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Serindere, Gozde
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Dentistry

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Gingival Enlargement in Patients who Have Undergone Renal Transplants: A Meta-Analysis Serindere, Gozde; Ozveren, Neslihan
Journal of Dentistry Indonesia Vol. 25, No. 2
Publisher : UI Scholars Hub

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Severe gingival enlargement (GE) is one of the most commonly observed adverse effects in patients who have undergone renal transplants due to the use of cyclosporine A. Objectives: We aimed to gain more insight into the prevalence of GE in patients with renal transplants. Methods: We searched the PubMed and Web of Science databases for relevant studies from January 1990 to January 2018. Using random effects models, we calculated summary incidence rates and 95% confidence intervals (CIs). Results: A total of 595 patients from 10 studies were included. Patients using cyclosporine A with or without any other drugs had a 62.6% (95% CI, 41.9%–79.5%) incidence of GE. Subgroup analysis according to diagnostic criteria showed that the incidence of GE was lower when using well-defined diagnostic criteria or scoring system. The incidence of GE was 88.2% (95% CI, 80.9%–93.0%) in patients using cyclosporine A with nifedipine. Cyclosporine A without nifedipine was associated with a significantly decreased risk of GE incidence when compared with the combination of cyclosporine A and nifedipine (odds ratio: 0.198; 95% CI, 0.083–0.473; P < 0.001). Conclusions: It is important for all clinicians to know the effects of the aforementioned drugs and the treatment options.
Comparison of Ultrasonography and Cone Beam Computed Tomography in the Differential Diagnosis of Periapical Lesions: A Prospective Radiopathological Study Serindere, Gozde; Aktuna Belgin, Ceren; Bulte, Mert; Gursoy, Didar; Salimov, Fariz
Journal of Dentistry Indonesia Vol. 29, No. 3
Publisher : UI Scholars Hub

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Objective: The aim of this study is to evaluate the correlation between ultrasonography (USG) and cone beam computed tomography (CBCT) and the accuracy between histopathological diagnosis and preliminary diagnosis in the diagnosis of periapical lesion. Methods: 20 patients with periapical lesion in the jaw, were included in the study. The presence of expansion or perforation and dimensions of the lesion were performed with CBCT. In the examination of the lesion with USG, shape, echogenicity, vascularization of the lesion and the presence of buccal expansion and perforation, were determined. Subsequently, a biopsy was taken from the lesion for histopathological examination and the final result was compared with the accuracy of the preliminary diagnoses. Results: Kolmogorov-Smirnov test, Wilcoxon test (w) and Cohen’s kappa coefficient (κ) was used to analyze the data. Three of the 4 lesions diagnosed as periapical granuloma as a preliminary diagnosis were confirmed as periapical granuloma in histopathological examination. Periapical cyst was confirmed in histopathological examination of 14 of 16 lesions diagnosed as periapical cyst as a preliminary diagnosis. Mesiodistal (MD) measurements in CBCT measurements were significantly higher than the USG group (p <0.05). There was 100% agreement (p = 0.000) between the evaluation of buccal expansion, buccal perforation, and palatal-lingual perforation between CBCT and USG. Conclusion: It was concluded that the combined use of USG and CBCT can provide the clinician with important information in the diagnosis of periapical lesion.
Comparison of ChatGPT, Claude AI, and Dental Students in the Detection of Artifacts on Panoramic Radiography Erol, Elif Çeçen; Aktuna Belgin, Ceren; Serindere, Gözde; Gunduz, Kaan
Journal of Dentistry Indonesia Vol. 33, No. 1
Publisher : UI Scholars Hub

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Background: Accurate radiographic diagnosis requires images obtained with proper technique. Artifacts are unwanted irregularities or densities not produced by the primary X-ray beam and may obscure anatomical details in radiographic images. This retrospective study aimed to evaluate the performance of ChatGPT, Claude AI, and intern dental students in detecting artifacts in panoramic radiographs (PRs). Methods: Between January and December 2024, panoramic radiographs of 40 patients containing 74 artifacts (motion, mispositioning, airway/soft tissue, and foreign body/metal artifacts) were retrospectively evaluated. The artifact detection performance of ChatGPT-4.0, Claude AI 3.5 Sonnet, and intern dental students was subsequently evaluated and compared with the radiologist-defined gold standard. Results: Dental students demonstrated higher overall accuracy than both AI models in detecting artifacts on PRs. Among the LLMs, Claude AI showed higher accuracy than ChatGPT in detecting motion artifacts (65.0% vs 42.5%), foreign body/metal artifacts (90.0% vs 62.5%), and patient mispositioning (85.0% vs 67.5%), whereas ChatGPT performed better in identifying airway/soft tissue artifacts (87.5% vs 65.0%). Conclusions: ChatGPT and Claude AI demonstrated lower performance than dental students in detecting artifacts in panoramic radiographs. These findings suggest that human evaluation remains essential in radiographic interpretation, and further development of LLMs is needed for reliable clinical application.
Co-Authors Aktuna Belgin, Ceren Bulte, Mert Erol, Elif Çeçen Gunduz, Kaan Gursoy, Didar Ozveren, Neslihan Salimov, Fariz