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Diagnostics
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Imaging and Artificial Intelligence in Rheumatology
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Imaging and Artificial Intelligence in Rheumatology

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 9781

Special Issue Editors

Prof. Dr. Philipp Sewerin

E-Mail Website
Guest Editor
Rheumazentrum Ruhrgebiet, University Hospital of the Ruhr University Bochum, Herne, Germany
Interests: inflammatory joint diseases; rheumatoid arthritis; psoriatic arthritis; SpA; axSpA; nraxSpA; MRI; hybrid imaging
Dr. Daniel B. Abrar

E-Mail Website
Guest Editor
Institute for Diagnostic and Interventional Radiology, University Hospital Düsseldorf, Düsseldorf, Germany
Interests: MRI; musculoskeletal diseases; hybrid imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In rheumatology, imaging techniques have always been used to detect inflammatory joint and systemic diseases, as well as to investigate the efficacy of clinical therapies within and outside clinical trials. These techniques contribute to the diagnostic algorithm and therapy monitoring; however, they are also increasingly frequently used for prognosis assessment.

In addition, artificial intelligence has already found a place in medicine. The value of AI was evaluated very early in imaging procedures, particularly due to the often highly standardized data sets. It was shown that these systems can support physicians in (early) diagnosis, but also in therapy control. The application of AI-based algorithms is, therefore, an important tool for the future, in order to improve and optimize the effectiveness of such procedures.

In this section, we will present a compilation of research, with a particular focus on imaging and AI in rheumatology. This includes not only the established imaging techniques, but also new, modern techniques, sequences, application areas or algorithms. In addition, studies based on AI should also be addressed, in order to cover this promising topic.

Prof. Dr. Philipp Sewerin
Dr. Daniel B. Abrar
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • rheumatic diseases
  • imaging
  • innovative techniques
  • AI

Published Papers (5 papers)

