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Giraudo C, Sudol-Szopinska I, Fichera G, Evangelista L, Zanatta E, Del Grande F, Stramare R, Bazzocchi A, Guglielmi G, Rennie W. Update on Rheumatic Diseases in Clinical Practice: Recent Concepts and Developments. Radiol Clin North Am 2024; 62:725-738. [PMID: 39059968 DOI: 10.1016/j.rcl.2024.02.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/28/2024]
Abstract
Diagnostic imaging is essential in the diagnostic process of rheumatic diseases. Given the heterogeneity of this group of diseases and the tremendous impact of novel therapeutic options, guidelines and recommendations regarding the optimal choice of the most appropriate technique/s are continuously revised and radiologists should always be up-to-date. Last, because of the continuous technological innovations, we will assist to the progressive application of advanced techniques and tools in rheumatology.
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Affiliation(s)
- Chiara Giraudo
- Department of Cardiac, Thoracic, Vascular Sciences and Public Health - DCTV, University of Padova, Via Giustiniani 2, Padova, 35122, Italy.
| | - Iwona Sudol-Szopinska
- Department of Radiology, National Institute of Geriatrics, Rheumatology and Rehabilitation, 1- Spartanska Street, Warsaw, Poland
| | - Giulia Fichera
- Pediatric Radiology, Padova Hospital, Via Giustiniani 2, Padova, 35122, Italy
| | - Laura Evangelista
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, Milan, 20072, Italy; IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, Milan, 20089, Italy
| | - Elisabetta Zanatta
- Department of Medicine -DIMED, Rheumatology Unit, University of Padova, Via Giustiniani 2, 35122, Padova, Italy
| | - Filippo Del Grande
- Istituto Di Imaging Della Svizzera Italiana (IIMSI), Clinica Di Radiologia Ente Ospedaliero Cantonale (EOC), Via Tesserete 46, Lugano 6900, Switzerland
| | - Roberto Stramare
- Department of Cardiac, Thoracic, Vascular Sciences and Public Health - DCTV, University of Padova, Via Giustiniani 2, Padova, 35122, Italy
| | - Alberto Bazzocchi
- Department of Diagnostic and Interventional Radiology, IRCCS Istituto Ortopedico Rizzoli, Via G. C. Pupilli 1, Bologna, 40136, Italy
| | - Giuseppe Guglielmi
- Department of Clinical and Experimental Medicine, Radiology Unit, Foggia University School of Medicine, Via Gramsci 89, 71122, Foggia, Italy; Department of Radiology, Scientific Institute "Casa Sollievo Della Sofferenza" Hospital, Viale Cappuccini, San Giovanni Rotondo, 71013, Italy
| | - Winston Rennie
- Department of Radiology, Leicester Royal Infirmary, Infirmary Square, Leicester, LE1 5WW, United Kingdom
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Caratsch L, Lechtenboehmer C, Caorsi M, Oung K, Zanchi F, Aleman Y, Walker UA, Omoumi P, Hügle T. Detection and Grading of Radiographic Hand Osteoarthritis Using an Automated Machine Learning Platform. ACR Open Rheumatol 2024; 6:388-395. [PMID: 38576187 PMCID: PMC11168904 DOI: 10.1002/acr2.11665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 01/31/2024] [Accepted: 02/09/2024] [Indexed: 04/06/2024] Open
Abstract
OBJECTIVE Automated machine learning (autoML) platforms allow health care professionals to play an active role in the development of machine learning (ML) algorithms according to scientific or clinical needs. The aim of this study was to develop and evaluate such a model for automated detection and grading of distal hand osteoarthritis (OA). METHODS A total of 13,690 hand radiographs from 2,863 patients within the Swiss Cohort of Quality Management (SCQM) and an external control data set of 346 non-SCQM patients were collected and scored for distal interphalangeal OA (DIP-OA) using the modified Kellgren/Lawrence (K/L) score. Giotto (Learn to Forecast [L2F]) was used as an autoML platform for training two convolutional neural networks for DIP joint extraction and subsequent classification according to the K/L scores. A total of 48,892 DIP joints were extracted and then used to train the classification model. Heatmaps were generated independently of the platform. User experience of a web application as a provisional user interface was investigated by rheumatologists and radiologists. RESULTS The sensitivity and specificity of this model for detecting DIP-OA were 79% and 86%, respectively. The accuracy for grading the correct K/L score was 75%, with a κ score of 0.76. The accuracy per DIP-OA class differed, with 86% for no OA (defined as K/L scores 0 and 1), 71% for a K/L score of 2, 46% for a K/L score of 3, and 67% for a K/L score of 4. Similar values were obtained in an independent external test set. Qualitative and quantitative user experience testing of the web application revealed a moderate to high demand for automated DIP-OA scoring among rheumatologists. Conversely, radiologists expressed a low demand, except for the use of heatmaps. CONCLUSION AutoML platforms are an opportunity to develop clinical end-to-end ML algorithms. Here, automated radiographic DIP-OA detection is both feasible and usable, whereas grading among individual K/L scores (eg, for clinical trials) remains challenging.
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Affiliation(s)
- Leo Caratsch
- Lausanne University Hospital and University of LausanneLausanneSwitzerland
- City Hospital WaidZurichSwitzerland
- L2F (Learn to Forecast)LausanneSwitzerland
| | - Christian Lechtenboehmer
- Lausanne University Hospital and University of LausanneLausanneSwitzerland
- City Hospital WaidZurichSwitzerland
- L2F (Learn to Forecast)LausanneSwitzerland
- University Hospital of BaselBaselSwitzerland
| | | | - Karine Oung
- Lausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Fabio Zanchi
- Lausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Yasser Aleman
- Lausanne University Hospital and University of LausanneLausanneSwitzerland
| | | | - Patrick Omoumi
- Lausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Thomas Hügle
- Lausanne University Hospital and University of LausanneLausanneSwitzerland
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Izumi K, Suzuki K, Hashimoto M, Jinzaki M, Ko S, Takeuchi T, Kaneko Y. Ensemble detection of hand joint ankylosis and subluxation in radiographic images using deep neural networks. Sci Rep 2024; 14:7696. [PMID: 38565576 PMCID: PMC10987556 DOI: 10.1038/s41598-024-58242-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 03/27/2024] [Indexed: 04/04/2024] Open
Abstract
The modified total Sharp score (mTSS) is often used as an evaluation index for joint destruction caused by rheumatoid arthritis. In this study, special findings (ankylosis, subluxation, and dislocation) are detected to estimate the efficacy of mTSS by using deep neural networks (DNNs). The proposed method detects and classifies finger joint regions using an ensemble mechanism. This integrates multiple DNN detection models, specifically single shot multibox detectors, using different training data for each special finding. For the learning phase, we prepared a total of 260 hand X-ray images, in which proximal interphalangeal (PIP) and metacarpophalangeal (MP) joints were annotated with mTSS by skilled rheumatologists and radiologists. We evaluated our model using five-fold cross-validation. The proposed model produced a higher detection accuracy, recall, precision, specificity, F-value, and intersection over union than individual detection models for both ankylosis and subluxation detection, with a detection rate above 99.8% for the MP and PIP joint regions. Our future research will aim at the development of an automatic diagnosis system that uses the proposed mTSS model to estimate the erosion and joint space narrowing score.
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Affiliation(s)
- Keisuke Izumi
- Division of Rheumatology, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan.
- Medical AI Center, Keio University School of Medicine, Tokyo, Japan.
