Factors Affecting Operation Duration in Posterior Spinal Fusion (PSF) Using Dual Attending Surgeon Strategy Among Lenke 1 and 2 Adolescent Idiopathic Scoliosis (AIS) Patients

A Novel Deformity Correction Manipulation System for Better Correction of Large Thoracic Scoliosis

OBJECTIVE

Treating patients with large thoracic scoliosis (between 70° and 100°) poses technical challenges, particularly with traditional correction techniques (TCT). To address this, we developed a novel deformity correction manipulation system (DCMS) aimed at reducing surgical complexity and trauma. This study aims to assess the safety and effectiveness of DCMS in treating large thoracic scoliosis.

METHODS

From January 2016 to June 2021, 76 patients with large thoracic scoliosis were included in this retrospective study. The patients were divided into two groups: DCMS (n = 34) and TCT (n = 42). Basic patient data including age at surgery, sex, etiology, Risser sign, flexibility of the main thoracic curve, instrumented levels, number of screws, duration of hospital stay, and follow-up time were collected and analyzed. Radiographic and clinical outcomes, as measured by various radiographic parameters and Scoliosis Research Society-30 (SRS-30) scores, were retrospectively analyzed and compared between the two groups. Adverse events were also documented. Statistical analyses were performed using two-tailed independent t-tests, chi-square tests, and Fisher's exact test.

RESULTS

The DCMS group exhibited significantly shorter operative times, reduced blood loss, and shorter hospital stays compared to the TCT group. However, there were no significant differences between the two groups in terms of age at surgery, sex, etiology, Risser sign, flexibility of the main curve, instrumented levels, number of screws, and follow-up time. While preoperative major curves were statistically similar between the two groups, the DCMS group achieved a superior correction rate compared to the TCT group (74.2% ± 8.8% vs 68.1% ± 10.5%). No significant differences were observed in other radiographic parameters, SRS-30 scores, or the incidence of adverse events.

CONCLUSION

The application of DCMS resulted in shorter operative times, reduced blood loss, shorter hospital stays, and greater curve correction compared to TCT. DCMS proves to be a safe and effective technique for treating large thoracic curves.

Elective surgery scheduling under uncertainty in demand for intensive care unit and inpatient beds during epidemic outbreaks

Amid the epidemic outbreaks such as COVID-19, a large number of patients occupy inpatient and intensive care unit (ICU) beds, thereby making the availability of beds uncertain and scarce. Thus, elective surgery scheduling not only needs to deal with the uncertainty of the surgery duration and length of stay in the ward, but also the uncertainty in demand for ICU and inpatient beds. We model this surgery scheduling problem with uncertainty and propose an effective algorithm that minimizes the operating room overtime cost, bed shortage cost, and patient waiting cost. Our model is developed using fuzzy sets whereas the proposed algorithm is based on the differential evolution algorithm and heuristic rules. We set up experiments based on data and expert experience respectively. A comparison between the fuzzy model and the crisp (non-fuzzy) model proves the usefulness of the fuzzy model when the data is not sufficient or available. We further compare the proposed model and algorithm with several extant models and algorithms, and demonstrate the computational efficacy, robustness, and adaptability of the proposed framework.

Computerized-Assisted Scoliosis Diagnosis Based on Faster R-CNN and ResNet for the Classification of Spine X-Ray Images

In order to reduce the subjectivity of preoperative diagnosis and achieve accurate and rapid classification of idiopathic scoliosis and thereby improving the standardization and automation of spinal surgery diagnosis, we implement the Faster R-CNN and ResNet to classify patient spine images. In this paper, the images are based on spine X-ray imaging obtained by our radiology department. We compared the results with the orthopedic surgeon's measurement results for verification and analysis and finally presented the grading results for performance evaluation. The final experimental results can meet the clinical needs, and a fast and robust deep learning-based scoliosis diagnosis algorithm for scoliosis can be achieved without manual intervention using the X-ray scans. This can give rise to a computerized-assisted scoliosis diagnosis based on X-ray imaging, which has strong potential in clinical utility applied to the field of orthopedics.