Deep learning-based identification of spine growth potential on EOS radiographs.

OBJECTIVES: To develop an automatic computer-based method that can help clinicians in assessing spine growth potential based on EOS radiographs. METHODS: We developed a deep learning-based (DL) algorithm that can mimic the human judgment process to automatically determine spine growth potential and the Risser sign based on full-length spine EOS radiographs. A total of 3383 EOS cases were collected and used for the training and test of the algorithm. Subsequently, the completed DL algorithm underwent clinical validation on an additional 440 cases and was compared to the evaluations of four clinicians. RESULTS: Regarding the Risser sign, the weighted kappa value of our DL algorithm was 0.933, while that of the four clinicians ranged from 0.909 to 0.930. In the assessment of spine growth potential, the kappa value of our DL algorithm was 0.944, while the kappa values of the four clinicians were 0.916, 0.934, 0.911, and 0.920, respectively. Furthermore, our DL algorithm obtained a slightly higher accuracy (0.973) and Youden index (0.952) compared to the best values achieved by the four clinicians. In addition, the speed of our DL algorithm was 15.2 ± 0.3 s/40 cases, much faster than the inference speeds of the clinicians, ranging from 177.2 ± 28.0 s/40 cases to 241.2 ± 64.1 s/40 cases. CONCLUSIONS: Our algorithm demonstrated comparable or even better performance compared to clinicians in assessing spine growth potential. This stable, efficient, and convenient algorithm seems to be a promising approach to assist doctors in clinical practice and deserves further study. CLINICAL RELEVANCE STATEMENT: This method has the ability to quickly ascertain the spine growth potential based on EOS radiographs, and it holds promise to provide assistance to busy doctors in certain clinical scenarios. KEY POINTS: • In the clinic, there is no available computer-based method that can automatically assess spine growth potential. • We developed a deep learning-based method that could automatically ascertain spine growth potential. • Compared with the results of the clinicians, our algorithm got comparable results.

Accuracies of various types of spinal robot in robot-assisted pedicle screw insertion: a Bayesian network meta-analysis.

BACKGROUND: With the popularization of robot-assisted spinal surgeries, it is still uncertain whether robots with different designs could lead to different results in the accuracy of pedicle screw placement. This study aimed to compare the pedicle screw inserting accuracies among the spinal surgeries assisted by various types of robot and estimate the rank probability of each robot-assisted operative technique involved. METHODS: The electronic literature database of PubMed, Web of Science, EMBASE, CNKI, WANFANG and the Cochrane Library was searched in November 2021. The primary outcome was the Gertzbein-Robbins classification of pedicle screws inserted with various operative techniques. After the data extraction and direct meta-analysis process, a network model was established in the Bayesian framework and further analyses were carried out. RESULTS: Among all the 15 eligible RCTs, 4 types of robot device, namely Orthbot, Renaissance, SpineAssist and TiRobot, were included in this study. In the network meta-analysis, the Orthbot group (RR 0.27, 95% CI 0.13-0.58), the Renaissance group (RR 0.33, 95% CI 0.14-0.86), the SpineAssist group (RR 0.14, 95% CI 0.06-0.34) and the conventional surgery group (RR 0.21, 95% CI 0.13-0.31) were inferior to the TiRobot group in the proportion of grade A pedicle screws. Moreover, the results of rank probabilities revealed that in terms of accuracy, the highest-ranked robot was TiRobot, followed by Renaissance and Orthbot. CONCLUSIONS: In general, current RCT evidence indicates that TiRobot has an advantage in the accuracy of the pedicle screw placement, while there is no significant difference among the Orthbot-assisted technique, the Renaissance-assisted technique, the conventional freehand technique, and the SpineAssist-assisted technique in accuracy.

Metabolic Syndrome, as Defined Based on Parameters Including Visceral Fat Area, Predicts Complications After Surgery for Rectal Cancer.

BACKGROUND/OBJECTIVES: Metabolic syndrome (MetS) has become a major public health problem. However, few studies have examined the impact of MetS on the postoperative complications of colorectal cancer and the conclusions remain controversial. The present study aimed to investigate whether MetS, as defined based on visceral fat area (VFA) instead of BMI or waist circumference, would predict complications after surgery for rectal cancer. SUBJECTS/METHODS: We conducted a retrospective study of patients who underwent surgery for rectal cancer at our department between January 2013 and August 2018. Univariate and multivariate analyses evaluating the risk factors for postoperative complications were performed. A receiver operating characteristic curve analysis was used to determine the gender-specific cut-off values for VFA. RESULTS: A total of 381 patients were included in the study. The optimal cut-off values for VFA were 117.9 cm2 for men and 76.9 cm2 for women, and 153 patients were diagnosed as having MetS. The rate of postoperative complication was significantly higher in the MetS group than that in the non-MetS group (34.6% versus 15.8%, P < 0.001). The multivariate logistic regression analysis demonstrated that MetS (OR 3.712, P < 0.001), NRS 2002 scores ≥ 3 (OR 2.563, P = 0.001), and tumor located at the lower 1/3 (OR 3.290, P = 0.001) were independent risk factors for complications after surgery for rectal cancer. CONCLUSION: Metabolic syndrome, as defined based on parameters including visceral fat area, was an independent risk factor for complications after surgery for rectal cancer.

Factors associated with the decision for operative versus conservative treatment of displaced distal radius fractures in the elderly.

BACKGROUND: The treatment of senile distal radius fractures had not been clearly defined. The objective of this study was to identify the factors associated with the decision for operative treatment of displaced distal radius fractures in patients aged over 55 years. METHODS: Data of 318 patients with displaced distal radius fractures were collected on patient-, fracture- and surgeon-related characteristics that were plausibly related to the decision for operation. Mean comparisons or chi-squared test were used for univariate analysis of the above-mentioned factors, and then multiple logistic regression was used to identify factors associated with the decision for operation. RESULTS: Univariate analysis showed that age, osteoporosis, Charlson score, associated orthopaedic injuries requiring surgery, Orthopaedic Trauma Association (AO) and Fernandez classification, radial height, volar tilt, volar/dorsal comminution, ulnar variance, intra-articular displacement/step-off, associated distal radioulnar joint instability or radiocarpal joint dislocation and subspecialty of treating surgeons had statistically significant association with operative intervention. In the multivariate analysis, the predictors of operative intervention were younger patient age (P = 0.028), associated orthopaedic injuries requiring surgery (P = 0.020), higher AO classification (P = 0.037), higher Fernandez classification (P = 0.041), radial shortening >5 mm (P = 0.020), volar tilt > -10° (P = 0.020), volar/dorsal comminution (P = 0.020), ulnar variance >5 mm (P = 0.023), intra-articular displacement/step-off >2 mm (P = 0.004), associated distal radioulnar joint instability or radiocarpal joint dislocation (P = 0.047) and treatment by an upper extremity specialist (P = 0.038). CONCLUSION: The decision for surgery was predominantly influenced by the characteristics and severity of the fracture. Patients' age and treatment by an upper extremity specialist were also significant factors associated with a higher likelihood of operative intervention.