- Division of Rheumatology, Department of Medicine, NHO Tokyo Medical Center, Tokyo, Japan.
| | - Kanata Suzuki
- Medical AI Center, Keio University School of Medicine, Tokyo, Japan
- AI Laboratories, Fujitsu Limited, Kanagawa, Japan
| | - Masahiro Hashimoto
- Medical AI Center, Keio University School of Medicine, Tokyo, Japan
- Department of Radiology, Keio University School of Medicine, Tokyo, Japan
| | - Masahiro Jinzaki
- Medical AI Center, Keio University School of Medicine, Tokyo, Japan
- Department of Radiology, Keio University School of Medicine, Tokyo, Japan
| | - Shigeru Ko
- Medical AI Center, Keio University School of Medicine, Tokyo, Japan
- Department of Systems Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Tsutomu Takeuchi
- Division of Rheumatology, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan
- Medical AI Center, Keio University School of Medicine, Tokyo, Japan
| | - Yuko Kaneko
- Division of Rheumatology, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan
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4
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Stoel BC, Staring M, Reijnierse M, van der Helm-van Mil AHM. Deep learning in rheumatological image interpretation. Nat Rev Rheumatol 2024; 20:182-195. [PMID: 38332242 DOI: 10.1038/s41584-023-01074-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/20/2023] [Indexed: 02/10/2024]
Abstract
Artificial intelligence techniques, specifically deep learning, have already affected daily life in a wide range of areas. Likewise, initial applications have been explored in rheumatology. Deep learning might not easily surpass the accuracy of classic techniques when performing classification or regression on low-dimensional numerical data. With images as input, however, deep learning has become so successful that it has already outperformed the majority of conventional image-processing techniques developed during the past 50 years. As with any new imaging technology, rheumatologists and radiologists need to consider adapting their arsenal of diagnostic, prognostic and monitoring tools, and even their clinical role and collaborations. This adaptation requires a basic understanding of the technical background of deep learning, to efficiently utilize its benefits but also to recognize its drawbacks and pitfalls, as blindly relying on deep learning might be at odds with its capabilities. To facilitate such an understanding, it is necessary to provide an overview of deep-learning techniques for automatic image analysis in detecting, quantifying, predicting and monitoring rheumatic diseases, and of currently published deep-learning applications in radiological imaging for rheumatology, with critical assessment of possible limitations, errors and confounders, and conceivable consequences for rheumatologists and radiologists in clinical practice.
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Affiliation(s)
- Berend C Stoel
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands.
| | - Marius Staring
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Monique Reijnierse
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
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5
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Ma Y, Pan I, Kim SY, Wieschhoff GG, Andriole KP, Mandell JC. Deep learning discrimination of rheumatoid arthritis from osteoarthritis on hand radiography. Skeletal Radiol 2024; 53:377-383. [PMID: 37530866 DOI: 10.1007/s00256-023-04408-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 07/19/2023] [Accepted: 07/19/2023] [Indexed: 08/03/2023]
Abstract
PURPOSE To develop a deep learning model to distinguish rheumatoid arthritis (RA) from osteoarthritis (OA) using hand radiographs and to evaluate the effects of changing pretraining and training parameters on model performance. MATERIALS AND METHODS A convolutional neural network was retrospectively trained on 9714 hand radiograph exams from 8387 patients obtained from 2017 to 2021 at seven hospitals within an integrated healthcare network. Performance was assessed using an independent test set of 250 exams from 146 patients. Binary discriminatory capacity (no arthritis versus arthritis; RA versus not RA) and three-way classification (no arthritis versus OA versus RA) were evaluated. The effects of additional pretraining using musculoskeletal radiographs, using all views as opposed to only the posteroanterior view, and varying image resolution on model performance were also investigated. Area under the receiver operating characteristic curve (AUC) and Cohen's kappa coefficient were used to evaluate diagnostic performance. RESULTS For no arthritis versus arthritis, the model achieved an AUC of 0.975 (95% CI: 0.957, 0.989). For RA versus not RA, the model achieved an AUC of 0.955 (95% CI: 0.919, 0.983). For three-way classification, the model achieved a kappa of 0.806 (95% CI: 0.742, 0.866) and accuracy of 87.2% (95% CI: 83.2%, 91.2%) on the test set. Increasing image resolution increased performance up to 1024 × 1024 pixels. Additional pretraining on musculoskeletal radiographs and using all views did not significantly affect performance. CONCLUSION A deep learning model can be used to distinguish no arthritis, OA, and RA on hand radiographs with high performance.
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Affiliation(s)
- Yuntong Ma
- Department of Radiology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA.
| | - Ian Pan
- Department of Radiology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA
| | - Stanley Y Kim
- Department of Radiology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA
| | - Ged G Wieschhoff
- Department of Radiology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA
| | - Katherine P Andriole
- Department of Radiology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA
- MGH & BWH Center for Clinical Data Science, Suite 1303, 100 Cambridge St, Boston, MA, 02114, USA
| | - Jacob C Mandell
- Department of Radiology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA
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Nicoara AI, Sas LM, Bita CE, Dinescu SC, Vreju FA. Implementation of artificial intelligence models in magnetic resonance imaging with focus on diagnosis of rheumatoid arthritis and axial spondyloarthritis: narrative review. Front Med (Lausanne) 2023; 10:1280266. [PMID: 38173943 PMCID: PMC10761482 DOI: 10.3389/fmed.2023.1280266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Accepted: 12/04/2023] [Indexed: 01/05/2024] Open
Abstract
Early diagnosis in rheumatoid arthritis (RA) and axial spondyloarthritis (axSpA) is essential to initiate timely interventions, such as medication and lifestyle changes, preventing irreversible joint damage, reducing symptoms, and improving long-term outcomes for patients. Since magnetic resonance imaging (MRI) of the wrist and hand, in case of RA and MRI of the sacroiliac joints (SIJ) in case of axSpA can identify inflammation before it is clinically discernible, this modality may be crucial for early diagnosis. Artificial intelligence (AI) techniques, together with machine learning (ML) and deep learning (DL) have quickly evolved in the medical field, having an important role in improving diagnosis, prognosis, in evaluating the effectiveness of treatment and monitoring the activity of rheumatic diseases through MRI. The improvements of AI techniques in the last years regarding imaging interpretation have demonstrated that a computer-based analysis can equal and even exceed the human eye. The studies in the field of AI have investigated how specific algorithms could distinguish between tissues, diagnose rheumatic pathology and grade different signs of early inflammation, all of them being crucial for tracking disease activity. The aim of this paper is to highlight the implementation of AI models in MRI with focus on diagnosis of RA and axSpA through a literature review.
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Affiliation(s)
| | - Lorena-Mihaela Sas
- Radiology and Medical Imaging Laboratory, Craiova Emergency County Clinical Hospital, Craiova, Romania
- Department of Human Anatomy, University of Medicine and Pharmacy of Craiova, Craiova, Romania
| | - Cristina Elena Bita
- Department of Rheumatology, University of Medicine and Pharmacy of Craiova, Craiova, Romania
| | - Stefan Cristian Dinescu
- Department of Rheumatology, University of Medicine and Pharmacy of Craiova, Craiova, Romania
| | - Florentin Ananu Vreju
- Department of Rheumatology, University of Medicine and Pharmacy of Craiova, Craiova, Romania
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Liu L, Zhang Y, Sun L. Medimatrix: innovative pre-training of grayscale images for rheumatoid arthritis diagnosis revolutionises medical image classification. Health Inf Sci Syst 2023; 11:44. [PMID: 37771395 PMCID: PMC10522544 DOI: 10.1007/s13755-023-00246-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 09/08/2023] [Indexed: 09/30/2023] Open
Abstract
Efficient and accurate medical image classification (MIC) methods face two major challenges: (1) high similarity between images of different disease classes; and (2) generating large medical image datasets for training deep neural networks is challenging due to privacy restrictions and the need for expert ground truth annotations. In this paper, we introduce a novel deep learning method called pre-training grayscale images with supervised learning for MIC (MediMatrix). Instead of pre-training on color ImageNet, our approach uses MediMatrix on grayscale ImageNet. To improve the performance of the network, we introduce ShuffleAttention (SA), a self-attention mechanism. By combining SA with the multiple residual structure (ResSA block) and replacing short-cut connections with dense residual connections between corresponding layers (densepath), our network can dynamically adjust channel attention weights and receive image inputs of different sizes, resulting in improved feature representation and better discrimination of similarities between different categories. MediMatrix effectively classifies X-ray images of rheumatoid arthritis (RA), enabling efficient screening without the need for expert analysis or invasive testing. Through extensive experiments, we demonstrate the superiority of MediMatrix over state-of-the-art methods and that color is not critical for rich natural image classification. Our results highlight the potential of computer-aided diagnosis combined with MediMatrix as a valuable screening tool for early detection and intervention in RA.