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14 pages, 3370 KiB  
Article
Deep Learned Segmentations of Inflammation for Novel ⁹⁹mTc-maraciclatide Imaging of Rheumatoid Arthritis
by Robert Cobb, Gary J. R. Cook and Andrew J. Reader
Diagnostics 2023, 13(21), 3298; https://doi.org/10.3390/diagnostics13213298 - 24 Oct 2023
Viewed by 843
Abstract
Rheumatoid arthritis (RA) is an autoimmune disease that causes joint pain, stiffness, and erosion. Power Doppler ultrasound and MRI are imaging modalities used in detecting and monitoring the disease, but they have limitations. ⁹⁹mTc-maraciclatide gamma camera imaging is a novel technique [...] Read more.
Rheumatoid arthritis (RA) is an autoimmune disease that causes joint pain, stiffness, and erosion. Power Doppler ultrasound and MRI are imaging modalities used in detecting and monitoring the disease, but they have limitations. ⁹⁹mTc-maraciclatide gamma camera imaging is a novel technique that can detect joint inflammation at all sites in a single examination and has been shown to correlate with power Doppler ultrasound. In this work, we investigate if machine learning models can be used to automatically segment regions of normal, low, and highly inflamed tissue from 192 ⁹⁹mTc-maraciclatide scans of the hands and wrists from 48 patients. Two models were trained: a thresholding model that learns lower and upper threshold values and a neural-network-based nnU-Net model that uses a convolutional neural network (CNN). The nnU-Net model showed 0.94 ± 0.01, 0.51 ± 0.14, and 0.76 ± 0.16 modified Dice scores for segmenting the normal, low, and highly inflamed tissue, respectively, when compared to clinical segmented labels. This outperforms the thresholding model, which achieved modified Dice scores of 0.92 ± 0.01, 0.14 ± 0.07, and 0.35 ± 0.21, respectively. This is an important first step in developing artificial intelligence (AI) tools to assist clinicians’ workflow in the use of this new radiopharmaceutical. Full article
(This article belongs to the Special Issue Imaging and Artificial Intelligence in Rheumatology)
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15 pages, 3168 KiB  
Article
An AI-Powered Clinical Decision Support System to Predict Flares in Rheumatoid Arthritis: A Pilot Study
by Hannah Labinsky, Dubravka Ukalovic, Fabian Hartmann, Vanessa Runft, André Wichmann, Jan Jakubcik, Kira Gambel, Katharina Otani, Harriet Morf, Jule Taubmann, Filippo Fagni, Arnd Kleyer, David Simon, Georg Schett, Matthias Reichert and Johannes Knitza
Diagnostics 2023, 13(1), 148; https://doi.org/10.3390/diagnostics13010148 - 01 Jan 2023
Cited by 6 | Viewed by 2808
Abstract
Treat-to-target (T2T) is a main therapeutic strategy in rheumatology; however, patients and rheumatologists currently have little support in making the best treatment decision. Clinical decision support systems (CDSSs) could offer this support. The aim of this study was to investigate the accuracy, effectiveness, [...] Read more.
Treat-to-target (T2T) is a main therapeutic strategy in rheumatology; however, patients and rheumatologists currently have little support in making the best treatment decision. Clinical decision support systems (CDSSs) could offer this support. The aim of this study was to investigate the accuracy, effectiveness, usability, and acceptance of such a CDSS—Rheuma Care Manager (RCM)—including an artificial intelligence (AI)-powered flare risk prediction tool to support the management of rheumatoid arthritis (RA). Longitudinal clinical routine data of RA patients were used to develop and test the RCM. Based on ten real-world patient vignettes, five physicians were asked to assess patients’ flare risk, provide a treatment decision, and assess their decision confidence without and with access to the RCM for predicting flare risk. RCM usability and acceptance were assessed using the system usability scale (SUS) and net promoter score (NPS). The flare prediction tool reached a sensitivity of 72%, a specificity of 76%, and an AUROC of 0.80. Perceived flare risk and treatment decisions varied largely between physicians. Having access to the flare risk prediction feature numerically increased decision confidence (3.5/5 to 3.7/5), reduced deviations between physicians and the prediction tool (20% to 12% for half dosage flare prediction), and resulted in more treatment reductions (42% to 50% vs. 20%). RCM usability (SUS) was rated as good (82/100) and was well accepted (mean NPS score 7/10). CDSS usage could support physicians by decreasing assessment deviations and increasing treatment decision confidence. Full article
(This article belongs to the Special Issue Imaging and Artificial Intelligence in Rheumatology)
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14 pages, 2334 KiB  
Article
Adaptive IoU Thresholding for Improving Small Object Detection: A Proof-of-Concept Study of Hand Erosions Classification of Patients with Rheumatic Arthritis on X-ray Images
by Karl Ludger Radke, Matthias Kors, Anja Müller-Lutz, Miriam Frenken, Lena Marie Wilms, Xenofon Baraliakos, Hans-Jörg Wittsack, Jörg H. W. Distler, Daniel B. Abrar, Gerald Antoch and Philipp Sewerin
Diagnostics 2023, 13(1), 104; https://doi.org/10.3390/diagnostics13010104 - 29 Dec 2022
Cited by 5 | Viewed by 2392
Abstract
In recent years, much research evaluating the radiographic destruction of finger joints in patients with rheumatoid arthritis (RA) using deep learning models was conducted. Unfortunately, most previous models were not clinically applicable due to the small object regions as well as the close [...] Read more.
