Browsing by Author "Gurger, Murat"

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Does Arthroscopic Rotator Cuff Repair Improve Kinesiophobia, Depression, and Spatiotemporal Parameters in the Long Term
(Erciyes University School of Medicine, 2023) Deniz, Gulnihal; Bilek, Furkan; Esmez, Omer; Gulkesen, Arif; Gurger, Murat
Objective: This study aimed to investigate the long-term effects on pain, kinesiophobia, depression, functional capacity, balance, mobility, and spatiotemporal parameters in individuals who underwent rotator cuff (RC) surgery.Materials and Methods: Measurements were conducted on 45 individuals recommended for RC arthroscopy. These included bilateral upper extremity range of motion (ROM), muscle strength, bilateral hand grip strength, spatiotemporal parameters, 9-hole peg test (9-HPT), Beck Depression Inventory (BDI), Tampa Scale of Kinesiophobia (Tampa), Shoulder Pain and Disability Index (SPADI), and Constant-Murley Score. All measurements were taken one week before and six months after the arthroscopic intervention.Results: Post-RC arthroscopy results showed significant improvements in upper extremity ROM, muscle strength, hand grip strength, 9-HPT, Tampa, BDI, SPADI, and Constant-Mur-ley Score compared to pre-arthroscopy measurements. Spatiotemporal parameters such as total weight transfer, step cycle duration, double stance duration, step length, gait cycle length, foot angle, and cadence values were highly significant in both operated and non-operated extremities after arthroscopic surgery (p<0.01). However, hindfoot pressure analysis and swing phase values were significant only on the operated side after arthroscopic surgery (p<0.05).Conclusion: Prior to RC arthroscopy, individuals exhibited kinesiophobia, depression, reduced functional capacity, balance asymmetry, decreased mobility, and, consequently, spatiotemporal parameter asymmetry between the extremities. Gait disturbances (lengthened swing phase, decreased step length, increased foot angle), balance loss, and arm sway asymmetry were also evident before RC arthroscopy. Based on these findings, we suggest incorporating balance and gait training into the early rehabilitation program after RC arthroscopy.
Turkernextv2: An Innovative CNN Model for Knee Osteoarthritis Pressure Image Classification
(MDPI, 2025) Esmez, Omer; Deniz, Gulnihal; Bilek, Furkan; Gurger, Murat; Barua, Prabal Datta; Dogan, Sengul; Tuncer, Turker
Background/Objectives: Lightweight CNNs for medical imaging remain limited. We propose TurkerNeXtV2, a compact CNN that introduces two new blocks: a pooling-based attention with an inverted bottleneck (TNV2) and a hybrid downsampling module. These blocks improve stability and efficiency. The aim is to achieve transformer-level effectiveness while keeping the simplicity, low computational cost, and deployability of CNNs. Methods: The model was first pretrained on the Stable ImageNet-1k benchmark and then fine-tuned on a collected plantar-pressure OA dataset. We also evaluated the model on a public blood-cell image dataset. Performance was measured by accuracy, precision, recall, and F1-score. Inference time (images per second) was recorded on an RTX 5080 GPU. Grad-CAM was used for qualitative explainability. Results: During pretraining on Stable ImageNet-1k, the model reached a validation accuracy of 87.77%. On the OA test set, the model achieved 93.40% accuracy (95% CI: 91.3-95.2%) with balanced precision and recall above 90%. On the blood-cell dataset, the test accuracy was 98.52%. The average inference time was 0.0078 s per image (approximate to 128.8 images/s), which is comparable to strong CNN baselines and faster than the transformer baselines tested under the same settings. Conclusions: TurkerNeXtV2 delivers high accuracy with low computational cost. The pooling-based attention (TNV2) and the hybrid downsampling enable a lightweight yet effective design. The model is suitable for real-time and clinical use. Future work will include multi-center validation and broader tests across imaging modalities.