TR-Dizin İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/396
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Research Project Kolon Polipleri için Kolonoskopi ve Histopatoloji Görüntülerinden Yapay Zekâ Destekli Prognostik Belirteç Tespiti(2023) Doğan, Serkan; Doğan, Refika Sultan; Aydın, Zafer; Akay, Ebru; Güzel, Ömer Faruk; Yilmaz, Bulent; Taşdemir, Sena Büşra YengeçKolon kanseri vakalarının çoğu kolon mukozasında anormal hücre çoğalmasından kaynaklanan poliplerle başlar. Bu projede Kayseri Şehir Hastanesi gastroenteroloji kliniğine gelen 201 hastada tespit edilen poliplere dair kolonoskopi video ve görüntülerinden ve biyopsi örneklerinden elde edilen patoloji raporu ve immunohistokimyasal (İHK) gen ve protein analizi sonuçlarını içeren kapsamlı bir veri seti oluşturulmuştur. Bu projede, elde ettiğimiz veri setinde yer alan görüntülerden kolon poliplerinin evresini/patolojisini tahmin etmek için yenilikçi derin öğrenme ve makine öğrenmesi yöntemlerini temel alan çevrim içi veya dışı kullanılabilen kapsamlı bir yapay zekâ destekli bilgisayarlı görü sistemi geliştirilmiştir. Bu proje kapsamında; kolonoskopi videolarından gerçek zamanlı polip lokalizasyonu, videolardan görüntülerin elde edilmesi, polip görüntülerinden hiperplastik ve tübüler polip ayrımının otomatik yapılması ve hekim performansıyla karşılaştırılması, bu görüntüler üzerinde ayırt edici özniteliklerin tespit edilmesi, farklı büyütmelerde alınan histopatoloji görüntülerinden adenomatöz olan ve olmayan poliplerin ve poliplerin alt tiplerinin yenilikçi derin öğrenme yöntemleriyle tespiti, Ki-67, p53, VEGF, PDL-lenfosit ve PDL-epitel, BRAF ve cd34 isimli gen ve proteinlerin İHK analizlerinin sonuçlarının polip tipleri ve alt tipleri için yorumlanması ve poliplerin bu bilgilere göre etiketlenmesi gerçekleştirilmiştir.Research Project Artificial Intelligence Assisted Prognostic Marker Determination from Colonoscopy and Histopathology Images for Colon Polyps(2023) Doğan, Serkan; Doğan, Refika Sultan; Aydın, Zafer; Akay, Ebru; Güzel, Ömer Faruk; Yilmaz, Bulent; Taşdemir, Sena Büşra YengeçIn this project our goal is to develop a computer vision and artificial intelligence-based system to be used in the real-time or offline differentiation (and characterization) of colon polyps that are known to be the precursors of colon cancer. The system will use image/video processing, machine learning and deep learning approaches. It is expected to provide a real-time optical biopsy opportunity to the endoscopists to examine the polyps in terms of type, phase, malignancy potential, etc. during the colonoscopy procedure without a need to send the extracted tissue to the pathology clinic. The system will allow to make sense of the molecular level of the tissues from the colonoscopy and histopathology images.Article Citation - WoS: 5Citation - Scopus: 10A New Tool for QT Interval Analysis During Sleep in Healthy and Obstructive Sleep Apnea Subjects: A Study on Women(Tubitak Scientific & Technological Research Council Turkey, 2013) Kaya, Kemal Alican; Yilmaz, BulentBy monitoring the Q wave/T wave (QT) interval computed from electrocardiography (ECG) signals during sleep, it is possible to create a link between the ventricular repolarization and sleep stages. In this study, we aimed to find a robust and simple approach to automatically determine the fiducials on each 30-s sleep epoch, such as the Q, R, and T-end points, on long sleep ECG recordings in order to statistically analyze the effect of obstructive sleep apnea (OSA) and sleep stages on QT intervals. This is a retrospective study in which the ECG data extracted from the polysomnography recordings of 7 healthy women and 5 women with OSA, acquired in a sleep laboratory, were used. Experts annotated the sleep stage and OSA presence information for each 30-s epoch. Later, we visually selected epochs with clean signals from a total of 8324 epochs. On the selected epochs, we determined R peaks on each heartbeat, and by aligning each ECG portion corresponding to a heartbeat using those R points, we computed an average ECG signal for each epoch. On the average ECG signals, we developed a novel approach to find the Q and T-end points. With the help of Bazzet's formula, we computed the corrected QT interval (QTc) values for each epoch using the QT and the median RR interval. Finally, we analyzed the QTc values for the different sleep stages and healthy or OSA groups. We employed statistical approaches such as the Mann-Whitney U test, Freidman's test, and the Wilcoxon signed-rank test. As a result of this study, we found that OSA has a prolongation effect on the total duration of the ventricular depolarization and repolarization. We also observed that the QTc values computed in each sleep stage were significantly different between the healthy and OSA groups. Additionally, we discovered that within the healthy group, the QTc values were distinctive in the different sleep stages.