Person: KESER, GAYE
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Publication Metadata only A deep learning apprroach to detection of oral cancer lesions from intra oral patient images(2023-10-07) KESER, GAYE; NAMDAR PEKİNER, FİLİZ MEDİHA; KESER G., NAMDAR PEKİNER F. M., BAYRAKDAR İ. Ş., ÇELİK Ö., ORHAN K.Publication Metadata only Squamous cell carcinoma resulting from chronic trauma: a case report(2023-10-07) ÜNAL, SUAY YAĞMUR; KESER, GAYE; NAMDAR PEKİNER, FİLİZ MEDİHA; ÜNAL S. Y., KESER G., NAMDAR PEKİNER F. M.Publication Metadata only Comparative evaluation of impacted canine localisation using two different panoramic radiography devices(2023-11-10) NAMDAR PEKİNER, FİLİZ MEDİHA; KESER, GAYE; ÖNER TALMAÇ A. G., İBİŞ T., NAMDAR PEKİNER F. M., KESER G.Publication Open Access Assessing the reliability of CBCT-based AI-generated STL files in diagnosing osseous changes of the mandibular condyle: a comparative study with ground truth diagnosis(2023-09-04) KESER, GAYE; NAMDAR PEKİNER, FİLİZ MEDİHA; Orhan K., Sanders A., Ünsal G., Ezhov M., Mısırlı M., Gusarev M., İçen M., Shamshiev M., Keser G., Namdar Pekiner F., et al.Objectives: This study aims to evaluate the reliability of AI-generated STL files in diagnosing osseous changes of the mandibular condyle and compare them to a ground truth (GT) diagnosis made by six radiologists. Methods: A total of 432 retrospective CBCT images from four universities were evaluated by six dentomaxillofacial radiologists who identified osseous changes such as flattening, erosion, osteophyte formation, bifid condyle formation, and osteosclerosis. All images were evaluated by each radiologist blindly and recorded on a spreadsheet. All evaluations were compared and for the disagreements, a consensus meeting was held online to create a uniform GT diagnosis spreadsheet. A web-based dental AI software was used to generate STL files of the CBCT images, which were then evaluated by two dentomaxillofacial radiologists. The new observer, GT, was compared to this new STL file evaluation, and the interclass correlation (ICC) value was calculated for each pathology. Results: Out of the 864 condyles assessed, the ground truth diagnosis identified 372 cases of flattening, 185 cases of erosion, 70 cases of osteophyte formation, 117 cases of osteosclerosis, and 15 cases of bifid condyle formation. The ICC values for flattening, erosion, osteophyte formation, osteosclerosis, and bifid condyle formation were 1.000, 0.782, 1.000, 0.000, and 1.000, respectively, when comparing diagnoses made using STL files with the ground truth. Conclusions: AI-generated STL files are reliable in diagnosing bifid condyle formation, osteophyte formation, and flattening of the condyle. However, the diagnosis of osteosclerosis using AI-generated STL files is not reliable, and the accuracy of diagnosis is affected by the erosion grade.Publication Metadata only Renal hastalıklarda diş hekimliği yaklaşımı(İstanbul Tıp Kitabevleri, 2022-01-01) KESER, GAYE; Keser G.Publication Metadata only Oral squamous cell carcinoma of buccal mucosa: a case report(2023-10-07) KESER, GAYE; NAMDAR PEKİNER, FİLİZ MEDİHA; Yülek H., KESER G., NAMDAR PEKİNER F. M., OLGAÇ N. V.Publication Open Access Plaut-vincent stomatitis: A case report(2023-12-01) ÜNAL, SUAY YAĞMUR; KESER, GAYE; NAMDAR PEKİNER, FİLİZ MEDİHA; Ünal S. Y., Keser G., Namdar Pekiner F. M.Necrotizing ulcerative stomatitis or Plaut-Vincent’s Stomatitis is a complication of necrotizing ulcerative gingivitis that extends beyond the gingiva and is involved in other parts of the oral mucosa, with Fusiform bacillus, Borrelia vincenti and other anaerobic microorganisms being the most common associated bacteria. It starts with sore throat, bad smell in the mouth, bleeding gums in young adults with poor oral hygiene and decreased immune resistance. In this case, clinical findings of Plaut-Vincent Stomatitis belonging to a male patient are presented. In a 22-yearold male patient, erythematous, ulcers with irregular margins and grayish-white fibrin were observed in the soft tissue of the right third molar region of the mandible and in the buccal mucosa. The patient has halitosis, difficulty in