Automating Scoliosis Measurements in Radiographic Studies with Machine Learning: Comparing Artificial Intelligence and Clinical Reports.

Scoliosis is a condition of abnormal lateral spinal curvature affecting an estimated 2 to 3% of the US population, or seven million people. The Cobb angle is the standard measurement of spinal curvature in scoliosis but is known to have high interobserver and intraobserver variability. Thus, the objective of this study was to build and validate a system for automatic quantitative evaluation of the Cobb angle and to compare AI generated and human reports in the clinical setting. After IRB was obtained, we retrospectively collected 2150 frontal view scoliosis radiographs at a tertiary referral center (January 1, 2019, to January 1, 2021, ≥ 16 years old, no hardware). The dataset was partitioned into 1505 train (70%), 215 validation (10%), and 430 test images (20%). All thoracic and lumbar vertebral bodies were segmented with bounding boxes, generating approximately 36,550 object annotations that were used to train a Faster R-CNN Resnet-101 object detection model. A controller algorithm was written to localize vertebral centroid coordinates and derive the Cobb properties (angle and endplate) of dominant and secondary curves. AI-derived Cobb angle measurements were compared to the clinical report measurements, and the Spearman rank-order demonstrated significant correlation (0.89, p < 0.001). Mean difference between AI and clinical report angle measurements was 7.34° (95% CI: 5.90-8.78°), which is similar to published literature (up to 10°). We demonstrate the feasibility of an AI system to automate measurement of level-by-level spinal angulation with performance comparable to radiologists.

Is it painful to be different? Sciatic nerve anatomical variants on MRI and their relationship to piriformis syndrome.

OBJECTIVES: To investigate the purported relationship between sciatic nerve variant anatomy and piriformis syndrome. METHODS: Over 49 months, 1039 consecutive noncontrast adult hip MRIs were completed for various clinical indications. Repeat and technically insufficient studies were excluded. Radiologists categorized sciatic nerve anatomy into Beaton and Anson anatomical types. Chart review using our institution's cohort search and navigation tool determined the prevalence of the explicit clinical diagnosis of piriformis syndrome (primary endpoint) and sciatica and buttock pain (secondary endpoints). A Z-test compared the prevalence of each diagnosis in the variant anatomy and normal groups. RESULTS: Seven hundred eighty-three studies were included, with sciatic nerve variants present in 150 hips (19.2%). None of the diagnoses had a statistically significant difference in prevalence between the variant and normal hip groups. Specifically, piriformis syndrome was present in 11.3% of variant hips compared with 9.0% of normal hips (p = 0.39). CONCLUSIONS: There were no significant differences in the prevalence of piriformis syndrome, buttock pain, or sciatica between normal and variant sciatic nerve anatomy. This large-scale correlative radiologic study into the relationship between sciatic nerve variants and piriformis syndrome calls into question this purported relationship. KEY POINTS: • Large retrospective study relating variant sciatic nerve anatomy, present in 19.2% of hip MRIs, and piriformis syndrome • While sciatic nerve variant anatomy has previously been implicated in piriformis syndrome in small studies, no relationship was identified between sciatic nerve variants and piriformis syndrome.