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Item Open Access Artificial intelligence-based hybrid anomaly detection and clinical decision support techniques for automated detection of cardiovascular diseases and Covid-19(2023-10) Terzi, Merve BegümCoronary artery diseases are the leading cause of death worldwide, and early diagnosis is crucial for timely treatment. To address this, we present a novel automated arti cial intelligence-based hybrid anomaly detection technique com posed of various signal processing, feature extraction, supervised, and unsuper vised machine learning methods. By jointly and simultaneously analyzing 12-lead electrocardiogram (ECG) and cardiac sympathetic nerve activity (CSNA) data, the automated arti cial intelligence-based hybrid anomaly detection technique performs fast, early, and accurate diagnosis of coronary artery diseases. To develop and evaluate the proposed automated arti cial intelligence-based hybrid anomaly detection technique, we utilized the fully labeled STAFF III and PTBD databases, which contain 12-lead wideband raw recordings non invasively acquired from 260 subjects. Using the wideband raw recordings in these databases, we developed a signal processing technique that simultaneously detects the 12-lead ECG and CSNA signals of all subjects. Subsequently, using the pre-processed 12-lead ECG and CSNA signals, we developed a time-domain feature extraction technique that extracts the statistical CSNA and ECG features critical for the reliable diagnosis of coronary artery diseases. Using the extracted discriminative features, we developed a supervised classi cation technique based on arti cial neural networks that simultaneously detects anomalies in the 12-lead ECG and CSNA data. Furthermore, we developed an unsupervised clustering technique based on the Gaussian mixture model and Neyman-Pearson criterion that performs robust detection of the outliers corresponding to coronary artery diseases. By using the automated arti cial intelligence-based hybrid anomaly detection technique, we have demonstrated a signi cant association between the increase in the amplitude of CSNA signal and anomalies in ECG signal during coronary artery diseases. The automated arti cial intelligence-based hybrid anomaly de tection technique performed highly reliable detection of coronary artery diseases with a sensitivity of 98.48%, speci city of 97.73%, accuracy of 98.11%, positive predictive value (PPV) of 97.74%, negative predictive value (NPV) of 98.47%, and F1-score of 98.11%. Hence, the arti cial intelligence-based hybrid anomaly detection technique has superior performance compared to the gold standard diagnostic test ECG in diagnosing coronary artery diseases. Additionally, it out performed other techniques developed in this study that separately utilize either only CSNA data or only ECG data. Therefore, it signi cantly increases the detec tion performance of coronary artery diseases by taking advantage of the diversity in di erent data types and leveraging their strengths. Furthermore, its perfor mance is comparatively better than that of most previously proposed machine and deep learning methods that exclusively used ECG data to diagnose or clas sify coronary artery diseases. It also has a very short implementation time, which is highly desirable for real-time detection of coronary artery diseases in clinical practice. The proposed automated arti cial intelligence-based hybrid anomaly detection technique may serve as an e cient decision-support system to increase physicians' success in achieving fast, early, and accurate diagnosis of coronary artery diseases. It may be highly bene cial and valuable, particularly for asymptomatic coronary artery disease patients, for whom the diagnostic information provided by ECG alone is not su cient to reliably diagnose the disease. Hence, it may signi cantly improve patient outcomes, enable timely treatments, and reduce the mortality associated with cardiovascular diseases. Secondly, we propose a new automated arti cial intelligence-based hybrid clinical decision support technique that jointly analyzes reverse transcriptase polymerase chain reaction (RT-PCR) curves, thorax computed tomography im ages, and laboratory data to perform fast and accurate diagnosis of Coronavirus disease 2019 (COVID-19). For this purpose, we retrospectively created the fully labeled Ankara University Faculty of Medicine COVID-19 (AUFM-CoV) database, which contains a wide variety of medical data, including RT-PCR curves, thorax computed tomogra phy images, and laboratory data. The AUFM-CoV is the most