Background:There is an average of 8 years delay in the diagnosis of ankylosing spondylitis (AS). The most important danger of late diagnosis is that the disease can cause physical and functional disability (2). There is no specific diagnostic biomarker for AS. Sacroiliac joint (SIJ) radiography is frequently used in the diagnosis and follow-up of AS due to its easy accessibility and low cost. It can be classified as grade 0, 1, 2, 3, 4, and these classes may not be sharply separated from each other (3).Objectives:Interpretation of the SIJ radiography may differ from physician to physician. In fact, the same physician may interpret it differently at different times (3). We wanted to find a solution to the intraobserver disagreement problem with the artificial intelligence model.Methods:The SIJ radiography of 590 patients who applied to our center were divided into 3 categories as right and left, separately, grade 0, grade 1-2, grade 3-4, and an educational data set was prepared for the object recognition method. 488 images were augmented through noise from 490 images in the training data. 242 articular objects were trained for grade 0, 278 for grade 1-2, and 1426 for grade 2-3. The model was tested with 100 images for 36 joint objects for grade 0, 29 for grade 1-2, and 135 for grade 3-4 to create a computer vision-artificial intelligence model (image 1).Results:Training performance is 70% for grade 0, %63 for grade 1-2, %90 for grade 3-4 and test performance is %52 for grade 0, %24 for grade 1-2, %86 intersection over union (I/U:Intersection over Union is a form of measurement used to indicate the accuracy of an object detector.) for grade 3-4. The mean average precision (mAP) score of our object detection model is %65.9 for test data set (image 1). The estimation quality of the model can be affected by the distribution and number of each class.Conclusion:The experience of the x-ray technician, dose adjustment, and position differences due to patient compliance complicate the standardization of SIJ radiography and this may cause interobserver disagreement (3). Artificial intelligence models to be created with a larger and homogeneous data set in order to ensure objective standardization in the interpretation of the SIJ graph can help physicians.References:[1]Braun J. ‘Axial spondyloarthritis including ankylosing spondylitis’ Rheumatology (Oxford). 2018 1;57(suppl_6):vi1-vi3[2]Rudwaleit M, van der Heijde D, Khan MA, Braun J, Sieper J. How to diagnose axial spondyloarthritis early. Ann Rheum Dis 2004; 63:535-543.[3]van den Berg, R. et al. Agreement between clinical practice and trained central reading in reading of sacroiliac joints on plain pelvic radiographs. Results from the DESIR cohort. Arthritis Rheumatol 66, 2403–2411 (2014).Disclosure of Interests:None declared.
A 46-year-old man presented with a complaint of effort dyspnea. On transthoracic echocardiography a circle appeared in LVOT. It was seen freely floating, disappearing in every systole and appearing again in diastole. Turbulence was seen inside the circle with color Doppler. Transesophageal echocardiography showed aortic cusps and their coaptation to be normal. Aortic root diameters were normal at the annulus, sinus of Valsalva, and sinotubular junction. There were no signs of dissection, infective endocarditis or abscess. But as the probe was advanced, left sinus of Valsalva was found to be prolapsed, and ruptured into LVOT.
All prosthetic valves are at least mildly stenotic and have relatively high transvalvular pressure gradients that can be observed despite normal prosthesis function. Such gradients may be due to a mismatch between prosthesis effective orifice area (EOA) and patient's body size. Valve prosthesis-patient mismatch (VP-PM) may occur due to mismatches of both parameters, the expected hemodynamic performance of the prosthesis and the cardiac output requirements of the patient, which are largely related to the body size at rest. In other words, a prosthesis may be adequate for patients with a small body surface area (BSA) but might become obstructive for patients with a large BSA. The only parameter that has proven to be consistently and realistically useful to predict and describe VP-PM is the effective orifice area index (EOAI). The projected EOAI was identified as the best parameter to predict the VP-PM occurrence after surgery. VP-PM has been known to be independently and significantly associated with clinical outcomes. Severe VP-PM has a significant impact on early and late mortality, whereas moderate VP-PM may have a significant effect on mortality only in vulnerable subsets of patients, and particularly in those with depressed LV systolic function. The surgeon's anticipation of VP-PM prior to surgery, and successfully implented preventive strategies can reduce the incidence of VP-PM. Preventive strategies to avoid VP-PM should be individualized according to the anticipated severity of VP-PM and of the patient's baseline risk profile.
BackgroundVirtual presentations have become increasingly common due to the COVID-19 pandemic and advancements in technology. However, it is not yet clear how to effectively use artificial intelligence (AI) in virtual presentations to enhance their effectiveness.ObjectivesThe aim of this study is to investigate the current state of AI in virtual presentations and to develop practical guidelines for using AI to enhance the effectiveness of virtual presentations.MethodsChatGPT is an artificial intelligence chatbot [1]. The final version contains information up to years of 2021. We wrote to ChatGPT: “I want to submit a study for the European League Against Rheumatism (EULAR) 2023. Title: “How to make a virtual presentation using artificial intelligence?”. Prepare a summary consisting of background and objectives sections for me.”. The texts generated by ChatGPT were transferred to another virtual platform to be converted to audio and video. The text in the background and objectives sections in this abstract was voiced by the speaking avatar [2].ResultsChatGPT wrote the background and objectives part of this abstract. As the authors, we have not made any changes in order to be objective. Thanks to another artificial intelligence, the content in this text was voiced by an avatar and turned into a video (Figure 1).ConclusionIn the near future, artificial intelligence will be used more effectively in the preparation and presentation of scientific articles. In this way, artificial intelligence will help scientists to use their time more efficiently. Developing technology also offers equal opportunities for scientists with social phobia and visual or speech disabilities.References[1]GPT-3 [Software]. Retrieved fromhttps://openai.com/[2]synthesia. Retrieved fromhttps://synthesia.ai/Figure 1.Acknowledgements:NIL.Disclosure of InterestsNone Declared.
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