İstanbul Sağlık ve Teknoloji Üniversitesi Kurumsal Akademik Arşivi
DSpace@İSTÜN, Üniversite mensupları tarafından doğrudan ve dolaylı olarak yayınlanan; kitap, makale, tez, bildiri, rapor, araştırma verisi gibi tüm akademik kaynakları uluslararası standartlarda dijital ortamda depolar, Üniversitenin akademik performansını izlemeye aracılık eder, kaynakları uzun süreli saklar ve telif haklarına uygun olarak Açık Erişime sunar.
Güncel Gönderiler
Impact of lightness differences in digitally simulated composite resin restorations on perceived smile attractiveness
(Wiley, 2025) Ntovas, Panagiotis; Ünal, Tuna; Korkut, Bora; Ferraris, Federico; Fehmer, Vincent; Sailer, Irena
Objectives: To investigate the effect of lightness differences between digitally simulated composite restorations and anterior maxillary teeth, in combination with restoration type, and clinical experience on perceived smile attractiveness. Materials and Methods: An imaging software program (Adobe Photoshop CC 2023) was used to digitally manipulate a frontal full-face portrait of a smiling female model, to create five types of moderate-sized composite resin restorations of moderate size. For each restoration 14 lightness differences were simulated. The image was digitally modified to simulate five different types of composite resin restorations (Class III, Class IV, Class V, diastema closure (bilateral and unilateral approach)). Each restoration was adjusted through 7 incremental increases and 7 incremental decreases of 1 unit in lightness (L* value), yielding a total of 70 images. The smile attractiveness of each picture was rated by 80 dentists and 80 laypersons, ranged from 21 to 77years using a visual analog scale. The Wilcoxon signed-rank test was employed to assess whether the mean of a sample significantly differed from the control (p<0.05). Results: Among the different restoration types, crown fracture repairs (Class IV) had the greatest negative impact, followed by proximal restorations (Class III), diastema closures (Bilateral approach), diastema closures (Unilateral approach), and, lastly, cervical restorations (Class V), which had the least impact on perceived smile attractiveness (p≤0.05). The influence of lightness differences, whether toward a darker or lighter restoration, was dependent on both the type of restoration and the observer's experience. Conclusions: The effect of lightness difference on perceived smile attractiveness was significantly influenced by both the type of composite resin restoration and the observer's experience. Dental professionals perceived lightness discrepancies as less attrac tive compared to laypersons, suggesting that experience plays a key role in the perception of esthetic outcomes. Clinical Significance: The repositioning of an esthetic direct dental restoration is highly influenced by the dentist's chromatic perception which is more sensitive than that of a layperson who evaluates its matching with the natural tooth. The findings of the present study can support evidence-based clinical decision-making.
Complementary and integrative medicine for the treatment of tourette’s syndrome
(Wiley, 2025) Pringsheim, Tamara; Deans, Catherine; Anis, Saar; Bhatia, Poonam; Black, Kevin; Değirmenci, Yıldız; Gilbert, Donald; Hartmann, Andreas; Hull, Mariam; Malaty, Irene
Background: There is widespread interest in complementary and integrative medicine (CIM) among people with Tourette's syndrome (TS). Objective: To perform a systematic review of evidence on the use of CIM to reduce tics and improve tic-related quality of life. Methods: We included clinical studies of CIM in children, adolescents and adults with TS and chronic tic disorders, and assessed the change in tic severity and/or tic-related quality of life using validated scales. Risk of bias of randomized controlled trials was assessed using the risk of bias tool of the American Academy of Neurology, which classifies studies into Class I, II, III or IV based on quality criteria. Results: 49 clinical studies and three systematic reviews were included. Most studies were rated Class IV and therefore at high risk of bias. Class I studies demonstrated efficacy of functional MRI neurofeedback, 5-Ling granule, Jingxin Zhidong formula, and Ningdong granule in reducing tic severity. Class II studies suggest efficacy of mindfulness-based intervention for tics, acupuncture combined with atlantoaxial joint bone setting therapy, and art therapy. Systematic reviews summarizing the Chinese literature on acupuncture, acupuncture with herbal medicine and massage therapy suggest greater reduction in tics compared to conventional treatments but there is low confidence in the evidence due to poor methodological quality of included studies. Conclusions: Evidence to support the use of complementary and integrative medicine for TS is limited in methodological quality and widespread applicability. These limitations prohibit evidence-based recommendations for general use among individuals with TS.
