Identification of Anterior Cervical Spinal Instrumentation Using a Smartphone Application Powered by Machine Learning.

STUDY DESIGN: Cross-sectional study. OBJECTIVE: The purpose of this study is to develop and validate a machine learning algorithm for the automated identification of anterior cervical discectomy and fusion (ACDF) plates from smartphone images of anterior-posterior (AP) cervical spine radiographs. SUMMARY OF BACKGROUND DATA: Identification of existing instrumentation is a critical step in planning revision surgery for ACDF. Machine learning algorithms that are known to be adept at image classification may be applied to the problem of ACDF plate identification. METHODS: A total of 402 smartphone images containing 15 different types of ACDF plates were gathered. Two hundred seventy-five images (∼70%) were used to train and validate a convolution neural network (CNN) for classification of images from radiographs. One hundred twenty-seven (∼30%) images were held out to test algorithm performance. RESULTS: The algorithm performed with an overall accuracy of 94.4% and 85.8% for top-3 and top-1 accuracy, respectively. Overall positive predictive value, sensitivity, and f1-scores were 0.873, 0.858, and 0.855, respectively. CONCLUSION: This algorithm demonstrates strong performance in the classification of ACDF plates from smartphone images and will be deployed as an accessible smartphone application for further evaluation, improvement, and eventual widespread use.Level of Evidence: 3.

Emerging Technologies in the Treatment of Adult Spinal Deformity.

Outcomes for adult spinal deformity continue to improve as new technologies become integrated into clinical practice. Machine learning, robot-guided spinal surgery, and patientspecific rods are tools that are being used to improve preoperative planning and patient satisfaction. Machine learning can be used to predict complications, readmissions, and generate postoperative radiographs which can be shown to patients to guide discussions about surgery. Robot-guided spinal surgery is a rapidly growing field showing signs of greater accuracy in screw placement during surgery. Patient-specific rods offer improved outcomes through higher correction rates and decreased rates of rod breakage while decreasing operative time. The objective of this review is to evaluate trends in the literature about machine learning, robot-guided spinal surgery, and patient-specific rods in the treatment of adult spinal deformity.

Risk Factors for 30- and 90-Day Readmissions Due To Surgical Site Infection Following Posterior Lumbar Fusion.

STUDY DESIGN: This was a retrospective cohort study. OBJECTIVE: Identify the independent risk factors for 30- and 90-day readmission because of surgical site infection (SSI) in patients undergoing elective posterior lumbar fusion (PLF). SUMMARY OF BACKGROUND DATA: SSI is a significant cause of morbidity in the 30- and 90-day windows after hospital discharge. There remains a gap in the literature on independent risk factors for readmission because of SSI after PLF procedures. In addition, readmission for SSI after spine surgery beyond the 30-day postoperative period has not been well studied. METHODS: A retrospective analysis was performed on data from the 2012 to 2014 Healthcare Cost and Utilization Project Nationwide Readmissions Database. The authors identified 65,121 patients who underwent PLF. There were 191 patients (0.30%) readmitted with a diagnosis of SSI in the 30-day readmission window, and 283 (0.43%) patients readmitted with a diagnosis of SSI in the 90-day window. Baseline patient demographics and medical comorbidities were assessed. Bivariate and multivariate analyses were performed to examine the independent risk factors for readmission because of SSI. RESULTS: In the 30-day window after discharge, this study identified patients with liver disease, uncomplicated diabetes, deficiency anemia, depression, psychosis, renal failure, obesity, and Medicaid or Medicare insurance as higher risk patients for unplanned readmission with a diagnosis of SSI. The study identified the same risk factors in the 90-day window with the addition of diabetes with chronic complications, chronic pulmonary disease, and pulmonary circulation disease. CONCLUSIONS: Independent risk factors for readmission because of SSI included liver disease, uncomplicated diabetes, obesity, and Medicaid insurance status. These findings suggest that additional intervention in the perioperative workup for patients with these risk factors may be necessary to lower unplanned readmission because of SSI after PLF surgery.

Deep Learning Automates Measurement of Spinopelvic Parameters on Lateral Lumbar Radiographs.

