AB1161 ARTIFICIAL INTELLIGENCE FOR RHEUMATOLOGY

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1871-1872
Author(s):  
A. C. Genç ◽  
F. Turkoglu Genc ◽  
A. B. Kara ◽  
L. Genc Kaya ◽  
Z. Ozturk ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Sacroiliac Joint ◽  
Axial Skeleton ◽  
Observer Agreement ◽  
Mri Findings ◽  
Learning Methods ◽  
Expert Radiologist ◽  
Active Sacroiliitis

Background:Magnetic resonance imaging (MRI) of sacroiliac (SI) joints is used to detect early sacroiliitis(1). There can be an interobserver disagreement in MRI findings of SI joints of spondyloarthropathy patients between a rheumatologist, a local radiologist, and an expert radiologist(2). Artificial Intelligence and deep learning methods to detect abnormalities have become popular in radiology and other medical fields in recent years(3). Search for “artificial intelligence” and “radiology” in Pubmed for the last five years returned around 1500 clinical studies yet no results were retrieved for “artificial intelligence” and “rheumatology”.Objectives:Artificial Intelligence (AI) can help to detect the pathological area like sacroiliitis or not and also allows us to characterize it as quantitatively rather than qualitatively in the SI-MRI.Methods:Between the years of 2015 and 2019, 8100 sacroiliac MRIs were taken at our center. The MRIs of 1150 patients who were reported as active or chronic sacroiliitis from these sacroiliac MRIs or whose MRIs were considered by the primary physician in favor of sacroiliitis was included in the study. 1441 MRI coronal STIR sequence of 1150 patients were tagged as ‘’active sacroiliitis’’ and trained to detect and localize active sacroiliitis and provide prediction performance. This model is available for various operating systems. (Image1)Results:Precision score, the percentage of sacroiliac images of the trained model, is 87.1%. Recall, the percentage of the total sacroiliac MRIs correctly classified by the model, is 82.1% and the mean average precision (mAP) of the model is 89%.Conclusion:There are gray areas in medicine like sacroiliitis. Inter-observer variability can be reduced by AI and deep learning methods. The efficiency and reliability of health services can be increased in this way.References:[1]Jans L, Egund N, Eshed I, Sudoł-Szopińska I, Jurik AG. Sacroiliitis in Axial Spondyloarthritis: Assessing Morphology and Activity. Semin Musculoskelet Radiol. 2018;22: 180–188.[2]B. Arnbak, T. S. Jensen, C. Manniche, A. Zejden, N. Egund, and A. G. Jurik, “Spondyloarthritis-related and degenerative MRI changes in the axial skeleton—an inter- and intra-observer agreement study,”BMC Musculoskeletal Disorders, vol. 14, article 274, 2013.[3]Rueda, Juan C et al. “Interobserver Agreement in Magnetic Resonance of the Sacroiliac Joints in Patients with Spondyloarthritis.”International journal of rheumatology(2017).Image1.Bilateral active sacroiliitis detected automatically by AI model (in right sacroiliac joint 75.6%> (50%), in left sacroiliac joint 65% (>50%))Disclosure of Interests:None declared

scholarly journals AB1093 INTEROBSERVER AGREEMENT IN MAGNETIC RESONANCE OF SACROILIAC JOINTS ABOUT ACTIVE SACROILIITIS

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1835.1-1836
Author(s):  
A. C. Genç ◽  
F. Turkoglu Genc ◽  
A. B. Kara ◽  
Z. Ozturk ◽  
D. Karatas ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Interobserver Agreement ◽  
Corporate Strategy ◽  
Axial Spondyloarthritis ◽  
Musculoskeletal Diseases ◽  
P Value ◽  
Mri Findings ◽  
Expert Radiologist ◽  
Significant Difference ◽  
Active Sacroiliitis