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Affiliation(s)
- Linchen Liu
- Department of Rheumatology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, 210009 China
| | - Yiyang Zhang
- Department of Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, 210044 China
| | - Le Sun
- Department of Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, 210044 China
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Anttila TT, Aspinen S, Pierides G, Haapamäki V, Laitinen MK, Ryhänen J. Enchondroma Detection from Hand Radiographs with an Interactive Deep Learning Segmentation Tool-A Feasibility Study. J Clin Med 2023; 12:7129. [PMID: 38002741 PMCID: PMC10672653 DOI: 10.3390/jcm12227129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Revised: 11/01/2023] [Accepted: 11/13/2023] [Indexed: 11/26/2023] Open
Abstract
Enchondromas are common benign bone tumors, usually presenting in the hand. They can cause symptoms such as swelling and pain but often go un-noticed. If the tumor expands, it can diminish the bone cortices and predispose the bone to fracture. Diagnosis is based on clinical investigation and radiographic imaging. Despite their typical appearance on radiographs, they can primarily be misdiagnosed or go totally unrecognized in the acute trauma setting. Earlier applications of deep learning models to image classification and pattern recognition suggest that this technique may also be utilized in detecting enchondroma in hand radiographs. We trained a deep learning model with 414 enchondroma radiographs to detect enchondroma from hand radiographs. A separate test set of 131 radiographs (47% with an enchondroma) was used to assess the performance of the trained deep learning model. Enchondroma annotation by three clinical experts served as our ground truth in assessing the deep learning model's performance. Our deep learning model detected 56 enchondromas from the 62 enchondroma radiographs. The area under receiver operator curve was 0.95. The F1 score for area statistical overlapping was 69.5%. Our deep learning model may be a useful tool for radiograph screening and raising suspicion of enchondroma.
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Affiliation(s)
- Turkka Tapio Anttila
- Musculoskeletal and Plastic Surgery, Department of Hand Surgery, University of Helsinki and Helsinki University Hospital, 00029 Helsinki, Finland
| | - Samuli Aspinen
- Musculoskeletal and Plastic Surgery, Department of Hand Surgery, University of Helsinki and Helsinki University Hospital, 00029 Helsinki, Finland
| | - Georgios Pierides
- Musculoskeletal and Plastic Surgery, Department of Hand Surgery, University of Helsinki and Helsinki University Hospital, 00029 Helsinki, Finland
| | - Ville Haapamäki
- Department of Radiology, University of Helsinki and Helsinki University Hospital, 00029 Helsinki, Finland
| | - Minna Katariina Laitinen
- Musculoskeletal and Plastic Surgery, Department of Orthopedic Surgery, University of Helsinki and Helsinki University Hospital, 00029 Helsinki, Finland
| | - Jorma Ryhänen
- Musculoskeletal and Plastic Surgery, Department of Hand Surgery, University of Helsinki and Helsinki University Hospital, 00029 Helsinki, Finland
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Gilvaz VJ, Reginato AM. Artificial intelligence in rheumatoid arthritis: potential applications and future implications. Front Med (Lausanne) 2023; 10:1280312. [PMID: 38034534 PMCID: PMC10687464 DOI: 10.3389/fmed.2023.1280312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 10/13/2023] [Indexed: 12/02/2023] Open
Abstract
The widespread adoption of digital health records, coupled with the rise of advanced diagnostic testing, has resulted in an explosion of patient data, comparable in scope to genomic datasets. This vast information repository offers significant potential for improving patient outcomes and decision-making, provided one can extract meaningful insights from it. This is where artificial intelligence (AI) tools like machine learning (ML) and deep learning come into play, helping us leverage these enormous datasets to predict outcomes and make informed decisions. AI models can be trained to analyze and interpret patient data, including physician notes, laboratory testing, and imaging, to aid in the management of patients with rheumatic diseases. As one of the most common autoimmune diseases, rheumatoid arthritis (RA) has attracted considerable attention, particularly concerning the evolution of diagnostic techniques and therapeutic interventions. Our aim is to underscore those areas where AI, according to recent research, demonstrates promising potential to enhance the management of patients with RA.
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Affiliation(s)
- Vinit J. Gilvaz
- Division of Rheumatology, Department of Medicine, Rhode Island Hospital, Warren Alpert Medical School of Brown University, Providence, RI, United States
| | - Anthony M. Reginato
- Division of Rheumatology, Department of Medicine, Rhode Island Hospital, Warren Alpert Medical School of Brown University, Providence, RI, United States
- Department of Dermatology, Rhode Island Hospital, Warren Alpert Medical School of Brown University, Providence, RI, United States
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Phatak S, Chakraborty S, Goel P. Computer vision detects inflammatory arthritis in standardized smartphone photographs in an Indian patient cohort. Front Med (Lausanne) 2023; 10:1280462. [PMID: 38020147 PMCID: PMC10666644 DOI: 10.3389/fmed.2023.1280462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 10/12/2023] [Indexed: 12/01/2023] Open
Abstract
Introduction Computer vision extracts meaning from pixelated images and holds promise in automating various clinical tasks. Convolutional neural networks (CNNs), a deep learning network used therein, have shown promise in analyzing X-ray images and joint photographs. We studied the performance of a CNN on standardized smartphone photographs in detecting inflammation in three hand joints and compared it to a rheumatologist's diagnosis. Methods We enrolled 100 consecutive patients with inflammatory arthritis with an onset period of less than 2 years, excluding those with deformities. Each patient was examined by a rheumatologist, and the presence of synovitis in each joint was recorded. Hand photographs were taken in a standardized manner, anonymized, and cropped to include joints of interest. A ResNet-101 backbone modified for two class outputs (inflamed or not) was used for training. We also tested a hue-augmented dataset. We reported accuracy, sensitivity, and specificity for three joints: wrist, index finger proximal interphalangeal (IFPIP), and middle finger proximal interphalangeal (MFPIP), taking the rheumatologist's opinion as the gold standard. Results The cohort consisted of 100 individuals, of which 22 of them were men, with a mean age of 49.7 (SD 12.9) years. The majority of the cohort (n = 68, 68%) had rheumatoid arthritis. The wrist (125/200, 62.5%), MFPIP (94/200, 47%), and IFPIP (83/200, 41.5%) were the three most commonly inflamed joints. The CNN achieved the highest accuracy, sensitivity, and specificity in detecting synovitis in the MFPIP (83, 77, and 88%, respectively), followed by the IFPIP (74, 74, and 75%, respectively) and the wrist (62, 90, and 21%, respectively). Discussion We have demonstrated that computer vision was able to detect inflammation in three joints of the hand with reasonable accuracy on standardized photographs despite a small dataset. Feature engineering was not required, and the CNN worked despite a diversity in clinical diagnosis. Larger datasets are likely to improve accuracy and help explain the basis of classification. These data suggest a potential use of computer vision in screening and follow-up of inflammatory arthritis.
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Affiliation(s)
| | | | - Pranay Goel
- Indian Institute of Science, Education and Research, Pune, India
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11
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Okita Y, Hirano T, Wang B, Nakashima Y, Minoda S, Nagahara H, Kumanogoh A. Automatic evaluation of atlantoaxial subluxation in rheumatoid arthritis by a deep learning model. Arthritis Res Ther 2023; 25:181. [PMID: 37749583 PMCID: PMC10518918 DOI: 10.1186/s13075-023-03172-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 09/13/2023] [Indexed: 09/27/2023] Open
Abstract
BACKGROUND This work aims to develop a deep learning model, assessing atlantoaxial subluxation (AAS) in rheumatoid arthritis (RA), which can often be ambiguous in clinical practice. METHODS We collected 4691 X-ray images of the cervical spine of the 906 patients with RA. Among these images, 3480 were used for training the deep learning model, 803 were used for validating the model during the training process, and the remaining 408 were used for testing the performance of the trained model. The two-dimensional key points' detection model of Deep High-Resolution Representation Learning for Human Pose Estimation was adopted as the base convolutional neural network model. The model inferred four coordinates to calculate the atlantodental interval (ADI) and space available for the spinal cord (SAC). Finally, these values were compared with those by clinicians to evaluate the performance of the model. RESULTS Among the 408 cervical images for testing the performance, the trained model correctly identified the four coordinates in 99.5% of the dataset. The values of ADI and SAC were positively correlated among the model and two clinicians. The sensitivity of AAS diagnosis with ADI or SAC by the model was 0.86 and 0.97 respectively. The specificity of that was 0.57 and 0.5 respectively. CONCLUSIONS We present the development of a deep learning model for the evaluation of cervical lesions of patients with RA. The model was demonstrably shown to be useful for quantitative evaluation.
