Objective: This study aimed to assess the degree of interest in robot-assisted spine surgery (RASS) among residents and to investigate the learning curve for beginners performing robotic surgery. Methods: We conducted a survey to assess awareness and interest in RASS among young neurosurgery residents. Subsequently, we offered a hands-on training program using a dummy to educate one resident. After completing the program, the trained resident performed spinal fusion surgery with robotic assistance under the supervision of a mentor. The clinical outcomes and learning curve associated with robotic surgery were then analyzed. Results: Neurosurgical residents had limited opportunities to participate in spinal surgery during their training. Despite this, there was a significant interest in the emerging field of robotic surgery. A trained resident performed RASS under the supervision of a senior surgeon. A total of 166 screw insertions were attempted in 28 patients, with 2 screws failing due to skiving. According to the Gertzbein-Robbins classification, 85.54% of the screws were rated as grade A, 11.58% as grade B, 0.6% as grade C, and 1.2% as grade D. The clinical acceptance rate was approximately 96.99%, which is comparable to the results reported by senior experts and time per screw statistically significantly decreased as experience was gained. Conclusion RASS can be performed with high accuracy within a relatively short timeframe, if residents receive adequate training.

Objective: This study aimed to assess the degree of interest in robot-assisted spine surgery (RASS) among residents and to investigate the learning curve for beginners performing robotic surgery. Methods: We conducted a survey to assess awareness and interest in RASS among young neurosurgery residents. Subsequently, we offered a hands-on training program using a dummy to educate one resident. After completing the program, the trained resident performed spinal fusion surgery with robotic assistance under the supervision of a mentor. The clinical outcomes and learning curve associated with robotic surgery were then analyzed. Results: Neurosurgical residents had limited opportunities to participate in spinal surgery during their training. Despite this, there was a significant interest in the emerging field of robotic surgery. A trained resident performed RASS under the supervision of a senior surgeon. A total of 166 screw insertions were attempted in 28 patients, with 2 screws failing due to skiving. According to the Gertzbein-Robbins classification, 85.54% of the screws were rated as grade A, 11.58% as grade B, 0.6% as grade C, and 1.2% as grade D. The clinical acceptance rate was approximately 96.99%, which is comparable to the results reported by senior experts and time per screw statistically significantly decreased as experience was gained. Conclusion RASS can be performed with high accuracy within a relatively short timeframe, if residents receive adequate training.

Objective: This study aimed to assess the degree of interest in robot-assisted spine surgery (RASS) among residents and to investigate the learning curve for beginners performing robotic surgery. Methods: We conducted a survey to assess awareness and interest in RASS among young neurosurgery residents. Subsequently, we offered a hands-on training program using a dummy to educate one resident. After completing the program, the trained resident performed spinal fusion surgery with robotic assistance under the supervision of a mentor. The clinical outcomes and learning curve associated with robotic surgery were then analyzed. Results: Neurosurgical residents had limited opportunities to participate in spinal surgery during their training. Despite this, there was a significant interest in the emerging field of robotic surgery. A trained resident performed RASS under the supervision of a senior surgeon. A total of 166 screw insertions were attempted in 28 patients, with 2 screws failing due to skiving. According to the Gertzbein-Robbins classification, 85.54% of the screws were rated as grade A, 11.58% as grade B, 0.6% as grade C, and 1.2% as grade D. The clinical acceptance rate was approximately 96.99%, which is comparable to the results reported by senior experts and time per screw statistically significantly decreased as experience was gained. Conclusion RASS can be performed with high accuracy within a relatively short timeframe, if residents receive adequate training.

Purpose: Unnecessary prostate biopsies for detecting prostate cancer (PCa) should be minimized. Therefore, this study developed a machine learning (ML) model to predict PCa in Korean men and evaluated its usability. Materials and Methods: We retrospectively analyzed clinical data from 928 patients who underwent prostate biopsies at Kangwon National University Hospital between May 2013 and May 2023. Of these, 377 (41.6%) were diagnosed with PCa, and 551 (59.4%) did not have cancer. For external validation, clinical data from 385 patients aged 48–89 years who underwent prostate biopsies from September 2005 to September 2023 at Wonju Severance Christian Hospital were also included. Twenty-two clinical features were used to develop an ML model to predict PCa. Features were selected based on their contributions to model performance, leading to the inclusion of 15 features. A meta-learner was constructed using logistic regression to predict the probability of PCa, and the classifier was trained and validated on randomly extracted training and test sets at an 8:2 ratio. Results: The prostate health index, prostate volume, age, nodule on digital rectal examination, and prostate-specific antigen were the top 5 features for predicting PCa. The area under the receiver operating characteristic curve (AUC) of the meta-learner logistic regression model was 0.89, and the accuracy, sensitivity, and specificity were 0.828, 0.711, and 0.909, respectively. Our model also showed excellent prediction performance for high-grade PCa, with a Gleason score of 7 or higher and an AUC of 0.903. Furthermore, we evaluated the performance of the model using external cohort clinical data and achieved an AUC of 0.863. Conclusions Our ML model excelled in predicting PCa, specifically clinically significant PCa. Although extensive cross-validation in other clinical cohorts is needed, this ML model is a promising option for future diagnostics.