STUDY DESIGN: Cross-sectional database study. OBJECTIVE: The objective of this study was to develop an algorithm for the automated measurement of spinopelvic parameters on lateral lumbar radiographs with comparable accuracy to surgeons. SUMMARY OF BACKGROUND DATA: Sagittal alignment measurements are important for the evaluation of spinal disorders. Manual measurement methods are time-consuming and subject to rater-dependent error. Thus, a need exists to develop automated methods for obtaining sagittal measurements. Previous studies of automated measurement have been limited in accuracy, inapplicable to common plain films, or unable to measure pelvic parameters. METHODS: Images from 816 patients receiving lateral lumbar radiographs were collected sequentially and used to develop a convolutional neural network (CNN) segmentation algorithm. A total of 653 (80%) of these radiographs were used to train and validate the CNN. This CNN was combined with a computer vision algorithm to create a pipeline for the fully automated measurement of spinopelvic parameters from lateral lumbar radiographs. The remaining 163 (20%) of radiographs were used to test this pipeline. Forty radiographs were selected from the test set and manually measured by three surgeons for comparison. RESULTS: The CNN achieved an area under the receiver-operating curve of 0.956. Algorithm measurements of L1-S1 cobb angle, pelvic incidence, pelvic tilt, and sacral slope were not significantly different from surgeon measurement. In comparison to criterion standard measurement, the algorithm performed with a similar mean absolute difference to spine surgeons for L1-S1 Cobb angle (4.30°â€Š±â€Š4.14° vs. 4.99°â€Š±â€Š5.34°), pelvic tilt (2.14°â€Š±â€Š6.29° vs. 1.58°â€Š±â€Š5.97°), pelvic incidence (4.56°â€Š±â€Š5.40° vs. 3.74°â€Š±â€Š2.89°), and sacral slope (4.76°â€Š±â€Š6.93° vs. 4.75°â€Š±â€Š5.71°). CONCLUSION: This algorithm measures spinopelvic parameters on lateral lumbar radiographs with comparable accuracy to surgeons. The algorithm could be used to streamline clinical workflow or perform large scale studies of spinopelvic parameters.Level of Evidence: 3.

Automated Measurement of Lumbar Lordosis on Radiographs Using Machine Learning and Computer Vision.

STUDY DESIGN: Cross sectional database study. OBJECTIVE: To develop a fully automated artificial intelligence and computer vision pipeline for assisted evaluation of lumbar lordosis. METHODS: Lateral lumbar radiographs were used to develop a segmentation neural network (n = 629). After synthetic augmentation, 70% of these radiographs were used for network training, while the remaining 30% were used for hyperparameter optimization. A computer vision algorithm was deployed on the segmented radiographs to calculate lumbar lordosis angles. A test set of radiographs was used to evaluate the validity of the entire pipeline (n = 151). RESULTS: The U-Net segmentation achieved a test dataset dice score of 0.821, an area under the receiver operating curve of 0.914, and an accuracy of 0.862. The computer vision algorithm identified the L1 and S1 vertebrae on 84.1% of the test set with an average speed of 0.14 seconds/radiograph. From the 151 test set radiographs, 50 were randomly chosen for surgeon measurement. When compared with those measurements, our algorithm achieved a mean absolute error of 8.055° and a median absolute error of 6.965° (not statistically significant, P > .05). CONCLUSION: This study is the first to use artificial intelligence and computer vision in a combined pipeline to rapidly measure a sagittal spinopelvic parameter without prior manual surgeon input. The pipeline measures angles with no statistically significant differences from manual measurements by surgeons. This pipeline offers clinical utility in an assistive capacity, and future work should focus on improving segmentation network performance.

The most influential papers in direct anterior approach to total hip arthroplasty.