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Affiliation(s)
- Yasutaka Okita
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan.
| | - Toru Hirano
- Department of Rheumatology, Nishinomiya Municipal Central Hospital, Hyogo, Japan
| | - Bowen Wang
- Osaka University Institute for Datability Science (IDS), Suita, Osaka, Japan
| | - Yuta Nakashima
- Osaka University Institute for Datability Science (IDS), Suita, Osaka, Japan
| | - Saki Minoda
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Hajime Nagahara
- Osaka University Institute for Datability Science (IDS), Suita, Osaka, Japan
| | - Atsushi Kumanogoh
- Department of Respiratory Medicine and Clinical Immunology, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan
- Laboratory of Immunopathology, World Premier International Immunology Frontier Research Center, Osaka University, Suita, Osaka, Japan
- The Institute for Open and Transdisciplinary Research Initiatives (OTRI), Osaka, Japan
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Wang H, Ou Y, Fang W, Ambalathankandy P, Goto N, Ota G, Okino T, Fukae J, Sutherland K, Ikebe M, Kamishima T. A deep registration method for accurate quantification of joint space narrowing progression in rheumatoid arthritis. Comput Med Imaging Graph 2023; 108:102273. [PMID: 37531811 DOI: 10.1016/j.compmedimag.2023.102273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 07/15/2023] [Accepted: 07/15/2023] [Indexed: 08/04/2023]
Abstract
Rheumatoid arthritis (RA) is a chronic autoimmune inflammatory disease that leads to progressive articular destruction and severe disability. Joint space narrowing (JSN) has been regarded as an important indicator for RA progression and has received significant attention. Radiology plays a crucial role in the diagnosis and monitoring of RA through the assessment of joint space. A new framework for monitoring joint space by quantifying joint space narrowing (JSN) progression through image registration in radiographic images has emerged as a promising research direction. This framework offers the advantage of high accuracy; however, challenges still exist in reducing mismatches and improving reliability. In this work, we utilize a deep intra-subject rigid registration network to automatically quantify JSN progression in the early stages of RA. In our experiments, the mean-square error of the Euclidean distance between the moving and fixed images was 0.0031, the standard deviation was 0.0661 mm and the mismatching rate was 0.48%. Our method achieves sub-pixel level accuracy, surpassing manual measurements significantly. The proposed method is robust to noise, rotation and scaling of joints. Moreover, it provides misalignment visualization, which can assist radiologists and rheumatologists in assessing the reliability of quantification, exhibiting potential for future clinical applications. As a result, we are optimistic that our proposed method will make a significant contribution to the automatic quantification of JSN progression in RA. Code is available at https://github.com/pokeblow/Deep-Registration-QJSN-Finger.git.
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Affiliation(s)
- Haolin Wang
- Graduate School of Health Sciences, Hokkaido University, Sapporo, 060-0812, Hokkaido, Japan
| | - Yafei Ou
- Research Center For Integrated Quantum Electronics, Hokkaido University, Sapporo, 060-0813, Hokkaido, Japan; Graduate School of Information Science and Technology, Hokkaido University, Sapporo, 060-0813, Hokkaido, Japan.
| | - Wanxuan Fang
- Graduate School of Health Sciences, Hokkaido University, Sapporo, 060-0812, Hokkaido, Japan
| | - Prasoon Ambalathankandy
- Research Center For Integrated Quantum Electronics, Hokkaido University, Sapporo, 060-0813, Hokkaido, Japan; Graduate School of Information Science and Technology, Hokkaido University, Sapporo, 060-0813, Hokkaido, Japan
| | - Naoto Goto
- Research Center For Integrated Quantum Electronics, Hokkaido University, Sapporo, 060-0813, Hokkaido, Japan; Graduate School of Information Science and Technology, Hokkaido University, Sapporo, 060-0813, Hokkaido, Japan
| | - Gen Ota
- Research Center For Integrated Quantum Electronics, Hokkaido University, Sapporo, 060-0813, Hokkaido, Japan; Graduate School of Information Science and Technology, Hokkaido University, Sapporo, 060-0813, Hokkaido, Japan
| | - Taichi Okino
- Department of Radiological Technology, Sapporo City General Hospital, Sapporo, 060-8604, Hokkaido, Japan
| | - Jun Fukae
- Kuriyama Red Cross Hospital, Yubari, 069-1513, Hokkaido, Japan
| | - Kenneth Sutherland
- Global Center for Biomedical Science and Engineering, Hokkaido University, Sapporo, 060-8638, Hokkaido, Japan
| | - Masayuki Ikebe
- Research Center For Integrated Quantum Electronics, Hokkaido University, Sapporo, 060-0813, Hokkaido, Japan; Graduate School of Information Science and Technology, Hokkaido University, Sapporo, 060-0813, Hokkaido, Japan
| | - Tamotsu Kamishima
- Faculty of Health Sciences, Hokkaido University, Sapporo, 060-0812, Hokkaido, Japan
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13
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Parashar A, Rishi R, Parashar A, Rida I. Medical imaging in rheumatoid arthritis: A review on deep learning approach. Open Life Sci 2023; 18:20220611. [PMID: 37426615 PMCID: PMC10329279 DOI: 10.1515/biol-2022-0611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 07/11/2023] Open
Abstract
Arthritis is a musculoskeletal disorder. Millions of people have arthritis, making it one of the most common joint disorders. Osteoarthritis (OA) and rheumatoid arthritis (RA) are the most common types of arthritis among the many different types available. Pain, stiffness, and inflammation are among the early signs of arthritis, which can progress to severe immobility at a later stage if left untreated. Although arthritis cannot be cured at any point in time, it can be managed if diagnosed and treated correctly. Clinical diagnostic and medical imaging methods are currently used to evaluate OA and RA, both debilitating conditions. This review is focused on deep learning approaches used by taking medical imaging (X-rays and magnetic resonance imaging) as input for the detection of RA.
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Affiliation(s)
- Apoorva Parashar
- Department of Computer Science and Engineering, Maharshi Dayanand University, Rohtak, India
| | - Rahul Rishi
- Department of Computer Science and Engineering, Maharshi Dayanand University, Rohtak, India
| | - Anubha Parashar
- Department of Computer Science and Engineering, Manipal UniversityJaipur, India
| | - Imad Rida
- BMBI Laboratory, University of Technology of Compiègne, 60200, Compiègne, France
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14
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Wang J, Tian Y, Zhou T, Tong D, Ma J, Li J. A survey of artificial intelligence in rheumatoid arthritis. RHEUMATOLOGY AND IMMUNOLOGY RESEARCH 2023; 4:69-77. [PMID: 37485476 PMCID: PMC10362600 DOI: 10.2478/rir-2023-0011] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 06/14/2023] [Indexed: 07/25/2023]
Abstract
The article offers a survey of currently notable artificial intelligence methods (released between 2019-2023), with a particular emphasis on the latest advancements in detecting rheumatoid arthritis (RA) at an early stage, providing early treatment, and managing the disease. We discussed challenges in these areas followed by specific artificial intelligence (AI) techniques and summarized advances, relevant strengths, and obstacles. Overall, the application of AI in the fields of RA has the potential to enable healthcare professionals to detect RA at an earlier stage, thereby facilitating timely intervention and better disease management. However, more research is required to confirm the precision and dependability of AI in RA, and several problems such as technological and ethical concerns related to these approaches must be resolved before their widespread adoption.