Purpose: Unnecessary prostate biopsies for detecting prostate cancer (PCa) should be minimized. Therefore, this study developed a machine learning (ML) model to predict PCa in Korean men and evaluated its usability. Materials and Methods: We retrospectively analyzed clinical data from 928 patients who underwent prostate biopsies at Kangwon National University Hospital between May 2013 and May 2023. Of these, 377 (41.6%) were diagnosed with PCa, and 551 (59.4%) did not have cancer. For external validation, clinical data from 385 patients aged 48–89 years who underwent prostate biopsies from September 2005 to September 2023 at Wonju Severance Christian Hospital were also included. Twenty-two clinical features were used to develop an ML model to predict PCa. Features were selected based on their contributions to model performance, leading to the inclusion of 15 features. A meta-learner was constructed using logistic regression to predict the probability of PCa, and the classifier was trained and validated on randomly extracted training and test sets at an 8:2 ratio. Results: The prostate health index, prostate volume, age, nodule on digital rectal examination, and prostate-specific antigen were the top 5 features for predicting PCa. The area under the receiver operating characteristic curve (AUC) of the meta-learner logistic regression model was 0.89, and the accuracy, sensitivity, and specificity were 0.828, 0.711, and 0.909, respectively. Our model also showed excellent prediction performance for high-grade PCa, with a Gleason score of 7 or higher and an AUC of 0.903. Furthermore, we evaluated the performance of the model using external cohort clinical data and achieved an AUC of 0.863. Conclusions Our ML model excelled in predicting PCa, specifically clinically significant PCa. Although extensive cross-validation in other clinical cohorts is needed, this ML model is a promising option for future diagnostics.

Objective: This study aimed to assess the degree of interest in robot-assisted spine surgery (RASS) among residents and to investigate the learning curve for beginners performing robotic surgery. Methods: We conducted a survey to assess awareness and interest in RASS among young neurosurgery residents. Subsequently, we offered a hands-on training program using a dummy to educate one resident. After completing the program, the trained resident performed spinal fusion surgery with robotic assistance under the supervision of a mentor. The clinical outcomes and learning curve associated with robotic surgery were then analyzed. Results: Neurosurgical residents had limited opportunities to participate in spinal surgery during their training. Despite this, there was a significant interest in the emerging field of robotic surgery. A trained resident performed RASS under the supervision of a senior surgeon. A total of 166 screw insertions were attempted in 28 patients, with 2 screws failing due to skiving. According to the Gertzbein-Robbins classification, 85.54% of the screws were rated as grade A, 11.58% as grade B, 0.6% as grade C, and 1.2% as grade D. The clinical acceptance rate was approximately 96.99%, which is comparable to the results reported by senior experts and time per screw statistically significantly decreased as experience was gained. Conclusion RASS can be performed with high accuracy within a relatively short timeframe, if residents receive adequate training.

Purpose: Unnecessary prostate biopsies for detecting prostate cancer (PCa) should be minimized. Therefore, this study developed a machine learning (ML) model to predict PCa in Korean men and evaluated its usability. Materials and Methods: We retrospectively analyzed clinical data from 928 patients who underwent prostate biopsies at Kangwon National University Hospital between May 2013 and May 2023. Of these, 377 (41.6%) were diagnosed with PCa, and 551 (59.4%) did not have cancer. For external validation, clinical data from 385 patients aged 48–89 years who underwent prostate biopsies from September 2005 to September 2023 at Wonju Severance Christian Hospital were also included. Twenty-two clinical features were used to develop an ML model to predict PCa. Features were selected based on their contributions to model performance, leading to the inclusion of 15 features. A meta-learner was constructed using logistic regression to predict the probability of PCa, and the classifier was trained and validated on randomly extracted training and test sets at an 8:2 ratio. Results: The prostate health index, prostate volume, age, nodule on digital rectal examination, and prostate-specific antigen were the top 5 features for predicting PCa. The area under the receiver operating characteristic curve (AUC) of the meta-learner logistic regression model was 0.89, and the accuracy, sensitivity, and specificity were 0.828, 0.711, and 0.909, respectively. Our model also showed excellent prediction performance for high-grade PCa, with a Gleason score of 7 or higher and an AUC of 0.903. Furthermore, we evaluated the performance of the model using external cohort clinical data and achieved an AUC of 0.863. Conclusions Our ML model excelled in predicting PCa, specifically clinically significant PCa. Although extensive cross-validation in other clinical cohorts is needed, this ML model is a promising option for future diagnostics.