comprehensive database that includes thorax computed tomography images of COVID-19 pneu monia (CVP), other viral and bacterial pneumonias (VBP), and parenchymal lung diseases (PLD), all of which present signi cant challenges for di erential diagnosis. We developed a new automated arti cial intelligence-based hybrid clinical de cision support technique, which is an ensemble learning technique consisting of two preprocessing methods, long short-term memory network-based deep learning method, convolutional neural network-based deep learning method, and arti cial neural network-based machine learning method. By jointly analyzing RT-PCR curves, thorax computed tomography images, and laboratory data, the proposed automated arti cial intelligence-based hybrid clinical decision support technique bene ts from the diversity in di erent data types that are critical for the reliable detection of COVID-19 and leverages their strengths. The multi-class classi cation performance results of the proposed convolu tional neural network-based deep learning method on the AUFM-CoV database showed that it achieved highly reliable detection of COVID-19 with a sensitivity of 91.9%, speci city of 92.5%, precision of 80.4%, and F1-score of 86%. There fore, it outperformed thorax computed tomography in terms of the speci city of COVID-19 diagnosis. Moreover, the convolutional neural network-based deep learning method has been shown to very successfully distinguish COVID-19 pneumonia (CVP) from other viral and bacterial pneumonias (VBP) and parenchymal lung diseases (PLD), which exhibit very similar radiological ndings. Therefore, it has great potential to be successfully used in the di erential diagnosis of pulmonary dis eases containing ground-glass opacities. The binary classi cation performance results of the proposed convolutional neural network-based deep learning method showed that it achieved a sensitivity of 91.5%, speci city of 94.8%, precision of 85.6%, and F1-score of 88.4% in diagnosing COVID-19. Hence, it has compara ble sensitivity to thorax computed tomography in diagnosing COVID-19. Additionally, the binary classi cation performance results of the proposed long short-term memory network-based deep learning method on the AUFM-CoV database showed that it performed highly reliable detection of COVID-19 with a sensitivity of 96.6%, speci city of 99.2%, precision of 98.1%, and F1-score of 97.3%. Thus, it outperformed the gold standard RT-PCR test in terms of the sensitivity of COVID-19 diagnosis Furthermore, the multi-class classi cation performance results of the proposed automated arti cial intelligence-based hybrid clinical decision support technique on the AUFM-CoV database showed that it diagnosed COVID-19 with a sen sitivity of 66.3%, speci city of 94.9%, precision of 80%, and F1-score of 73%. Hence, it has been shown to very successfully perform the di erential diagnosis of COVID-19 pneumonia (CVP) and other pneumonias. The binary classi cation performance results of the automated arti cial intelligence-based hybrid clinical decision support technique revealed that it diagnosed COVID-19 with a sensi tivity of 90%, speci city of 92.8%, precision of 91.8%, and F1-score of 90.9%. Therefore, it exhibits superior sensitivity and speci city compared to laboratory data in COVID-19 diagnosis. The performance results of the proposed automated arti cial intelligence-based hybrid clinical decision support technique on the AUFM-CoV database demon strate its ability to provide highly reliable diagnosis of COVID-19 by jointly ana lyzing RT-PCR data, thorax computed tomography images, and laboratory data. Consequently, it may signi cantly increase the success of physicians in diagnosing COVID-19, assist them in rapidly isolating and treating COVID-19 patients, and reduce their workload in daily clinical practice.Item Open Access Metamaterial-based wireless RF-MEMS strain sensors(IEEE, 2010) Melik, Rohat; Ünal, Emre; Perkgoz, Nihan Kosku; Puttlitz, C.; Demir, Hilmi VolkanApproximately 10% of the fractures do not heal properly because of the inability to monitor fracture healing. Standard radiography is not capable of discriminating whether bone healing is occurring normally or aberrantly. We propose and develop an implantable wireless sensor that monitors strain on implanted hardware in real time telemetrically. This enables clinicians to monitor fracture healing. Here we present the development and demonstration of metamaterial-based radiofrequency (RF) micro-electro-mechanical system (MEMS) strain sensors for wireless strain sensing to monitor fracture healing. The operating