Background:Axial spondyloarthritis has characteristic clinical features such as enthesitis, sacroiliitis and spondylitis, and extra-articular manifestations(1). Magnetic resonance imaging (MRI) of sacroiliac (SI) joints is used to detect early sacroiliitis(2). Health institutions in our country carry out some of the radiology reporting services by outsourcing for reasons such as high cost and insufficient number of radiologists(3).Objectives:We decided to evaluate the interobserver agreement in active MRI findings of SI between radiologist of outsourcing radiology services and local/expert radiologist in musculoskeletal diseases.Methods:Between the years of 2015 and 2019, 8100 sacroiliac MRIs were taken at our center. The MRI of 1150 patients who were reported as active or chronic sacroiliitis from these sacroiliac MRIs or whose MRI was considered by the primary physician in favor of sacroiliitis was included in the study. Concordance between Evaluation and Service Procurement was examined using Kappa (k) coefficients. Mc Nemar test was used to compare the evaluation result between two observers. A p-value <0.05 was considered significant. Analyses were performed using commercial software (IBM SPSS Statistics, Version 23.0. Armonk, NY: IBM Corp.)Results:Of the 1150 patients examined in the study, 526 (45.7%) were male and 624 (54.3%) were female. The general average age is 37.20 ± 11.65 and the average age of men and women is 34.98 ± 11.19 and 39.07 ± 11.71 respectively. A statistically significant difference was found between the expert radiologists and outsourcing radiologist reports. In other words, a high level of compatibility was not found among the evaluators (p <0.001). When the consistency between expert radiologist and outsourced radiologist reports was examined, it was observed that there was a medium level of concordance (k = 0.589).Conclusion:The diagnosis of a spondyloarthropathy may be delayed for some reasons. In addition to the insidious course of the disease, being contented with an outsourced radiologist report may delay diagnosis. If the patient’s clinic and MRI report are not consistent, the patient should not be removed from follow-up.References:[1]Braun J. ‘Axial spondyloarthritis including ankylosing spondylitis’ Rheumatology (Oxford). 2018 1;57(suppl_6):vi1-vi3[2]Jans L, Egund N, Eshed I, Sudoł-Szopińska I, Jurik AG. Sacroiliitis in Axial Spondyloarthritis: Assessing Morphology and Activity. Semin Musculoskelet Radiol. 2018;22: 180–188.[3]Quélin B, Duhamel F. Bringing Together Strategic Outsourcing and Corporate Strategy: European Management Journal. 2003. pp. 647–661. doi:10.1016/s0263-2373(03)00113-0OUTSOURCING RADIOLOGIST REPORTSTOTALpNOT ACTIVE SACROILIITISACTIVE SACROILIITISEXPERT RADIOLOGIST REPORTSNOT ACTIVE SACROILIITIS508178686<0.0010.589ACTIVE SACROILIITIS59405464TOTAL5675831150Disclosure of Interests:None declared

scholarly journals Radiological Followup of the Evolution of Inflammatory Process in Sacroiliac Joint with Magnetic Resonance Imaging: A Case with Pyogenic Sacroiliitis

2012 ◽  
Vol 2012 ◽  
pp. 1-4 ◽  
Author(s):  
Muhammet Cinar ◽  
Hatice Tugba Sanal ◽  
Sedat Yilmaz ◽  
Ismail Simsek ◽  
Hakan Erdem ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Sacroiliac Joint ◽  
Resonance Imaging ◽  
Mr Images ◽  
Buttock Pain ◽  
Pyogenic Sacroiliitis ◽  
Acute Form ◽  
Active Sacroiliitis ◽  
Bone Structures ◽  
Surrounding Bone

Pyogenic sacroiliitis (PS) is an acute form of sacroiliitis that mostly starts with very painful buttock pain. Here in this case, the followup magnetic resonance (MR) images of a 49-year-old male patient with PS is displayed. After his sacroiliitis was documented by MR images, he was treated with the combination of rifampicin plus streptomycin and moxifloxacin. Serial MR investigations were done to disclose acute and subsequent imaging changes concerning sacroiliac joint and surrounding bone structures. Although after treatment all the symptoms were completely resolved, 20 months later changes suggesting active sacroiliitis on MR images were continuing.

scholarly journals Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods

2020 ◽  
Vol 189 ◽  
pp. 105316 ◽  
Author(s):  
Rogier R. Wildeboer ◽  
Ruud J.G. van Sloun ◽  
Hessel Wijkstra ◽  
Massimo Mischi
Keyword(s):  
Prostate Cancer ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Cancer Imaging ◽  
Learning Methods

scholarly journals Performance analysis of pre-trained transfer learning models for the classification of the rolling bearing faults

2021 ◽  
Vol 2070 (1) ◽  
pp. 012141
Author(s):  
Pavan Sharma ◽  
Hemant Amhia ◽  
Sunil Datt Sharma
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Manufacturing Industry ◽  
Rolling Bearing ◽  
Performance Comparison ◽  
Learning Methods ◽  
Training Time ◽  
Bearing Faults ◽  
Artificial Intelligence Techniques