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Affiliation(s)
- Jiaqi Wang
- Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou311121, Zhejiang Province, China
| | - Yu Tian
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou310027, Zhejiang Province, China
| | - Tianshu Zhou
- Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou311121, Zhejiang Province, China
| | - Danyang Tong
- Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou311121, Zhejiang Province, China
| | - Jing Ma
- Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou311121, Zhejiang Province, China
| | - Jingsong Li
- Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou311121, Zhejiang Province, China
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou310027, Zhejiang Province, China
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15
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Srinivasan S, Gunasekaran S, Mathivanan SK, Jayagopal P, Khan MA, Alasiry A, Marzougui M, Masood A. A Framework of Faster CRNN and VGG16-Enhanced Region Proposal Network for Detection and Grade Classification of Knee RA. Diagnostics (Basel) 2023; 13:diagnostics13081385. [PMID: 37189485 DOI: 10.3390/diagnostics13081385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 03/29/2023] [Accepted: 04/04/2023] [Indexed: 05/17/2023] Open
Abstract
We developed a framework to detect and grade knee RA using digital X-radiation images and used it to demonstrate the ability of deep learning approaches to detect knee RA using a consensus-based decision (CBD) grading system. The study aimed to evaluate the efficiency with which a deep learning approach based on artificial intelligence (AI) can find and determine the severity of knee RA in digital X-radiation images. The study comprised people over 50 years with RA symptoms, such as knee joint pain, stiffness, crepitus, and functional impairments. The digitized X-radiation images of the people were obtained from the BioGPS database repository. We used 3172 digital X-radiation images of the knee joint from an anterior-posterior perspective. The trained Faster-CRNN architecture was used to identify the knee joint space narrowing (JSN) area in digital X-radiation images and extract the features using ResNet-101 with domain adaptation. In addition, we employed another well-trained model (VGG16 with domain adaptation) for knee RA severity classification. Medical experts graded the X-radiation images of the knee joint using a consensus-based decision score. We trained the enhanced-region proposal network (ERPN) using this manually extracted knee area as the test dataset image. An X-radiation image was fed into the final model, and a consensus decision was used to grade the outcome. The presented model correctly identified the marginal knee JSN region with 98.97% of accuracy, with a total knee RA intensity classification accuracy of 99.10%, with a sensitivity of 97.3%, a specificity of 98.2%, a precision of 98.1%, and a dice score of 90.1% compared with other conventional models.
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Affiliation(s)
- Saravanan Srinivasan
- Department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai 600062, India
| | - Subathra Gunasekaran
- Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai 600119, India
| | - Sandeep Kumar Mathivanan
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Prabhu Jayagopal
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | | | - Areej Alasiry
- College of Computer Science, King Khalid University, Abha 61413, Saudi Arabia
| | - Mehrez Marzougui
- College of Computer Science, King Khalid University, Abha 61413, Saudi Arabia
- Electronics and Micro-Electronics Laboratory, Faculty of Sciences, University of Monastir, Monastir 5000, Tunisia
| | - Anum Masood
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), 7034 Trondheim, Norway
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16
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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. Diagnostics (Basel) 2022; 13:diagnostics13010104. [PMID: 36611395 PMCID: PMC9818241 DOI: 10.3390/diagnostics13010104] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 12/08/2022] [Accepted: 12/28/2022] [Indexed: 12/31/2022] Open
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 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.
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17
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D'Angelo T, Caudo D, Blandino A, Albrecht MH, Vogl TJ, Gruenewald LD, Gaeta M, Yel I, Koch V, Martin SS, Lenga L, Muscogiuri G, Sironi S, Mazziotti S, Booz C. Artificial intelligence, machine learning and deep learning in musculoskeletal imaging: Current applications. JOURNAL OF CLINICAL ULTRASOUND : JCU 2022; 50:1414-1431. [PMID: 36069404 DOI: 10.1002/jcu.23321] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 08/18/2022] [Accepted: 08/20/2022] [Indexed: 06/15/2023]
Abstract
Artificial intelligence is rapidly expanding in all technological fields. The medical field, and especially diagnostic imaging, has been showing the highest developmental potential. Artificial intelligence aims at human intelligence simulation through the management of complex problems. This review describes the technical background of artificial intelligence, machine learning, and deep learning. The first section illustrates the general potential of artificial intelligence applications in the context of request management, data acquisition, image reconstruction, archiving, and communication systems. In the second section, the prospective of dedicated tools for segmentation, lesion detection, automatic diagnosis, and classification of musculoskeletal disorders is discussed.
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Affiliation(s)
- Tommaso D'Angelo
- Department of Biomedical Sciences and Morphological and Functional Imaging, University Hospital Messina, Messina, Italy
- Department of Radiology and Nuclear Medicine, Rotterdam, Netherlands
| | - Danilo Caudo
- Department of Biomedical Sciences and Morphological and Functional Imaging, University Hospital Messina, Messina, Italy
- Department or Radiology, IRRCS Centro Neurolesi "Bonino Pulejo", Messina, Italy
| | - Alfredo Blandino
- Department of Biomedical Sciences and Morphological and Functional Imaging, University Hospital Messina, Messina, Italy
| | - Moritz H Albrecht
- Division of Experimental Imaging, Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Thomas J Vogl
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Leon D Gruenewald
- Division of Experimental Imaging, Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Michele Gaeta
- Department of Biomedical Sciences and Morphological and Functional Imaging, University Hospital Messina, Messina, Italy
| | - Ibrahim Yel
- Division of Experimental Imaging, Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Vitali Koch
- Division of Experimental Imaging, Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Simon S Martin
- Division of Experimental Imaging, Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Lukas Lenga
- Division of Experimental Imaging, Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Giuseppe Muscogiuri
- School of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy
- Department of Radiology, IRCCS Istituto Auxologico Italiano, San Luca Hospital, Milan, Italy
| | - Sandro Sironi
- School of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy
- Department of Radiology, ASST Papa Giovanni XXIII Hospital, Bergamo, Italy
| | - Silvio Mazziotti
- Department of Biomedical Sciences and Morphological and Functional Imaging, University Hospital Messina, Messina, Italy
| | - Christian Booz
- Division of Experimental Imaging, Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany
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18
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Miyama K, Bise R, Ikemura S, Kai K, Kanahori M, Arisumi S, Uchida T, Nakashima Y, Uchida S. Deep learning-based automatic-bone-destruction-evaluation system using contextual information from other joints. Arthritis Res Ther 2022; 24:227. [PMID: 36192761 PMCID: PMC9528108 DOI: 10.1186/s13075-022-02914-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 09/25/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND X-ray images are commonly used to assess the bone destruction of rheumatoid arthritis. The purpose of this study is to propose an automatic-bone-destruction-evaluation system fully utilizing deep neural networks (DNN). This system detects all target joints of the modified Sharp/van der Heijde score (SHS) from a hand X-ray image. It then classifies every target joint as intact (SHS = 0) or non-intact (SHS ≥ 1). METHODS We used 226 hand X-ray images of 40 rheumatoid arthritis patients. As for detection, we used a DNN model called DeepLabCut. As for classification, we built four classification models that classify the detected joint as intact or non-intact. The first model classifies each joint independently, whereas the second model does it while comparing the same contralateral joint. The third model compares the same joint group (e.g., the proximal interphalangeal joints) of one hand and the fourth model compares the same joint group of both hands. We evaluated DeepLabCut's detection performance and classification models' performances. The classification models' performances were compared to three orthopedic surgeons. RESULTS Detection rates for all the target joints were 98.0% and 97.3% for erosion and joint space narrowing (JSN). Among the four classification models, the model that compares the same contralateral joint showed the best F-measure (0.70, 0.81) and area under the curve of the precision-recall curve (PR-AUC) (0.73, 0.85) regarding erosion and JSN. As for erosion, the F-measure and PR-AUC of this model were better than the best of the orthopedic surgeons. CONCLUSIONS The proposed system was useful. All the target joints were detected with high accuracy. The classification model that compared the same contralateral joint showed better performance than the orthopedic surgeons regarding erosion.
