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
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.
An AI-powered mobile application for aroid identification and interactive learning: Enhancing pharmacognosy education through deep learning and NLP
(IEEE, 2025) Tokatlı, Nazlı; Bilmez, Yakuphan; Kılıç, Yusuf; Alpınar, Abdülkerim
Aroid plants (Araceae family), recognized for their distinct inflorescence, possess significant botanical, pharmaceutical, and practical importance due to their content of both beneficial compounds and toxins such as calcium oxalate crystals. Accurate identification of these species is particularly crucial in pharmacy education; however, morphological similarities among Aroid species often lead to confusion among students. This paper presents a deep learning-based mobile application designed to support both plant identification and interactive learning. The solution leverages EfficientNet and Convolutional Neural Network (CNN) architectures, achieving up to 96 % accuracy in classifying Aroid species. The visual classification model, trained on a comprehensive dataset, is deployed via a RESTful API and integrated within a Flutter-based mobile application. In addition, the app incorporates a Natural Language Processing (NLP)-powered chatbot to address user inquiries regarding plant characteristics and care. While technical evaluations demonstrate robust model performance, a comprehensive user evaluation aimed at assessing the system's educational value, usability, and chatbot interaction is planned as future work. This study underscores the potential of AI-driven mobile solutions in advancing pharmacognosy education, with future developments aimed at expanding the app's botanical scope and enhancing user engagement based on forthcoming survey results.
Does platelet-rich fibrin improve the clinical outcomes of intentional replantation in the treatment of periodontally hopeless teeth?
(İstanbul University Press, 2025) Parlak, Hanife Merva; Parmaksız, Ayhan; Uyanık, Mehmet Özgür; Duruel, Onurcem; Keçeli, Hüseyin Gencay
Purpose The purpose of this study was to evaluate the impact of using platelet-rich fibrin (PRF) on clinical parameters in intentional replantation (IR) treatment. Materials and Methods Data were obtained from 32 mandibular anterior teeth with a 15-month follow up, treated with either IR (n=17) or IR+PRF (n=15). Periodontal parameters included probing depth (PD), clinical attachment level (CAL), plaque index (PI), gingival index (GI), and keratinized tissue height (KTH), all of which were assessed retrospectively. Results PD reduction at mesial and midlingual sites was greater in the IR+PRF group at the 15-month follow-up (p=0.043 and p=0.017, respectively), whereas CAL gain in the IR+PRF group was significantly higher at 3, 6, and 15 months (p<0.05). GI scores were similar in both groups, while PI scores were higher in the IR group at 6 and 15 months (p<0.05). Changes in KTH were similar in both groups at all follow-up periods. Conclusion IR can be considered for the treatment of periodontally hopeless mandibular anterior teeth, and combining IR with PRF may improve clinical outcomes. However, its clinical use should be recommended cautiously due to the lack of histological data regarding the effects of PRF on IR healing.