frequency of these sensors shifts under mechanical loading; this shift is related to the surface strain of the implantable test material. In this work, we implemented metamaterials in two different architectures as bio-implantable wireless strain sensors for the first time. These custom-design metamaterials exhibit better performance as sensors than traditional RF structures (e.g., spiral coils) because of their unique structural properties (splits). They feature a low enough operating frequency to avoid the background absorption of soft tissue and yield higher Q-factors compared to the spiral structures (because their gaps have much higher electric field density). In our first metamaterial architecture of an 5x5 array, the wireless sensor shows high sensitivity (109kHz/kgf, 5.148kHz/microstrain) with low nonlinearity-error (<200microstrain). Using our second architecture, we then improved the structure of classical metamaterial and obtained nested metamaterials that incorporate multiple metamaterials in a compact nested structure and measured strain telemetrically at low operating frequencies. This novel nested metamaterial structure outperformed classical metamaterial structure as wireless strain sensors. By employing nested metamaterial architecture, the operating frequency is reduced from 529.8 MHz to 506.2 MHz while the sensitivity is increased from 0.72 kHz/kgf to 1.09 kHz/kgf. ©2010 IEEE.Item Restricted Öncü ve duayen bir radyolog: Tuğrul Pırnar(Bilkent University, 2023) Akıncı, Alp; Bağcı, Alinur; Özemre, İrem; Pırnar, Tuğrul; Yılmaz, DilaraTuğrul Pırnar öncü ve duayen bir Türk radyoloğudur. Hacettepe Üniversitesi ve Bilkent Üniversitesi’nde rektörlük yapan Pırnar, Türkiye’de hem eğitim de hem de Tıp alanında büyük başarılara imza attı. Yurtdışında aldığı radyoloji eğitimlerinden sonra Pırnar, Dokuz Eylül Üniversitesi’nin bünyesinde Türkiye’ye ilk tomografi ve manyetik rezonansı getirdi. Tuğrul Pırnar, kendini gelişime adayan ve eğitime önem veren biriydi. Emeklilik hayatında ailesi ile birlikte zaman geçirdi. Tuğrul Pırnar, aynı ismi taşıyan grup arkadaşımız Tuğrul Pırnar’ın dedesiydi.Item Open Access Transrectal prostate biopsy and fiducial marker placement in a standard 1.5T magnetic resonance imaging scanner(Elsevier Inc., 2006-01) Susil, R. C.; Menard, C.; Kriegel, A.; Coleman, J. A.; Camphausen, K.; Choyke, P.; Fichtinger, G.; Whitcomb, L. L.; Coleman, C. N.; Atalar, ErginPurpose: We investigated the accuracy and feasibility of a system that provides transrectal needle access to the prostate concurrent with 1.5 Tesla MRI which previously has not been possible. Materials and Methods: In 5 patients with previously diagnosed prostate cancer, MRI guided intraprostatic placement of gold fiducial markers (4 procedures) and/or prostate biopsy (3 procedures) was performed using local anesthesia. Results: Mean procedure duration was 76 minutes and all patients tolerated the intervention well. Procedure related adverse events included self-limited hematuria and hematochezia following 3 of 8 procedures (all resolved in less than 1 week). Mean needle placement accuracy was 1.9 mm for the fiducial marker placement studies and 1.8 mm for the biopsy procedures. Mean fiducial marker placement accuracy was 4.8 mm and the mean fiducial marker placement accuracy transverse to the needle direction was 2.6 mm. All patients who underwent the procedure were able to complete their course of radiotherapy without delay or complication. Conclusions: While studies of clinical usefulness are warranted, transrectal 1.5 T MRI guided prostate biopsy and fiducial marker placement is feasible using this system, providing new opportunities for image guided diagnostic and therapeutic prostate interventions.Item Restricted Türkiye’de girişimsel radyoloji: yöntemi ve Türkiye’de uygulanmaya başlanması(Bilkent University, 2024) Budan, Ebrar; Yıldırım, Melek Ilgın; Saraç, Sara Gül; Yıldırım, Yusuf Eren; Kakillioğlu, ZeynepBu makale girişimsel radyolojinin gelişen teknoloji ile birlikte nasıl ortaya çıktığını ve Türkiye’de nasıl bir süreç geçirdiğini anlatmaktadır. Bu anlatı yapılırken, girişimsel radyolojinin radyoloji ile farkı ortaya konularak, girişimsel radyolojinin tarihçesi ile uygulama alanlarına değinilecektir. Girişimsel radyoloji incelenirken, özellikle Türkiye’de ortaya çıkışını ve uygulanışını aydınlatmak hedeflenmektedir. Bu doğrultuda girişimsel radyolojinin Türkiye’deki öncülerinden Prof.Dr. Saruhan Çekirge ile görüşülerek bir sözlü tarih çalışması ortaya konulacaktır. Girişimsel radyolojinin avantaj ve dezavantajları ile geleceği hakkında öngörü amaçlanmaktadır.