Tıp Fakültesi / Faculty of Medicine

Permanent URI for this collectionhttps://hdl.handle.net/11727/1403

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Author: search.filters.author.Emmez, Hakansearch.filters.applied.f.rights: search.filters.rights.info:eu-repo/semantics/openAccess

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    A Novel Deep Learning Algorithm for the Automatic Detection of High-Grade Gliomas on T2-Weighted Magnetic Resonance I mages: A Preliminary Machine Learning Study
    (2020) Atici, Mehmet Ali; Sagiroglu, Seref; Celtikci, Pinar; Ucar, Murat; Borcek, Alp Ozgun; Emmez, Hakan; Celtikci, Emrah; 0000-0002-1655-6957; 31608975
    AIM: To propose a convolutional neural network (CNN) for the automatic detection of high-grade gliomas (HGGs) on T2-weighted magnetic resonance imaging (MRI) scans. MATERIAL and METHODS: A total of 3580 images obtained from 179 individuals were used for training and validation. After random rotation and vertical flip, training data was augmented by factor of 10 in each iteration. In order to increase data processing time, every single image converted into a Jpeg image which has a resolution of 320x320. Accuracy, precision and recall rates were calculated after training of the algorithm. RESULTS: Following training, CNN achieved acceptable performance ratios of 0.854 to 0.944 for accuracy, 0.812 to 0.980 for precision and 0.738 to 0.907 for recall. Also, CNN was able to detect HGG cases even though there is no apparent mass lesion in the given image. CONCLUSION: Our preliminary findings demonstrate; currently proposed CNN model achieves acceptable performance results for the automatic detection of HGGs on T2-weighted images.
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    Spine Tango in Turkish: Development of a Local Registry System
    (2017) Civi, Soner; Borcek, Alp Ozgun; Bulduk, Erkut B.; Emmez, Hakan; Kaymaz, Memduh; 0000-0002-1055-5152; 27593753; U-2400-2018
    AIM: Successfully established registry systems, rather than personal efforts to collect data, are required to record, analyze, compare and secure patient related data. Unfortunately, our country does not have such patient registry systems for spinal pathologies and surgeries at this time. In order to fill this gap in patient management in Turkey, the authors adopted already established Spine Tango registry system in a unique way answering the requirements of our health system. This article aims to present the adaptation process of Spine Tango forms for use in Turkish and describe the first implementation with 50 patients treated for spinal pathologies in a tertiary referral center. MATERIAL and METHODS: In 2011, an effort was initiated by the first author to translate the original Spine Tango forms into Turkish. Funding for this project was provided by authors themselves. With the assistance of a Spine Tango team, the translation process was completed. The Turkish forms were then used in an academic institution with a high spinal workload. A local solution was developed by the authors using commercially available software and mobile instruments. This system was tested with 50 spine patients from June 2012 to January 2013. RESULTS: The analysis of the data gathered using the new Turkey Spine Tango registry system was successful. CONCLUSION: In an environment of exponentially increasing medical data, successfully established registry systems have the potential to facilitate patient management. The authors recommend the use of Turkish Spine Tango forms for clinics performing spinal interventions.