, Berit Dalsgaard Nielsen¹⁷, Aaron Juche¹⁸, Luca Seitz¹⁹, Minna Kohler²⁰, Kresten Keller²¹, Rositsa Karalilova²², Thomas Daikeler²³, Sarah Mackie²⁴, Karina Torralba²⁵, Kornelis van der Geest²⁶, Dennis Boumans²⁷, Philipp Bosch²⁸, Alessandro Tomelleri²⁹, Markus Aschwanden³⁰, Peter Brossart¹, Charlotte Behning³¹ and Claus Juergen Bauer¹, ¹Clinic of Internal Medicine III, Department of Oncology, Hematology, Rheumatology and Clinical Immunology, University Hospital of Bonn, Bonn, Germany, ²Department of Rheumatology, Southwest Jutland Hospital Esbjerg, Esbjerg, Denmark, ³Department of Rheumatology, Medical University Graz, Graz, Austria; Department of Rheumatology, Hospital of Bruneck (ASAA-SABES), Teaching Hospital of the Paracelsius Medical University, Brunico, Italy, ⁴Division of Rheumatology, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy; Department of Internal Medicine and Therapeutics, Università di Pavia, Pavia, Italy, ⁵Mayo Clinic, Rochester, MN, ⁶Rheumatology, Immanuel Krankenhaus Berlin, Medical Centre for Rheumatology Berlin-Buch, Berlin, Germany, ⁷Hospital Universitario La Paz, Madrid, Spain, ⁸Department of Internal Medicine, Clinical Division of Internal Medicine II, Medical University Innsbruck, Innsbruck, Austria, ⁹Department of Rheumatology, Universitiy Medical Centre Ljubljana, Ljubljana, Slovenia, ¹⁰University of Turin, Roma, Italy, ¹¹Department of Internal Medicine, Rheumatology, Diabetology, Geriatrics and Clinical Immunology, Pomeranian Medical University in Szczecin, Szczecin, Poland, ¹²Vasculitis service, Rheumatology department, Norfolk and Norwich University Hospital, Norwich, United Kingdom, ¹³Department of Rheumatology, Centro Hospitalar Universitário Lisboa Norte, Centro Académico de Medicina de Lisboa, Lisbon, Portugal; Rheumatology Research Unit, Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Centro Académico de Medicina de Lisboa, Lisbon, Portugal, ¹⁴Center for Rheumatology and Spine Diseases, Rigshospitalet, Glostrup, Denmark, ¹⁵Rheumatology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, ¹⁶Copenhagen Center for Arthritis Research (COPECARE), Center for Rheumatology and Spine Diseases, Rigshospitalet, Copenhagen, Denmark, ¹⁷Department of Rheumatology, Aarhus University Hospital, Aarhus, Denmark; Department of Medicine, The Regional Hospital in Horsens, Horsens, Denmark, ¹⁸Department of Rheumatology, Immanuel Hospital, Berlin, Germany, ¹⁹Rheumatology and Immunology, Inselspital University Hospital Bern, Bern, Switzerland, ²⁰Massachusetts General Hospital, Harvard Medical School, Boston, MA, ²¹Department of Rheumatology, Aarhus University Hospital; Department of Clinical Medicine, Aarhus University, Aarhus, Denmark, ²²Clinic of Rheumatology, Medical University Plovdiv, Plovdiv, Bulgaria, ²³Clinic for Rheumatology, University Hospital Basel, Basel, Switzerland, ²⁴Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, UK; Leeds Biomedical Research Centre, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom, ²⁵Division of Rheumatology, Department of Medicine, Loma Linda University School of Medicine, Loma Linda, CA, ²⁶Rheumatology and Clinical Immunology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands, ²⁷Rheumatology and Clinical Immunology, Hospital Group Twente, Almelo, Netherlands, ²⁸Medical University of Graz, Graz, Austria, ²⁹Unit of Immunology, Rheumatology, Allergy and Rare Diseases, San Raffaele Scientific Institute, Milano, Italy, ³⁰Department of Angiology, University Hospital Basel, Basel, Switzerland, ³¹Institute of Medical Biometry, Informatics and Epidemiology, University Hospital of Bonn, Bonn, Germany

Meeting: ACR Convergence 2023

Keywords: giant cell arteritis, Imaging, Ultrasound

Session Information

Date: Tuesday, November 14, 2023

Title: (2387–2424) Vasculitis – Non-ANCA-Associated & Related Disorders Poster III

Session Type: Poster Session C

Session Time: 9:00AM-11:00AM

Background/Purpose: Giant cell arteritis (GCA) is the most common form of vasculitis in adults, necessitating prompt diagnosis to prevent severe complications. However, access to expert GCA ultrasonography is often limited to larger medical centers. The utilization of artificial intelligence (AI)-based assistance for ultrasound image classification holds significant potential. The development of such AI systems involves selecting a neural network architecture from a wide range of options, each with distinct characteristics, including limitations in processing image size, resolution, result quality, and resource efficiency. This project aims to address the minimum resolution required for human experts to reliably classify ultrasound images of commonly affected arteries, distinguishing between GCA and normal.