Objective: This study aimed to assess the degree of interest in robot-assisted spine surgery (RASS) among residents and to investigate the learning curve for beginners performing robotic surgery. Methods: We conducted a survey to assess awareness and interest in RASS among young neurosurgery residents. Subsequently, we offered a hands-on training program using a dummy to educate one resident. After completing the program, the trained resident performed spinal fusion surgery with robotic assistance under the supervision of a mentor. The clinical outcomes and learning curve associated with robotic surgery were then analyzed. Results: Neurosurgical residents had limited opportunities to participate in spinal surgery during their training. Despite this, there was a significant interest in the emerging field of robotic surgery. A trained resident performed RASS under the supervision of a senior surgeon. A total of 166 screw insertions were attempted in 28 patients, with 2 screws failing due to skiving. According to the Gertzbein-Robbins classification, 85.54% of the screws were rated as grade A, 11.58% as grade B, 0.6% as grade C, and 1.2% as grade D. The clinical acceptance rate was approximately 96.99%, which is comparable to the results reported by senior experts and time per screw statistically significantly decreased as experience was gained. Conclusion RASS can be performed with high accuracy within a relatively short timeframe, if residents receive adequate training.

Purpose: This study aimed to describe adult living donor liver transplantation (LDLT) for acute liver failure and evaluate its clinical significance by comparing its surgical and survival outcomes with those of deceased donor liver transplantation (DDLT). Methods: We retrospectively reviewed the medical records of 267 consecutive patients (161 LDLT recipients and 106 DDLT recipients) aged 18 years or older who underwent liver transplantation between January 2006 and December 2020. Results: The mean periods from hepatic encephalopathy to liver transplantation were 5.85 days and 8.35 days for LDLT and DDLT, respectively (P = 0.091). Among these patients, 121 (45.3%) had grade III or IV hepatic encephalopathy (living, 34.8% vs. deceased, 61.3%; P < 0.001), and 38 (14.2%) had brain edema (living, 16.1% vs. deceased, 11.3%; P = 0.269) before liver transplantation. There were no significant differences in in-hospital mortality (living, 11.8% vs. deceased, 15.1%; P = 0.435), 10-year overall survival (living, 90.8% vs. deceased, 84.0%; P = 0.096), and graft survival (living, 83.5% vs. deceased, 71.3%;P = 0.051). However, postoperatively, the mean intensive care unit stay was shorter in the LDLT group (5.0 days vs. 9.5 days, P < 0.001). In-hospital mortality was associated with vasopressor use (odds ratio [OR], 3.40; 95% confidence interval [CI], 1.45–7.96; P = 0.005) and brain edema (OR, 2.75; 95% CI, 1.16–6.52; P = 0.022) of recipient at the time of transplantation. However, LDLT (OR, 1.26; 95% CI, 0.59–2.66; P = 0.553) was not independently associated with in-hospital mortality. Conclusion LDLT is feasible for acute liver failure when organs from deceased donors are not available.

Purpose: The purpose of this study was to compare the concurrent validity and test-retest reliability of ‘KPIMT Torticollis Protractor’, a smart phone and I-pad application for convenient range of motion measurement, and ‘Image J’, an analysis software with high reliability and validity, according to head tilt and active cervical rotation angle. This was done to determine the clinical utility of ‘KPIMT Torticollis Protractor’. Methods: Head tilt and active cervical spine rotation angles of 40 children with congenital muscular torticollis were measured using Image J and KPIMT Torticollis Protractor, respectively. The level of concurrent validity and inter-rater and intra-rater reliability between the two measurement methods were analyzed. Results: For forty participants, the concurrent validity between Image J and KPIMT Torticollis Protractor showed very high validity with ICC of ICC 0.977 (0.995-0.999), 0.994 (0.994-0.998), CVME% 0.71-0.72%, SEM% 0.31-0.34%, MDC% 0.86-0.94%. The test-retest intra-rater reliability showed very high reliability ICC 0.911 (0.911-0.966), CVME% 0.71%, SEM% 0.34-0.36%, MDC% 0.81-0.94%. The test-retest inter-rater showed very high reliability ICC 0.936 (0.933-0.957), CVME% 0.70%, SEM% 0.34-0.35%, MDC% 0.81-0.83%. Conclusion The KPIMT Torticollis Protractor, a smart phone and IPD application, is a highly reliable and valid device for angle measurement in children with congenital myotonia and can be easily used in clinical practice.