Kim KR, et al. Yonsei Med J. 2021 Jan;62(1):59-67. https://doi.org/10.3349/ymj.2021.62.1.59

Objective: This multicenter study compared radiological parameters and clinical outcomes between surgical and nonsurgical management and investigated treatment characteristics associated with the successful management of unstable atlas fractures.Methods: We retrospectively evaluated 53 consecutive patients with unstable atlas fracture who underwent halo-vest immobilization (HVI) or surgical fixation. Clinical outcomes were assessed using neck visual analogue scale and disability index. The radiological assessment included total lateral mass displacement (LMD) and the anterior atlantodental interval (AADI).Results: Thirty-two patients underwent surgical fixation and 21 received HVI (mean follow-up, 24.9 months). In the surgical fixation, but not in the HVI, LMD, and AADI showed statistically significant improvements at the last follow-up. The osseous healing rate and time-to-healing were 100% and 14.3 weeks with surgical fixation, compared with 71.43% and 20.0 weeks with HVI, respectively. Patients treated with HVI showed poorer neck pain and neck disability outcomes than those who received surgical treatment. LMD showed an association with osseous healing outcomes in nonoperative management. Clinical outcomes and osseous healing showed no significant differences according to Dickman’s classification of transverse atlantal ligament injuries.Conclusion: Surgical internal fixation had a higher fusion rate, shorter fracture healing time, more favorable clinical outcomes, and a more significant reduction in LMD and AADI compared to nonoperative management. The pitfalls of external immobilization are inadequate maintenance and a lower probability of reducing fractured lateral masses. Stabilization by surgical reduction with interconnected fixation proved to be a more practical management strategy than nonoperative treatment for unstable atlas fractures.

Shin JJ, et al. Yonsei Med J. 2022 Jan;63(1):72-81. https://doi.org/10.3349/ymj.2022.63.1.72

Kim KR, et al. Yonsei Med J. 2021 Mar;62(3):240-248. https://doi.org/10.3349/ymj.2021.62.3.240

Objective : A chronic subdural hematoma (CSDH) is a collection of bloody fluid located in the subdural space and encapsulated by neo-membranes. An inner subdural hygroma (ISH) is observed between the inner membrane of a CSDH and the brain surface. We present six cases of CSDH combined with ISH treated via endoscopy.Methods : Between 2011 and 2022, among the 107 patients diagnosed with CSDH in our institute, six patients were identified as presenting with CSDH combined with ISH and were included in this study. Preoperative computerized tomography (CT) and magnetic resonance imaging (MRI) were performed simultaneously, and endoscopic surgery for aspiration of the hematoma was performed in all cases of CSDH combined with ISH.Results : The mean age of patients was 71 years (range, 66 to 79). The patients were all male. In two cases, the ISH was not identified on CT, but was clearly seen on MRI in all patients. The inner membrane of the CSDH was tense and bulging after draining of the CSDH in endoscopic view due to the high pressure of the ISH. After fenestration of the inner membrane of the CSDH and aspiration of the ISH, the membrane was sunken down due to the decreasing pressure of the ISH. There was one recurrence in post-operative 2-month follow up. The symptoms improved in all patients after surgery, and there were no surgery-related complications.Conclusion : CSDH combined with ISH can be diagnosed on imaging, and endoscopic surgery facilitates safe and effective treatment.

This study measured parameters automatically by marking the point for measuring each parameter on whole-spine radiographs. Between January 2020 and December 2021, 1017 sequential lateral whole-spine radiographs were retrospectively obtained. Of these, 819 and 198 were used for training and testing the performance of the landmark detection model, respectively. To objectively evaluate the program’s performance, 690 whole-spine radiographs from four other institutions were used for external validation. The combined dataset comprised radiographs from 857 female and 850 male patients (average age 42.2 ± 27.3 years; range 20–85 years). The landmark localizer showed the highest accuracy in identifying cervical landmarks (median error 1.5–2.4 mm), followed by lumbosacral landmarks (median error 2.1–3.0 mm). However, thoracic landmarks displayed larger localization errors (median 2.4–4.3 mm), indicating slightly reduced precision compared with the cervical and lumbosacral regions. The agreement between the deep learning model and two experts was good to excellent, with intraclass correlation coefficient values >0.88. The deep learning model also performed well on the external validation set. There were no statistical differences between datasets in all parameters, suggesting that the performance of the artificial intelligence model created was excellent. The proposed automatic alignment analysis system identified anatomical landmarks and positions of the spine with high precision and generated various radiograph imaging parameters that had a good correlation with manual measurements.