In recent years, much research evaluating the radiographic destruction of finger joints in patients with rheumatoid arthritis (RA) using deep learning models was conducted. Unfortunately, most previous models were not clinically applicable due to the small object regions as well as the close spatial relationship. In recent years, a new network structure called RetinaNets, in combination with the focal loss function, proved reliable for detecting even small objects. Therefore, the study aimed to increase the recognition performance to a clinically valuable level by proposing an innovative approach with adaptive changes in intersection over union (IoU) values during training of Retina Networks using the focal loss error function. To this end, the erosion score was determined using the Sharp van der Heijde (SvH) metric on 300 conventional radiographs from 119 patients with RA. Subsequently, a standard RetinaNet with different IoU values as well as adaptively modified IoU values were trained and compared in terms of accuracy, mean average accuracy (mAP), and IoU. With the proposed approach of adaptive IoU values during training, erosion detection accuracy could be improved to 94% and an mAP of 0.81 ± 0.18. In contrast Retina networks with static IoU values achieved only an accuracy of 80% and an mAP of 0.43 ± 0.24. Thus, adaptive adjustment of IoU values during training is a simple and effective method to increase the recognition accuracy of small objects such as finger and wrist joints. Full article
(This article belongs to the Special Issue Imaging and Artificial Intelligence in Rheumatology)
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11 pages, 2535 KiB  
Article
Virtual Monochromatic Images from Dual-Energy Computed Tomography Do Not Improve the Detection of Synovitis in Hand Arthritis
by Sevtap Tugce Ulas, Katharina Ziegeler, Sophia-Theresa Richter, Sarah Ohrndorf, Fabian Proft, Denis Poddubnyy and Torsten Diekhoff
Diagnostics 2022, 12(8), 1891; https://doi.org/10.3390/diagnostics12081891 - 04 Aug 2022
Cited by 1 | Viewed by 1671
Abstract
The objective of this study was to investigate subtraction images from different polychromatic and virtual monochromatic reconstructions of dual-energy computed tomography (CT) for the detection of inflammation (synovitis/tenosynovitis or peritendonitis) in patients with hand arthritis. In this IRB-approved prospective study, 35 patients with [...] Read more.
The objective of this study was to investigate subtraction images from different polychromatic and virtual monochromatic reconstructions of dual-energy computed tomography (CT) for the detection of inflammation (synovitis/tenosynovitis or peritendonitis) in patients with hand arthritis. In this IRB-approved prospective study, 35 patients with acute hand arthritis underwent contrast-enhanced dual-energy CT and musculoskeletal ultrasound (MSUS) of the clinically dominant hand. CT subtractions (CT-S) were calculated from 80 and 135 kVp source data and monochromatic 50 and 70 keV images. CT-S and MSUS were scored for synovitis and tenosynovitis/peritendonitis. Specificity, sensitivity and diagnostic accuracy were assessed by using MSUS as a reference. Parameters of objective image quality were measured. Thirty-three patients were analyzed. MSUS was positive for synovitis and/or tenosynovitis/peritendonitis in 28 patients. The 70 keV images had the highest diagnostic accuracy, with 88% (vs. 50 keV, 82%; 80 kVp, 85%; and 135 kVp, 82%), and superior sensitivity, with 96% (vs. 50 keV: 86%, 80 kVp: 93% and 135 kVp: 79%). The 80 kVp images showed the highest signal- and contrast-to-noise ratio, while the 50 keV images provided the lowest image quality. While all subtraction methods of contrast-enhanced dual-energy CT proved to be able to detect inflammation with sufficient diagnostic accuracy, virtual monochromatic images with low keV showed no significant improvement over conventional subtraction techniques and lead to a loss of image quality. Full article
(This article belongs to the Special Issue Imaging and Artificial Intelligence in Rheumatology)
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7 pages, 538 KiB  
Case Report
Reversible Cerebral Vasoconstriction Syndrome and Raynaud’s Phenomenon: Is There a Link between the Pathogeneses of Their Underlying Complex Etiology? A Case Report and Literature Review
by Fahidah Alenzi and David P D’Cruz
Diagnostics 2023, 13(18), 2951; https://doi.org/10.3390/diagnostics13182951 - 14 Sep 2023
Viewed by 883
Abstract
Reversible cerebral vasoconstriction syndrome (RCVS) typically manifests as a sudden, severe thunderclap headache due to narrowing of the cerebral arteries. Symptoms usually resolve within three months. An imbalance in cerebral vascular tone, an abnormal endothelial function, and a decreased autoregulation of cerebral blood [...] Read more.
Reversible cerebral vasoconstriction syndrome (RCVS) typically manifests as a sudden, severe thunderclap headache due to narrowing of the cerebral arteries. Symptoms usually resolve within three months. An imbalance in cerebral vascular tone, an abnormal endothelial function, and a decreased autoregulation of cerebral blood flow are thought to be involved in the pathogenesis of RCVS. However, the precise origin of this condition is not yet fully understood. Symptoms of Raynaud’s phenomenon (RP) include vasospasm of arterioles of the digits. The pathophysiology of RP includes interactions between the endothelium, smooth muscle, and autonomic and sensory neurons that innervate arteries to help maintain vasomotor homeostasis. RP may occur before the clinical manifestation of a rheumatic condition. RCVS is rare in patients with autoimmune rheumatic disease. We describe a 54-year-old female who had a history of Raynaud’s phenomenon affecting her fingers and toes since the age of 12 years. The patient was diagnosed with RCVS in 2012. She described RCVS precipitants, including the regular use of cannabis, cocaine, and amphetamine and tobacco smoking. In 2021, she presented with oral ulcers, intermittent swallowing difficulties, and Raynaud’s phenomenon. Clinical examination revealed early sclerodactyly, and abnormal nail-fold capillaroscopy showed multiple giant capillaries, dilated capillary loops, and areas of capillary hemorrhage with capillary drop-out. The investigation revealed positive ANA, strongly positive SRP antibodies, and Ro60 antibodies. Our case report indicates that there may be a correlation between RCVS and Raynaud’s phenomenon, and a potential connection between RCVS and autoimmune rheumatic diseases. Hence, physicians must be aware of the red flags and subtle differences in neurological abnormalities, such as headaches, in patients with autoimmune rheumatic diseases who have an inactive clinical status to improve patient care and outcomes. Full article
(This article belongs to the Special Issue Imaging and Artificial Intelligence in Rheumatology)
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