BACKGROUND: Citation analysis is a commonly used method for appraising the impact of academic publications within a particular field of study. A gap exists in the citation analysis literature with regard to the topic of direct anterior approach (DAA) hip arthroplasty. The purpose of this study is to identify the 50 most frequently cited publications related to this topic. METHODS: The Clarivate Analytics Web of Knowledge database was utilized to search for publications relating to DAA hip arthroplasty. The top 50 most cited articles that met inclusion criteria were recorded and reviewed for various metrics. RESULTS: The top 50 publications were cited a total of 3521 times, with an average of 86.3 total citations per year between 1980 and 2019. 47 of the 50 articles identified had been published since the year 2000. Cohort designs were the most common study type. CONCLUSIONS: This analysis provides insight into factors that characterize highly cited articles on the specific topic of DAA hip arthroplasty. These factors include higher levels of evidence, recent publication, and origin in the United States. Citations of DAA hip arthroplasty papers appear to be on the rise. The curation and analysis of this set of 50 articles will provide orthopaedic surgery clinicians, researchers, and residency program directors a guide for quickly isolating influential articles on the topic of DAA hip arthroplasty. This may serve as a quick reference for clinical decision-making, foundation for further research, and curriculum on DAA hip arthroplasty.

Postoperative Complications Associated With Metabolic Syndrome Following Adult Spinal Deformity Surgery.

STUDY DESIGN: A retrospective cohort study. OBJECTIVE: The objective of this study was to examine the effect of metabolic syndrome on 30-day postoperative complications following corrective surgery for the adult spinal deformity (ASD). SUMMARY OF BACKGROUND DATA: Metabolic syndrome has been shown to increase the risk of cardiovascular morbidity and mortality. Few studies have examined the effect of metabolic syndrome on patients with ASD undergoing surgery. MATERIALS AND METHODS: We performed a retrospective cohort study of patients who underwent spinal fusion for ASD. Patients were divided into 2 groups based on the presence or absence of metabolic syndrome, which was defined as a combination of hypertension, diabetes mellitus, and obesity. Baseline patient characteristics and operative variables were compared between the 2 groups. We also compared the incidence of 30-day postoperative complications between the 2 groups. A multivariable regression analysis was then performed to identify 30-day postoperative complications that were independently associated with metabolic syndrome. RESULTS: A total of 6696 patients were included with 8.3% (n=553) having metabolic syndrome. Patients with metabolic syndrome were more likely to have renal comorbidity (P=0.042), bleeding disorder (P=0.011), American Society of Anesthesiology classification ≥3 (P<0.001), and undergo a long fusion (P=0.009). Patients with metabolic syndrome had higher rates of 30-day mortality (P=0.042), superficial surgical site infection (P=0.006), sepsis (P=0.003), cardiac complications (P<0.001), pulmonary complications (P=0.003), pulmonary embolism (P=0.050), prolonged hospitalization (P=0.010), nonhome discharge (P=0.007), and reoperation (P=0.003). Metabolic syndrome was an independent risk factor for cardiac complications [odds ratio (OR)=4.2; 95% confidence interval (CI): 1.7-10.2; P=0.001], superficial surgical site infection (OR=2.8; 95% CI: 1.4-5.7; P=0.004), sepsis (OR=2.2, 95% CI: 1.2-3.9; P=0.009), reoperation (OR=1.7; 95% CI: 1.2-2.5; P=0.006), pulmonary complications (OR=1.7; 95% CI: 1.1-2.5; P=0.017), and prolonged hospitalization (OR=1.4; 95% CI: 1.0-1.9; P=0.039). CONCLUSIONS: Recognition and awareness of the relationship between metabolic syndrome and postoperative complications following ASD surgery is important for preoperative optimization and perioperative care.

Applications of Machine Learning Using Electronic Medical Records in Spine Surgery.

Developments in machine learning in recent years have precipitated a surge in research on the applications of artificial intelligence within medicine. Machine learning algorithms are beginning to impact medicine broadly, and the field of spine surgery is no exception. Electronic medical records are a key source of medical data that can be leveraged for the creation of clinically valuable machine learning algorithms. This review examines the current state of machine learning using electronic medical records as it applies to spine surgery. Studies across the electronic medical record data domains of imaging, text, and structured data are reviewed. Discussed applications include clinical prognostication, preoperative planning, diagnostics, and dynamic clinical assistance, among others. The limitations and future challenges for machine learning research using electronic medical records are also discussed.