swallowing, pain in the oropharynx, and lymphadenopathy. In the treatment of infected tissues, improvement was observed after systemic antibiotics and hydrogen peroxide mouthwash were applied for 6-7 days. PlautVincent Stomatitis is frequently seen in young adults and poor oral hygiene, smoking, emotional stress, alcohol consumption and malnutrition are stated as etiological factors that predispose to this disease. Detection of ulcerated lesions in soft tissue examination is important in diagnosis and treatment.Publication Open Access A deep learning approach to automatic tooth detection and numbering in panoramic radiographs: An artificial intelligence study(2023-12-01) KESER, GAYE; NAMDAR PEKİNER, FİLİZ MEDİHA; MERTOĞLU D., KESER G., BAYRAKDAR İ. Ş., NAMDAR PEKİNER F. M., ÇELİK Ö., ORHAN K.Objective: n this study, in order to test the usability of artificial intelligence technologies in dentistry, which are becoming widespread and expanding day by day, and to investigate ways to benefit more from artificial intelligence technologies; a tooth detection and numbering study was performed on panoramic radiographs using a deep learning software. Methods: A radiographic dataset containing 200 anonymous panoramic radiographs collected from individuals over the age of 18 was assessed in this retrospective investigation. The images were separated into three groups: training (80%), validation (10%), and test (10%), and tooth numbering was performed with the DCNN artificial intelligence software. Results: The D-CNN system has been successful in detecting and numbering teeth. of teeth. The predicted precision, sensitivity, and F1 score were 0.996 (98.0%), 0.980 (98.0%), and 0.988 (98.8%), respectively. Conclusion: The precision, sensitivity and F1 scores obtained in our study were found to be high, as 0.996 (98.0%), 0.980 (98.0%) and 0.988 (98.8%), respectively. Although the current algorithm based on Faster R-CNN shows promising results, future studies should be done by increasing the number of data for better tooth detection and numbering results.Publication Open Access A deep learning algorithm for classification of oral lichen planus lesions from photographic images: a retrospective study(2023-02-01) NAMDAR PEKİNER, FİLİZ MEDİHA; KESER, GAYE; Keser G., Bayrakdar İ. Ş., Namdar Pekiner F. M., Çelik Ö., Orhan K.IntroductionDeep learning methods have recently been applied for the processing of medical images, and they have shown promise in a variety of applications. This study aimed to develop a deep learning approach for identifying oral lichen planus lesions using photographic images.Material and MethodsAnonymous retrospective photographic images of buccal mucosa with 65 healthy and 72 oral lichen planus lesions were identified using the CranioCatch program (CranioCatch, Eskişehir, Turkey). All images were re-checked and verified by Oral Medicine and Maxillofacial Radiology experts. This data set was divided into training (n =51; n=58), verification (n =7; n=7), and test (n =7; n=7) sets for healthy mucosa and mucosa with the oral lichen planus lesion, respectively. In the study, an artificial intelligence model was developed using Google Inception V3 architecture implemented with Tensorflow, which is a deep learning approach.ResultsAI deep learning model provided the classification of all test images for both healthy and diseased mucosa with a 100% success rate.ConclusionIn the healthcare business, AI offers a wide range of uses and applications. The increased effort increased complexity of the job, and probable doctor fatigue may jeopardize diagnostic abilities and results. Artificial intelligence (AI) components in imaging equipment would lessen this effort and increase efficiency. They can also detect oral lesions and have access to more data than their human counterparts. Our preliminary findings show that deep learning has the potential to handle this significant challenge.Publication Metadata only Evaluation of artificial intelligence for detecting periapical lesions on panoramic radiographs(2023-11-10) ÜNAL, SUAY YAĞMUR; KESER, GAYE; NAMDAR PEKİNER, FİLİZ MEDİHA; ÜNAL S. Y., KESER G., NAMDAR PEKİNER F. M., Yıldızbaş Z., Kurt M. A.