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Affiliation(s)
- Kazuki Miyama
- Department of Orthopaedic Surgery, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan. .,Department of Advanced Information Technology, Kyushu University, 744 Motooka, Nishi-Ku, Fukuoka, 819-0395, Japan.
| | - Ryoma Bise
- Department of Advanced Information Technology, Kyushu University, 744 Motooka, Nishi-Ku, Fukuoka, 819-0395, Japan
| | - Satoshi Ikemura
- Department of Orthopaedic Surgery, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Kazuhiro Kai
- Department of Orthopaedic Surgery, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Masaya Kanahori
- Department of Orthopaedic Surgery, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Shinkichi Arisumi
- Department of Orthopaedic Surgery, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Taisuke Uchida
- Department of Orthopaedic Surgery, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Yasuharu Nakashima
- Department of Orthopaedic Surgery, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Seiichi Uchida
- Department of Advanced Information Technology, Kyushu University, 744 Motooka, Nishi-Ku, Fukuoka, 819-0395, Japan
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19
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Momtazmanesh S, Nowroozi A, Rezaei N. Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review. Rheumatol Ther 2022; 9:1249-1304. [PMID: 35849321 PMCID: PMC9510088 DOI: 10.1007/s40744-022-00475-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 06/24/2022] [Indexed: 11/23/2022] Open
Abstract
Investigation of the potential applications of artificial intelligence (AI), including machine learning (ML) and deep learning (DL) techniques, is an exponentially growing field in medicine and healthcare. These methods can be critical in providing high-quality care to patients with chronic rheumatological diseases lacking an optimal treatment, like rheumatoid arthritis (RA), which is the second most prevalent autoimmune disease. Herein, following reviewing the basic concepts of AI, we summarize the advances in its applications in RA clinical practice and research. We provide directions for future investigations in this field after reviewing the current knowledge gaps and technical and ethical challenges in applying AI. Automated models have been largely used to improve RA diagnosis since the early 2000s, and they have used a wide variety of techniques, e.g., support vector machine, random forest, and artificial neural networks. AI algorithms can facilitate screening and identification of susceptible groups, diagnosis using omics, imaging, clinical, and sensor data, patient detection within electronic health record (EHR), i.e., phenotyping, treatment response assessment, monitoring disease course, determining prognosis, novel drug discovery, and enhancing basic science research. They can also aid in risk assessment for incidence of comorbidities, e.g., cardiovascular diseases, in patients with RA. However, the proposed models may vary significantly in their performance and reliability. Despite the promising results achieved by AI models in enhancing early diagnosis and management of patients with RA, they are not fully ready to be incorporated into clinical practice. Future investigations are required to ensure development of reliable and generalizable algorithms while they carefully look for any potential source of bias or misconduct. We showed that a growing body of evidence supports the potential role of AI in revolutionizing screening, diagnosis, and management of patients with RA. However, multiple obstacles hinder clinical applications of AI models. Incorporating the machine and/or deep learning algorithms into real-world settings would be a key step in the progress of AI in medicine.
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Affiliation(s)
- Sara Momtazmanesh
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.,Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Tehran, Iran.,Research Center for Immunodeficiencies, Pediatrics Center of Excellence, Children's Medical Center, Tehran University of Medical Sciences, Dr. Gharib St, Keshavarz Blvd, Tehran, Iran
| | - Ali Nowroozi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.,Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Nima Rezaei
- Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Tehran, Iran. .,Research Center for Immunodeficiencies, Pediatrics Center of Excellence, Children's Medical Center, Tehran University of Medical Sciences, Dr. Gharib St, Keshavarz Blvd, Tehran, Iran. .,Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
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20
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Deep Learning-Based Computer-Aided Diagnosis of Rheumatoid Arthritis with Hand X-ray Images Conforming to Modified Total Sharp/van der Heijde Score. Biomedicines 2022; 10:biomedicines10061355. [PMID: 35740376 PMCID: PMC9220074 DOI: 10.3390/biomedicines10061355] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 06/01/2022] [Accepted: 06/06/2022] [Indexed: 11/29/2022] Open
Abstract
Introduction: Rheumatoid arthritis (RA) is a systemic autoimmune disease; early diagnosis and treatment are crucial for its management. Currently, the modified total Sharp score (mTSS) is widely used as a scoring system for RA. The standard screening process for assessing mTSS is tedious and time-consuming. Therefore, developing an efficient mTSS automatic localization and classification system is of urgent need for RA diagnosis. Current research mostly focuses on the classification of finger joints. Due to the insufficient detection ability of the carpal part, these methods cannot cover all the diagnostic needs of mTSS. Method: We propose not only an automatic label system leveraging the You Only Look Once (YOLO) model to detect the regions of joints of the two hands in hand X-ray images for preprocessing of joint space narrowing in mTSS, but also a joint classification model depending on the severity of the mTSS-based disease. In the image processing of the data, the window level is used to simulate the processing method of the clinician, the training data of the different carpal and finger bones of human vision are separated and integrated, and the resolution is increased or decreased to observe the changes in the accuracy of the model. Results: Integrated data proved to be beneficial. The mean average precision of the proposed model in joint detection of joint space narrowing reached 0.92, and the precision, recall, and F1 score all reached 0.94 to 0.95. For the joint classification, the average accuracy was 0.88, and the accuracy of severe, mild, and healthy reached 0.91, 0.79, and 0.9, respectively. Conclusions: The proposed model is feasible and efficient. It could be helpful for subsequent research on computer-aided diagnosis in RA. We suggest that applying the one-hand X-ray imaging protocol can improve the accuracy of mTSS classification model in determining mild disease if it is used in clinical practice.
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21
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Bird A, Oakden-Rayner L, McMaster C, Smith LA, Zeng M, Wechalekar MD, Ray S, Proudman S, Palmer LJ. Artificial intelligence and the future of radiographic scoring in rheumatoid arthritis: a viewpoint. Arthritis Res Ther 2022; 24:268. [PMID: 36510330 PMCID: PMC9743640 DOI: 10.1186/s13075-022-02972-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 12/03/2022] [Indexed: 12/14/2022] Open
Abstract
Rheumatoid arthritis is an autoimmune condition that predominantly affects the synovial joints, causing joint destruction, pain, and disability. Historically, the standard for measuring the long-term efficacy of disease-modifying antirheumatic drugs has been the assessment of plain radiographs with scoring techniques that quantify joint damage. However, with significant improvements in therapy, current radiographic scoring systems may no longer be fit for purpose for the milder spectrum of disease seen today. We argue that artificial intelligence is an apt solution to further improve upon radiographic scoring, as it can readily learn to recognize subtle patterns in imaging data to not only improve efficiency, but can also increase the sensitivity to variation in mild disease. Current work in the area demonstrates the feasibility of automating scoring but is yet to take full advantage of the strengths of artificial intelligence. By fully leveraging the power of artificial intelligence, faster and more sensitive scoring could enable the ongoing development of effective treatments for patients with rheumatoid arthritis.
