No abstract

Caused by high-energy traumas, pelvic injuries are life-threatening complex injuries posing several unfavorable effects on the body. Pelvic fractures account for approximately 3-4.3% of all skeletal fractures. [1,2] Mortality rates due to pelvic injuries vary from 10 to 16%. [1,3] Victims of these injuries display an increased tendency to develop complications due to the nature of the injury and modes of treatment. [4-8] Complications are critical for patient survival and sequelae development. Percutaneous iliosacral and transsacral-transiliac screw fixation are the commonly preferred surgical methods for the treatment of posterior pelvic injuries. The advantages of this technique include minimal tissue damage due to the surgical procedure, improved stability of the fixation, short surgery time, and reduced risk for wound-related problems in the postoperative period. [9,10] However, patients may be prone to develop complications due to some factors, including variability in pelvic anatomy, the narrowness of the bone corridor to be screwed, and inadequacies in imaging and Objectives: This study aims to determine the role of computed tomography (CT)-derived templates, produced by threedimensional (3D) modeling, image processing and printing technology, in percutaneous transsacral screw fixation and evaluate the effects of their use on surgical success. Materials and methods: This prospective study conducted between June 2018 and December 2019 utilized 15 composite pelvis models for transsacral-transiliac screw fixation. For the procedure, modeled templates were utilized for wiring on the left side of the pelvis models, while the conventional method was performed on the right side of the pelvis models. In the computed tomography images acquired after wiring, appropriate wire position was evaluated. Results: The placed wires held the S1 body appropriately in all of the procedures with or without template use. With the template use, the wires were placed appropriately in the surgical bone corridor suitable for the transsacral-transiliac screw fixation in all of the models. However, with the conventional methods, the wires were not placed in the safe surgical bone corridor in four models. The wire deviation angle in the axial plane was significantly lower in the template group (p=0.001), whereas it was not different between the template group and the conventional method group in the coronal plane (p=0.054). The amount of deviation from the ideal wire entry site was significantly reduced in the template group compared to the conventional method group (p=0.001). Conclusion: With the use of 3D modeling and printing technology, CT-derived templates can be produced and utilized for transsacral screw fixation procedures and their use increases surgical success by reducing the surgical margin of error.

ÖzetOsteoporoz, vücudumuzdaki kemiklerin sertliklerinin azalıp, kalitelerinin bozulması sonucunda daha zayıf ve kırılabilir hale gelmeleri ile ortaya çıkan ve tüm iskeletimizi etkileyen sistemik bir hastalıktır. Bu çalışmada, bir iskelet hastalığı olan osteoporozun ön tanısında kullanılan X-ray absorbsiyometri (DEXA) testinin radyasyon dezavantajı sebebiyle, buna alternatif ve yapay zeka tabanlı, doğruluk değeri yüksek bir karar destek sistemi oluşturmak amaçlanmıştır. Gerçekleştirilecek sistem bir ön tanı yöntemi olarak kullanılacaktır. Bunun için, 70 hastadan alınan belirli parametrelerden oluşturulan veri seti yardımı ile tasarlanan olasılıksal sinir ağı (OSA) kullanılmıştır. Elde edilen başarı oranı ile Yapay sinir ağlarının osteoporoz hastalığının teşhisinde karar destek sistemi olarak kullanılabileceği görülmüştür. Bu çalışma sayesinde bu hastalığın şüphesi ile ilgili birime gelecek tüm hastalara DEXA testinin uygulanma olasılığı aza indirgenmiş olacaktır. AbstractOsteoporosis is a skeletal disorder characterized by low bone density and micro-architectural deterioration of bony tissue. Dual-energy x-ray absorptiometry (DEXA) uses x-ray beams at two photon energies to estimate bone mineral density (BMD). This method has been applied extensively to detect osteoporosis. Due to the radiation disadvantage of the DEXA test, alternatively, an artificial intelligence-based decision support system was aimed. The system to be performed will be used as a preliminary diagnosis method for osteoporosis. For this, the probabilistic neural network (PNN) was used from 70 patient's specific parameters. It has been observed that artificial neural networks can be used as a decision support system in the diagnosis of osteoporosis. Thanks to this study, the probability of the application of the DEXA test will be reduced to a minimum for all the patients who are suspected of having this disease.