PurposeBone flap resorption (BFR) after cranioplasty with an autologous bone flap (ABF) is well known. However, the prevalences and degrees of BFR remain unclear. This study aimed to evaluate changes in ABFs following cranioplasty and to investigate factors related with BFR.Materials and MethodsWe retrospectively reviewed 97 patients who underwent cranioplasty with frozen ABF between January 2007 and December 2016. Brain CT images of these patients were reconstructed to form three-dimensional (3D) images, and 3D images of ABF were separated using medical image editing software. ABF volumes on images were measured using 3D image editing software and were compared between images in the immediate postoperative period and at postoperative 12 months. Risk factors related with BFR were also analyzed.ResultsThe volumes of bone flaps calculated from CT images immediately after cranioplasty ranged from 55.3 cm3 to 175 cm3. Remnant bone flap volumes at postoperative 12 months ranged from 14.2% to 102.5% of the original volume. Seventy-five patients (77.3%) had a BFR rate exceeding 10% at 12 months after cranioplasty, and 26 patients (26.8%) presented severe BFR over 40%. Ten patients (10.3%) underwent repeated cranioplasty due to severe BFR. The use of a 5-mm burr for central tack-up sutures was significantly associated with BFR (p<0.001).ConclusionMost ABFs after cranioplasty are absorbed. Thus, when using frozen ABF, patients should be adequately informed. To prevent BFR, making holes must be kept to a minimum during ABF grafting.

ObjectiveIn general, quadriplegic patients use their voices to call the caregiver. However, severe quadriplegic patients are in a state of tracheostomy, and cannot generate a voice. These patients require other communication tools to call caregivers. Recently, monitoring of eye status using artificial intelligence (AI) has been widely used in various fields. We made eye status monitoring system using deep learning, and developed a communication system for quadriplegic patients can call the caregiver.MethodsThe communication system consists of 3 programs. The first program was developed for automatic capturing of eye images from the face using a webcam. It continuously captured and stored 15 eye images per second. Secondly, the captured eye images were evaluated for open or closed status by deep learning, which is a type of AI. Google TensorFlow was used as a machine learning tool or library for convolutional neural network. A total of 18,000 images were used to train deep learning system. Finally, the program was developed to utter a sound when the left eye was closed for 3 seconds.ResultsThe test accuracy of eye status was 98.7%. In practice, when the quadriplegic patient looked at the webcam and closed his left eye for 3 seconds, the sound for calling a caregiver was generated.ConclusionOur eye status detection software using AI is very accurate, and the calling system for the quadriplegic patient was satisfactory.

Most studies on the ossification of the posterior longitudinal ligament (OPLL) using the finite element method were conducted in the neutral state, and the resulting decompression was judged to be good. As these studies do not reflect the actual behavior of the cervical spine, this study conducted an analysis in the neutral state and a biomechanical analysis during flexion and extension behaviors. After validation via the construction of an intact cervical spine model, the focal OPLL model was inserted into the C4–C5 segment and a simulation was performed. The neutral state was shown by applying a fixed condition to the lower part of the T1 and Y-axis fixed condition of the spinal cord and simulating spinal cord compression with OPLL. For flexion and extension simulation, a ±30-degree displacement was additionally applied to the top of the C2 dens. Accordingly, it was confirmed that spinal cord decompression did not work well during the flexion and extension behaviors, but rather increased. Thus, if patients with focal OPLL inevitably need to undergo posterior decompression, additional surgery using an anterior approach should be considered.

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Objective The regional trauma center (RTC) in our hospital was established in November 2015. The Korean Trauma Data Bank (KTDB) was established in 2013 and maintains a prospective database. In this study, based on KTDB data, we investigated the characteristics of traumatic brain injury (TBI) in patients who visited our RTC. Methods Between 2017 and 2021, we analyzed the data of 1,939 patients with TBI. Demographic characteristics of patients were recorded, and variables such as transfer information, mechanism of injury, severity, occupational relevance, multiple trauma, and surgery were analyzed. Hospital length of stay (LOS), fatality, and Glasgow outcome scale (GOS) were analyzed to confirm treatment outcomes. Results This study enrolled 1,939 patients with a median age of 58 years and male predominance (75.5%). The transfer time decreased (from 1.95 hours to 1.1 hours) following an increase in the frequency of direct transfers to our hospital each year. Motor vehicle-related accidents (48.4%) were implicated as the most common cause, and the severity of TBI showed an increasing trend each year. The outcomes confirmed that the fatality rate and GOS scores deteriorated. The mean LOS in the hospital was 26.92 days, with a fatality rate of 23.6% (458 patients). Conclusion In this study, we investigated characteristics and treatment outcomes associated with TBI. Our research confirms that patients with TBI are currently well triaged at the accident site and rapidly transferred to our RTC. Follow-up studies are necessary to establish strategies for improved treatment outcomes.