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Affiliation(s)
- Alix Bird
- grid.1010.00000 0004 1936 7304Australian Institute of Machine Learning, University of Adelaide, Corner Frome Road and North Terrace, Adelaide, SA 5000 Australia ,grid.1010.00000 0004 1936 7304School of Public Health, The University of Adelaide, North Terrace, Adelaide, SA 5000 Australia
| | - Lauren Oakden-Rayner
- grid.1010.00000 0004 1936 7304Australian Institute of Machine Learning, University of Adelaide, Corner Frome Road and North Terrace, Adelaide, SA 5000 Australia ,grid.1010.00000 0004 1936 7304School of Public Health, The University of Adelaide, North Terrace, Adelaide, SA 5000 Australia
| | - Christopher McMaster
- grid.410678.c0000 0000 9374 3516Department of Rheumatology, Austin Health, Heidelberg, VIC 3084 Australia
| | - Luke A. Smith
- grid.1010.00000 0004 1936 7304Australian Institute of Machine Learning, University of Adelaide, Corner Frome Road and North Terrace, Adelaide, SA 5000 Australia ,grid.1010.00000 0004 1936 7304School of Public Health, The University of Adelaide, North Terrace, Adelaide, SA 5000 Australia
| | - Minyan Zeng
- grid.1010.00000 0004 1936 7304Australian Institute of Machine Learning, University of Adelaide, Corner Frome Road and North Terrace, Adelaide, SA 5000 Australia ,grid.1010.00000 0004 1936 7304School of Public Health, The University of Adelaide, North Terrace, Adelaide, SA 5000 Australia
| | - Mihir D. Wechalekar
- grid.1014.40000 0004 0367 2697Department of Rheumatology, Flinders Medical Centre, and College of Medicine and Public Health, Flinders University, Bedford Park, SA 5042 Australia
| | - Shonket Ray
- grid.418019.50000 0004 0393 4335Artificial Intelligence and Machine Learning, GlaxoSmithKline, South San Francisco, CA USA
| | - Susanna Proudman
- grid.416075.10000 0004 0367 1221Department of Rheumatology, Royal Adelaide Hospital, Adelaide, SA 5000 Australia
| | - Lyle J. Palmer
- grid.1010.00000 0004 1936 7304Australian Institute of Machine Learning, University of Adelaide, Corner Frome Road and North Terrace, Adelaide, SA 5000 Australia ,grid.1010.00000 0004 1936 7304School of Public Health, The University of Adelaide, North Terrace, Adelaide, SA 5000 Australia
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22
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Abstract
Rheumatoid arthritis (RA) is a systemic autoimmune disease that preferably affects small joints. As the well-timed diagnosis of the disease is essential for the treatment of the patient, several works have been conducted in the field of deep learning to develop fast and accurate automatic methods for RA diagnosis. These works mainly focus on medical images as they use X-ray and ultrasound images as input for their models. In this study, we review the conducted works and compare the methods that use deep learning with the procedure that is commonly followed by a medical doctor for the RA diagnosis. The results show that 93% of the works use only image modalities as input for the models as distinct from the medical procedure where more patient medical data are taken into account. Moreover, only 15% of the works use direct explainability methods, meaning that the efforts for solving the trustworthiness issue of deep learning models were limited. In this context, this work reveals the gap between the deep learning approaches and the medical doctors’ practices traditionally applied and brings to light the weaknesses of the current deep learning technology to be integrated into a trustworthy context inside the existed medical infrastructures.
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More S, Singla J. A generalized deep learning framework for automatic rheumatoid arthritis severity grading. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-212015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Knee rheumatoid arthritis (RA) is the highly prevalent, chronic, progressive condition in the world. To diagnose this disease in the early stage in detail analysis with magnetic resonance (MR) image is possible. The imaging modality feature allows unbiased assessment of joint space narrowing (JSN), cartilage volume, and other vital features. This provides a fine-grained RA severity evaluation of the knee, contrasted to the benchmark, and generally used Kellgren Lawrence (KL) assessment. In this research, an intelligent system is developed to predict KL grade from the knee dataset. Our approach is based on hybrid deep learning of 50 layers (ResNet50) with skip connections. The proposed approach also uses Adam optimizer to provide learning linearity in the training stage. Our approach yields KL grade and JSN for femoral and tibial tissue with lateral and medial compartments. Furthermore, the approach also yields area under curve (AUC) of 0.98, accuracy 96.85%, mean absolute error (MAE) 0.015, precision 98.31%, and other commonly used parameters for the existence of radiographic RA progression which is improved than the existing state-of-the-art.
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Affiliation(s)
- Sujeet More
- School of Computer Science and Engineering, Lovely Professional University, Jalandhar, Punjab, India
| | - Jimmy Singla
- School of Computer Science and Engineering, Lovely Professional University, Jalandhar, Punjab, India
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24
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Kedra J, Davergne T, Braithwaite B, Servy H, Gossec L. Machine learning approaches to improve disease management of patients with rheumatoid arthritis: review and future directions. Expert Rev Clin Immunol 2021; 17:1311-1321. [PMID: 34890271 DOI: 10.1080/1744666x.2022.2017773] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
INTRODUCTION Although the management of rheumatoid arthritis (RA) has improved in major way over the last decades, this disease still leads to an important burden for patients and society, and there is a need to develop more personalized approaches. Machine learning (ML) methods are more and more used in health-related studies and can be applied to different sorts of data (clinical, radiological, or 'omics' data). Such approaches may improve the management of patients with RA. AREAS COVERED In this paper, we propose a review regarding ML approaches applied to RA. A scoping literature search was performed in PubMed, in September 2021 using the following MeSH terms: 'arthritis, rheumatoid' and 'machine learning'. Based on this search, the usefulness of ML methods for RA diagnosis, monitoring, and prediction of response to treatment and RA outcomes, is discussed. EXPERT OPINION ML methods have the potential to revolutionize RA-related research and improve disease management and patient care. Nevertheless, these models are not yet ready to contribute fully to rheumatologists' daily practice. Indeed, these methods raise technical, methodological, and ethical issues, which should be addressed properly to allow their implementation. Collaboration between data scientists, clinical researchers, and physicians is therefore required to move this field forward.
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Affiliation(s)
- Joanna Kedra
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, Paris, France.,Rheumatology Department, Pitié-Salpêtrière Hospital, AP-HP, Paris, France
| | - Thomas Davergne
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, Paris, France
| | | | | | - Laure Gossec
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, Paris, France.,Rheumatology Department, Pitié-Salpêtrière Hospital, AP-HP, Paris, France
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25
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Wassan JT, Zheng H, Wang H. Role of Deep Learning in Predicting Aging-Related Diseases: A Scoping Review. Cells 2021; 10:cells10112924. [PMID: 34831148 PMCID: PMC8616301 DOI: 10.3390/cells10112924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 10/22/2021] [Accepted: 10/26/2021] [Indexed: 11/16/2022] Open
Abstract
Aging refers to progressive physiological changes in a cell, an organ, or the whole body of an individual, over time. Aging-related diseases are highly prevalent and could impact an individual’s physical health. Recently, artificial intelligence (AI) methods have been used to predict aging-related diseases and issues, aiding clinical providers in decision-making based on patient’s medical records. Deep learning (DL), as one of the most recent generations of AI technologies, has embraced rapid progress in the early prediction and classification of aging-related issues. In this paper, a scoping review of publications using DL approaches to predict common aging-related diseases (such as age-related macular degeneration, cardiovascular and respiratory diseases, arthritis, Alzheimer’s and lifestyle patterns related to disease progression), was performed. Google Scholar, IEEE and PubMed are used to search DL papers on common aging-related issues published between January 2017 and August 2021. These papers were reviewed, evaluated, and the findings were summarized. Overall, 34 studies met the inclusion criteria. These studies indicate that DL could help clinicians in diagnosing disease at its early stages by mapping diagnostic predictions into observable clinical presentations; and achieving high predictive performance (e.g., more than 90% accurate predictions of diseases in aging).
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Affiliation(s)
| | - Huiru Zheng
- School of Computing, Ulster University, Belfast BT15 1ED, UK;
- Correspondence:
| | - Haiying Wang
- School of Computing, Ulster University, Belfast BT15 1ED, UK;
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26
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Abstract
With advances in information technology, the demand for using data science to enhance healthcare and disease management is rapidly increasing. Among these technologies, machine learning (ML) has become ubiquitous and indispensable for solving complex problems in many scientific fields, including medical science. ML allows the development of guidelines and framing of the evaluation system for complex diseases based on massive data. In the analysis of rheumatic diseases, which are chronic and remarkably heterogeneous, ML can be anticipated to be extremely helpful in deciphering and revealing the inherent interrelationships in disease development and progression, which can further enhance the overall understanding of the disease, optimize patients' stratification, calibrate therapeutic strategies, and predict prognosis and outcomes. In this review, the basics of ML, its potential clinical applications in rheumatology, together with its strengths and limitations are summarized.