Abstract Nowadays, artificial intelligence techniques are getting popular in modern industry to diagnose the rolling bearing faults (RBFs). The RBFs occur in rotating machinery and these are common in every manufacturing industry. The diagnosis of the RBFs is highly needed to reduce the financial and production losses. Therefore, various artificial intelligence techniques such as machine and deep learning have been developed to diagnose the RBFs in the rotating machines. But, the performance of these techniques has suffered due the size of the dataset. Because, Machine learning and deep learning methods based methods are suitable for the small and large datasets respectively. Deep learning methods have also been limited to large training time. In this paper, performance of the different pre-trained models for the RBFs classification has been analysed. CWRU Dataset has been used for the performance comparison.

scholarly journals Towards Replacing Late Gadolinium Enhancement with Artificial Intelligence Virtual Native Enhancement for Gadolinium-Free Cardiovascular Magnetic Resonance Tissue Characterization in Hypertrophic Cardiomyopathy

Circulation ◽  
2021 ◽  
Author(s):  
Qiang Zhang ◽  
Matthew K. Burrage ◽  
Elena Lukaschuk ◽  
Mayooran Shanmuganathan ◽  
Iulia A. Popescu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Cardiovascular Magnetic Resonance ◽  
Deep Learning ◽  
Late Gadolinium Enhancement ◽  
Hypertrophic Cardiomyopathy ◽  
Magnetic Resonance ◽  
Image Quality ◽  
Tissue Characterization ◽  
Wilcoxon Test ◽  
Gadolinium Enhancement

Background: Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging is the gold standard for non-invasive myocardial tissue characterization, but requires intravenous contrast agent administration. It is highly desired to develop a contrast-agent-free technology to replace LGE for faster and cheaper CMR scans. Methods: A CMR Virtual Native Enhancement (VNE) imaging technology was developed using artificial intelligence. The deep learning model for generating VNE uses multiple streams of convolutional neural networks to exploit and enhance the existing signals in native T1-maps (pixel-wise maps of tissue T1 relaxation times) and cine imaging of cardiac structure and function, presenting them as LGE-equivalent images. The VNE generator was trained using generative adversarial networks. This technology was first developed on CMR datasets from the multi-center Hypertrophic Cardiomyopathy Registry (HCMR), using HCM as an exemplar. The datasets were randomized into two independent groups for deep learning training and testing. The test data of VNE and LGE were scored and contoured by experienced human operators to assess image quality, visuospatial agreement and myocardial lesion burden quantification. Image quality was compared using nonparametric Wilcoxon test. Intra- and inter-observer agreement was analyzed using intraclass correlation coefficients (ICC). Lesion quantification by VNE and LGE were compared using linear regression and ICC. Results: 1348 HCM patients provided 4093 triplets of matched T1-maps, cines, and LGE datasets. After randomization and data quality control, 2695 datasets were used for VNE method development, and 345 for independent testing. VNE had significantly better image quality than LGE, as assessed by 4 operators (n=345 datasets, p<0.001, Wilcoxon test). VNE revealed characteristic HCM lesions in high visuospatial agreement with LGE. In 121 patients (n=326 datasets), VNE correlated with LGE in detecting and quantifying both hyper-intensity myocardial lesions (r=0.77-0.79, ICC=0.77-0.87; p<0.001) and intermediate-intensity lesions (r=0.70-0.76, ICC=0.82-0.85; p<0.001). The native CMR images (cine plus T1-map) required for VNE can be acquired within 15 minutes. Producing a VNE image takes less than one second. Conclusions: VNE is a new CMR technology that resembles conventional LGE, without the need for contrast administration. VNE achieved high agreement with LGE in the distribution and quantification of lesions, with significantly better image quality.

scholarly journals Explainable Artificial Intelligence (xAI) Approaches and Deep Meta-Learning Models

2020 ◽  
Author(s):  
Evren Dağlarli
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Black Box ◽  
Learning Models ◽  
Learning Methods ◽  
Data Set ◽  
Box Models ◽  
Explainable Artificial Intelligence ◽  
Artificial Neural ◽  
Black Box Models

The explainable artificial intelligence (xAI) is one of the interesting issues that has emerged recently. Many researchers are trying to deal with the subject with different dimensions and interesting results that have come out. However, we are still at the beginning of the way to understand these types of models. The forthcoming years are expected to be years in which the openness of deep learning models is discussed. In classical artificial intelligence approaches, we frequently encounter deep learning methods available today. These deep learning methods can yield highly effective results according to the data set size, data set quality, the methods used in feature extraction, the hyper parameter set used in deep learning models, the activation functions, and the optimization algorithms. However, there are important shortcomings that current deep learning models are currently inadequate. These artificial neural network-based models are black box models that generalize the data transmitted to it and learn from the data. Therefore, the relational link between input and output is not observable. This is an important open point in artificial neural networks and deep learning models. For these reasons, it is necessary to make serious efforts on the explainability and interpretability of black box models.

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