Methods: In this study, a blinded classification task was conducted, involving 42 international experts from the OMERACT subgroup on ultrasound in large vessel vasculitis. A set of 250 vascular ultrasound images from both GCA patients and healthy individuals were presented to the experts. The image selection process initially comprised 10 B-mode scans each of the common temporal artery, its frontal branch, parietal branch, and the axillary artery (all in cross-section), as well as the axillary artery in longitudinal scan. These 50 images were presented in a random order at five distinct resolution levels: 32×32, 64×64, 128×128, 224×224, and 512×512 pixels (refer to Figure 1). The survey and image classification tasks were conducted using REDCap version 13.2.5, while data interim analysis was performed utilizing R (version 4.3.0).

Results: This interim analysis includes data from 30 study group members with an average of 10.5±6.4 years (mean±standard deviation) of experience in ultrasound in GCA. Across all artery categories, the proportion of images considered unclassifiable decreased as the image resolution increased. The mean percentage of unclassifiable images was 91% at 32×32 pixels and reduced to 26% at 512x512pixels (refer to Table 1). Similarly, lower image resolution resulted in less frequent correct classifications (see Figure 2). The chance of correct image classification was comparable at image resolutions of 512×512 pixels (highest resolution level) and 224×224 pixels (odds ratio = 0.99, p=0.891), while it was significantly lower at image sizes of 128×128 pixels (OR=0.79, p=0.01), 64×64 pixels (OR = 0.42, p< 0.001), and 32×32 pixels (OR=0.24, p< 0.001).

Conclusion: As the image resolution increases, the proficiency of human experts in GCA ultrasound improves, enabling more confident and accurate classification of vascular ultrasound images. At a resolution of 224×224 pixels and above, classification accuracy exceeded 50%. The best classification performance was achieved at a resolution of 512×512 pixels (74.13% of all single-image classifications correct). Yet given the comparable performance at a resolution level of 224×224 pixels (73.93% of all single-image classifications correct), this opens up a variety of perspectives in neural network architecture selection for the development of an AI based assistance for GCA ultrasound image classification.

Figure 1: Shown is an exemplary ultrasound image of the common temporal artery (cross-section, B-mode), presented to study participants at five different resolution levels.

Table 1: Frequency of the selected response category “Meaningful classification is not possible” depending on the image resolution level.

Figure 2: Frequency of correct image classification as “GCA patient” or “healthy individual” depending on image resolution level.

Disclosures: V. Schäfer: None; S. Chrysidis: None; C. Dejaco: AbbVie/Abbott, 1, 5, 6, Eli Lilly, 6, Galapagos Pharma, 1, 6, Janssen, 1, 6, Novartis, 1, 6, Pfizer, 6, Sparrow, 1; S. Monti: CSL Vifor, 6; M. Koster: None; P. Karakostas: None; W. Schmidt: AbbVie/Abbott, 1, 5, 6, Amgen, 1, 6, Bristol-Myers Squibb(BMS), 6, Chugai, 6, GlaxoSmithKlein(GSK), 1, 5, 6, Janssen, 6, Medac, 6, Novartis, 1, 5, 6, Roche, 6, UCB, 6; E. De Miguel: None; C. Duftner: None; A. Hocevar: None; A. Iagnocco: None; M. Milchert: None; C. Mukhtyar: None; C. Ponte: None; L. Terslev: Eli Lilly, 1, Janssen, 1, 6, Novartis, 6, UCB, 1; T. Kermani: None; U. Møller Døhn: None; B. Nielsen: None; A. Juche: None; L. Seitz: None; M. Kohler: Mymee, 2, Springer Publications, 9; K. Keller: None; R. Karalilova: None; T. Daikeler: None; S. Mackie: AbbVie/Abbott, 2, AstraZeneca, 2, GlaxoSmithKlein(GSK), 3, 12, Investigator, National Institute for Health and Care Research, 5, 12, investigator on STERLING-PMR trial, funded by NIHR; patron of the charity PMRGCAuk, Pfizer, 2, 6, Roche, 2, 6, 12, Support from Roche/Chugai to attend EULAR2019 in person, Sanofi, 2, 12, Investigator, Sparrow, 12, Investigator, UCB and Novartis, 6, Vifor, 6; K. Torralba: None; K. van der Geest: None; D. Boumans: None; P. Bosch: Janssen, 6, 12, Congress fees, Pfizer, 5; A. Tomelleri: Novartis, 1; M. Aschwanden: None; P. Brossart: None; C. Behning: None; C. Bauer: None.

To cite this abstract in AMA style:

Schäfer V, Chrysidis S, Dejaco C, Monti S, Koster M, Karakostas P, Schmidt W, De Miguel E, Duftner C, Hocevar A, Iagnocco A, Milchert M, Mukhtyar C, Ponte C, Terslev L, Kermani T, Møller Døhn U, Nielsen B, Juche A, Seitz L, Kohler M, Keller K, Karalilova R, Daikeler T, Mackie S, Torralba K, van der Geest K, Boumans D, Bosch P, Tomelleri A, Aschwanden M, Brossart P, Behning C, Bauer C. Exploring the Limit of Image Resolution for Human Expert Classification of Vascular Ultrasound Images in Giant Cell Arteritis and Healthy Subjects: The GCA-US-AI Project [abstract]. Arthritis Rheumatol. 2023; 75 (suppl 9). https://acrabstracts.org/abstract/exploring-the-limit-of-image-resolution-for-human-expert-classification-of-vascular-ultrasound-images-in-giant-cell-arteritis-and-healthy-subjects-the-gca-us-ai-project/. Accessed .

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