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Mate GS, Kureshi AK, Singh BK. An Efficient CNN for Hand X-Ray Classification of Rheumatoid Arthritis. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:6712785. [PMID: 34221300 PMCID: PMC8219419 DOI: 10.1155/2021/6712785] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 05/19/2021] [Accepted: 05/25/2021] [Indexed: 12/31/2022]
Abstract
Hand Radiography (RA) is one of the prime tests for checking the progress of rheumatoid joint inflammation in human bone joints. Recognizing the specific phase of RA is a difficult assignment, as human abilities regularly curb the techniques for it. Convolutional neural network (CNN) is the center for hand recognition for recognizing complex examples. The human cerebrum capacities work in a high-level way, so CNN has been planned depending on organic neural-related organizations in humans for imitating its unpredictable capacities. This article accordingly presents the convolutional neural network (CNN) which has the ability to naturally gain proficiency with the qualities and anticipate the class of hand radiographs from an expansive informational collection. The reproduction of the CNN halfway layers, which depict the elements of the organization, is likewise appeared. For arrangement of the model, a dataset of 290 radiography images is utilized. The result indicates that hand X-rays are rated with an accuracy of 94.46% by the proposed methodology. Our experiments show that the network sensitivity is observed to be 0.95 and the specificity is observed to be 0.82.
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Affiliation(s)
- Gitanjali S. Mate
- Department of Electronics and Telecommunication, JSPM's Rajarshi Shahu College of Engineering, Pune 411033, India
| | - Abdul K. Kureshi
- Department of Electronics, Maulana Mukhtar Ahmad Nadvi Technical Campus, Malegaon 423203, India
| | - Bhupesh Kumar Singh
- Arba Minch Institute of Technology, Arba Minch University, Arba Minch, Ethiopia
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28
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Jones MA, MacCuaig WM, Frickenstein AN, Camalan S, Gurcan MN, Holter-Chakrabarty J, Morris KT, McNally MW, Booth KK, Carter S, Grizzle WE, McNally LR. Molecular Imaging of Inflammatory Disease. Biomedicines 2021; 9:152. [PMID: 33557374 PMCID: PMC7914540 DOI: 10.3390/biomedicines9020152] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 01/25/2021] [Accepted: 01/31/2021] [Indexed: 02/06/2023] Open
Abstract
Inflammatory diseases include a wide variety of highly prevalent conditions with high mortality rates in severe cases ranging from cardiovascular disease, to rheumatoid arthritis, to chronic obstructive pulmonary disease, to graft vs. host disease, to a number of gastrointestinal disorders. Many diseases that are not considered inflammatory per se are associated with varying levels of inflammation. Imaging of the immune system and inflammatory response is of interest as it can give insight into disease progression and severity. Clinical imaging technologies such as computed tomography (CT) and magnetic resonance imaging (MRI) are traditionally limited to the visualization of anatomical information; then, the presence or absence of an inflammatory state must be inferred from the structural abnormalities. Improvement in available contrast agents has made it possible to obtain functional information as well as anatomical. In vivo imaging of inflammation ultimately facilitates an improved accuracy of diagnostics and monitoring of patients to allow for better patient care. Highly specific molecular imaging of inflammatory biomarkers allows for earlier diagnosis to prevent irreversible damage. Advancements in imaging instruments, targeted tracers, and contrast agents represent a rapidly growing area of preclinical research with the hopes of quick translation to the clinic.
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Affiliation(s)
- Meredith A. Jones
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73019, USA; (M.A.J.); (W.M.M.); (A.N.F.)
- Stephenson Cancer Center, University of Oklahoma, Oklahoma City, OK 73104, USA; (J.H.-C.); (K.T.M.); (M.W.M.); (K.K.B.); (S.C.)
| | - William M. MacCuaig
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73019, USA; (M.A.J.); (W.M.M.); (A.N.F.)
- Stephenson Cancer Center, University of Oklahoma, Oklahoma City, OK 73104, USA; (J.H.-C.); (K.T.M.); (M.W.M.); (K.K.B.); (S.C.)
| | - Alex N. Frickenstein
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK 73019, USA; (M.A.J.); (W.M.M.); (A.N.F.)
- Stephenson Cancer Center, University of Oklahoma, Oklahoma City, OK 73104, USA; (J.H.-C.); (K.T.M.); (M.W.M.); (K.K.B.); (S.C.)
| | - Seda Camalan
- Department of Internal Medicine, Wake Forest Baptist Health, Winston-Salem, NC 27157, USA; (S.C.); (M.N.G.)
| | - Metin N. Gurcan
- Department of Internal Medicine, Wake Forest Baptist Health, Winston-Salem, NC 27157, USA; (S.C.); (M.N.G.)
| | - Jennifer Holter-Chakrabarty
- Stephenson Cancer Center, University of Oklahoma, Oklahoma City, OK 73104, USA; (J.H.-C.); (K.T.M.); (M.W.M.); (K.K.B.); (S.C.)
- Department of Medicine, University of Oklahoma, Oklahoma City, OK 73104, USA
| | - Katherine T. Morris
- Stephenson Cancer Center, University of Oklahoma, Oklahoma City, OK 73104, USA; (J.H.-C.); (K.T.M.); (M.W.M.); (K.K.B.); (S.C.)
- Department of Surgery, University of Oklahoma, Oklahoma City, OK 73104, USA
| | - Molly W. McNally
- Stephenson Cancer Center, University of Oklahoma, Oklahoma City, OK 73104, USA; (J.H.-C.); (K.T.M.); (M.W.M.); (K.K.B.); (S.C.)
| | - Kristina K. Booth
- Stephenson Cancer Center, University of Oklahoma, Oklahoma City, OK 73104, USA; (J.H.-C.); (K.T.M.); (M.W.M.); (K.K.B.); (S.C.)
- Department of Surgery, University of Oklahoma, Oklahoma City, OK 73104, USA
| | - Steven Carter
- Stephenson Cancer Center, University of Oklahoma, Oklahoma City, OK 73104, USA; (J.H.-C.); (K.T.M.); (M.W.M.); (K.K.B.); (S.C.)
- Department of Surgery, University of Oklahoma, Oklahoma City, OK 73104, USA
| | - William E. Grizzle
- Department of Pathology, University of Alabama at Birmingham, Birmingham, AL 35294, USA;
| | - Lacey R. McNally
- Stephenson Cancer Center, University of Oklahoma, Oklahoma City, OK 73104, USA; (J.H.-C.); (K.T.M.); (M.W.M.); (K.K.B.); (S.C.)
- Department of Surgery, University of Oklahoma, Oklahoma City, OK 73104, USA
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Maksabedian Hernandez EJ, Tingzon I, Ampil L, Tiu J. Identifying chronic disease patients using predictive algorithms in pharmacy administrative claims: an application in rheumatoid arthritis. J Med Econ 2021; 24:1272-1279. [PMID: 34704871 DOI: 10.1080/13696998.2021.1999132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
OBJECTIVE To evaluate the predictive performance of logistic and linear regression versus machine learning (ML) algorithms to identify patients with rheumatoid arthritis (RA) treated with target immunomodulators (TIMs) using only pharmacy administrative claims. METHODS Adults aged 18-64 years with ≥1 TIM claim in the IBM MarketScan commercial database were included in this retrospective analysis. The predictive ability of logistic regression to identify RA patients was compared with supervised ML classification algorithms including random forest (RF), decision trees, linear support vector machines (SVMs), neural networks, naïve Bayes classifier, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and K-nearest neighbors (k-NN). Model performance was evaluated using F1 score, accuracy, precision, sensitivity, area under the receiver operating characteristic curve (AUROC), and Matthews correlation coefficient (MCC). Analyses were conducted in all-patient and etanercept-only samples. RESULTS In the all-patients sample, ML approaches did not outperform logistic regression. RF showed small improvements versus logistic regression that were not considered remarkable, respectively: F1 score (84.55% vs 83.96%), accuracy (84.05% vs 83.79%), sensitivity (84.53% vs 82.20%), AUROC (84.04% vs 83.85%), and MCC (68.07% vs 67.66%). Findings were similar in the etanercept samples. CONCLUSION Logistic regression and ML approaches successfully identified patients with RA in a large pharmacy administrative claims database. The ML algorithms were no better than logistic regression at prediction. RF, SVMs, LDA, and ridge classifier showed comparable performance, while neural networks, decision trees, naïve Bayes classifier, and QDA underperformed compared with logistic regression in identifying patients with RA.
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Affiliation(s)
| | | | | | - Jessica Tiu
- Thinking Machines Data Science, Manila, Philippines
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