Decoding surgical proficiency and complexity: A machine learning framework for robotic herniorrhaphy
(Springer Nature Link, 2025) Shin, Thomas H.; Fanta, Abeselom; Gökçal, Fahri; Shields, Mallory; Benlice, Çiğdem; Kudsi, Omar Yusef
Objective To evaluate the predictive value of objective performance indicators (OPIs) for case complexity assessment and explore their role in quantifying skill acquisition during robotic ventral herniorrhaphy. Summary background data Despite advances in herniorrhaphy techniques, unclear metrics of case complexity have signifi cant implications for operative planning, resource allocation, and patient outcomes. While existing complexity definitions rely primarily on clinical factors external to operator behavior, the expanding adoption of robotic platforms in ventral her nia repair provides unprecedented access to quantifiable surgical performance metrics. However, the relationship between these objective performance indicators and both case complexity and skill development remains incompletely understood, representing a gap that machine learning approaches may help address. Methods OPI and clinical data from 561 consecutive robotic ventral hernia repairs over eight years were analyzed using iterative ensemble machine learning models to predict case complexity. Dimensional reduction analyses using t-distributed stochastic neighbor embedding tracked skill evolution, with Euclidean distances calculated between successive cases to quantify skill acquisition over time. Results Gradient boosting models integrating clinical and OPI variables achieved F1 score of 0.87, while OPIs alone scored 0.58. Longitudinal analysis revealed high OPI variability during early cases, stabilizing within 10 months despite increas ing case complexity, indicating skill acquisition may compensate for procedural difficulty. Dimensional reduction analyses captured this evolution through weighted Euclidean distances. Conclusions Objective performance indicators poorly predict case complexity independently, yet their temporal evolution reveals surgical skill acquisition. The concurrent stabilization of OPI stochasticity and progression to more complex cases demonstrates that surgical proficiency and complexity assessment are interdependent phenomena, establishing digital metrics as tools for understanding the dynamic relationship between surgeon learning and case difficulty.
Change in the concentration of interleukin-10 and tumor necrosis factor-α in gingival crevicular fluid after probiotic use in patients undergoing treatment with fixed orthodontic appliances
(Springer Nature Link, 2025) Erdemir, Cihan; Alkumru, Pınar; Çıracı, Enver; Ekenoğlu Merdan, Yağmur; Gök Yurttaş, Asiye; Amasya, Hakan; Elgün, Tuğba
Purpose This study aimed to evaluate the effect of the use of chewable probiotic tablets on interleukin-10 (IL-10) and tumor necrosis factor-α (TNF-α) levels in gingival crevicular fluid (GCF) in patients undergoing treatment with fixed orthodontic appliances. Methods This prospective case–control study involved 60 patients undergoing treatment with fixed orthodontic appliances. Participants were divided into two groups. The test group was administered probiotic chewable tablets (Motiflor AS, Abfen Farma, Ankara, Turkey) once daily for 15 days, and the control group received routine orthodontic treatment without probiotics. GCF samples were collected from each participant at two time points: at the beginning of the treatment (T0) and on the 21st day (T1). Samples were obtained separately from all four canines using collection strips. The levels of IL-10 and TNF-α in GCF were analyzed using the enzyme-linked immunosorbent assay (ELISA) method. Statistical tests were performed to assess the normality of the distribution of quantitative variables. All analyses were performed using GraphPad Prism (version 9.1.1, GraphPad Software, San Diego, CA, USA). Data normality was assessed using the Kolmogorov–Smirnov test. Friedman’s test for repeated measures was employed, followed by Dunn’s post hoc test. Results The variability that was observed for the IL-10 cytokine levels in the control group was significantly higher than that for the test group (p< 0.05). IL-10 levels in the test group increased while the TNF-α levels decreased. T1/T0 ratio for TNF-α was found to be lower in the test group compared to the control group (p= 0.002). Conclusion The results suggest that probiotic tablets may play a role in modulating IL-10 and TNF-α levels during orthodontic tooth movement. However, the current study was limited to the first 21 days of mechanical force application to the teeth, and it is recommended to investigate the long-term effects or other factors affecting cytokine changes in future studies.
A comparative study of deep learning models for automated liver and tumor segmentation in 2d contrast-enhanced MRI images
(IEEE, 2025) Tokatlı, Nazlı; Bilmez, Yakuphan; Bayram, Mücahit; Bayır, Beyzanur; Özalkan, Helin; Tekin, Zeynep; Örmeci, Necati; Altun, Halis
This paper presents a comprehensive investigation into deep learning techniques for the automated segmentation of the liver and tumors from 2D abdominal contrast-enhanced Magnetic Resonance Imaging (MRI) slices. Addressing a significant challenge in medical image analysis, our study leverages the public ATLAS dataset [1], using a selection of 60 3D abdominal MRI scans, from which we extracted approximately 3,750 2D slices for model training and evaluation. The core objective was the precise identification and delineation of both the liver organ and any intrahepatic lesions. A comparative analysis was conducted on three U-Net-based architectures: the standard Attention U-Net model incorporating EfficientNet-b3 and CBAM but without Focal Loss, the Attention U-Net model with integrated Focal Loss, and the ResNet34-Based U-Net model. To optimize performance, we explored the efficacy of different loss functions, namely DiceLoss and a hybrid DiceLoss with Focalcoss. Our findings are promising: Among the evaluated models, the ResNet34-Based U-Net demonstrated the highest performance with a Dice score of 91.36% and an IoU score of 89.52%. It was followed by the Attention U-Net with Focal Loss, which achieved 86.41% Dice and 81.61% IoU scores, and the standard Attention U-Net, which obtained 85.93% Dice and 81.19% IoU scores. These results underscore the significant potential of our 2D-based methodology to enhance the precision and efficiency of liver and tumor detection from abdominal scans, offering a valuable tool to support clinicians in early diagnosis and to alleviate their workload.
