Normative Measurements of L1-S1 Segmental Angulation, Disk Space Height, and Neuroforaminal Dimensions Using Computed Tomography.

BACKGROUND AND OBJECTIVES: To establish normative anatomic measurements of lumbar segmental angulation (SA) and disk space height (DSH) in relation to neuroforaminal dimensions (NFDs), and to uncover the influence of patient demographic and anthropometric characteristics on SA, DSH, and NFDs. METHODS: NFDs, SA, and anterior, middle, and posterior DSH were measured using computed tomography of 969 patients. NFDs were defined as sagittal anterior-to-posterior width, foraminal height, and area. Statistical analyses were performed to assess associations among SA, DSH, NFDs, and patient height, weight, body mass index, sex, and ethnicity. RESULTS: SA and DSH measurements increased moving caudally from L1 to S1. Foraminal width decreased moving caudally from L1 to S1. Foraminal height and area demonstrated unimodal distribution patterns with the largest values clustered at L2-L3 on the right side and L3-L4 on the left. Significant differences in SA, DSH, and NFD measurements were observed based on the disk level. Inconsistent, marginal NFD differences were observed based on laterality. Across all disk levels, only weak-to-moderate correlations were observed between SA and DSH in relation to NFDs. Patient height, weight, and body mass index were only weakly associated with SA, DSH, and NFDs. Based on patient sex, significant differences were observed for SA, DSH, and NFD measurements from L1 to S1, with males demonstrating consistently larger values compared with females. Based on patient race and ethnicity, significant differences in SA and NFD measurements were observed from L1 to S1. CONCLUSION: This study describes 48 450 normative measurements of L1-S1 SA, DSH, and NFDs. These measurements serve as representative models of normal anatomic dimensions necessary for several applications including surgical planning and diagnosis of foraminal stenosis. Normative values of SA and DSH are not moderately or strongly associated with NFDs. SA, DSH, and NFDs are influenced by sex and ethnicity, but are not strongly or moderately influenced by patient anthropometric factors.

Surface anatomical landmarks for spine surgery: A CT-based study of the sternal notch and sternal angle in 1,035 patients.

BACKGROUND: Understanding the location of surface anatomical landmarks in relation to the cervical and thoracic spine is important for a wide array of clinical applications. The objective of this study was to investigate the influence of patient demographic and anthropometric characteristics on the locations of the sternal notch and sternal angle in relation to the spine using computed tomography (CT) of a large cohort of young adult patients without spinal pathology. METHODS: Vertebral levels corresponding to the sternal notch and sternal angle were analyzed using CT of 1,035 patients. Influences of patient height, weight, body mass index (BMI), sex, and ethnicity were assessed. RESULTS: 567 male and 468 female patients were included in this study. Mean patient height, weight, BMI, and age were 1.68 ± 0.11 m, 81.94 ± 24.39 kg, 27.79 ± 7.9 kg/m2, and 25.9 ± 5.9 years. Of the 1,035 patients, 495 were Hispanic or Latino, 321 were Caucasian, 130 were African American, 68 were Asian, 5 were identified as "other," and 16 did not have racial or ethnic data available. The location of the sternal notch in relation to the thoracic spine demonstrated a bimodal distribution pattern clustered at the T2 and T3 vertebral bodies. The location of the sternal angle in relation to the thoracic spine demonstrated a bimodal distribution pattern clustered at the T4 and T5 vertebral bodies. Moderate, negative correlations were observed between patient weight and location of the sternal notch (r = -0.447; p <.001) and sternal angle (r = -0.499; p <.001), respectively. Zero significant correlations were observed between patient height and location of the sternal notch (r = -0.045; p =.377) or sternal angle (r = -0.080; p =.229). A weak, negative correlation was observed between patient BMI and location of the sternal notch (r = -0.378; p <.001). A moderate, negative correlation was observed between patient BMI and location of the sternal angle (r = -0.445; p <.001). The locations of the sternal landmarks did not differ based on patient sex, race or ethnicity. CONCLUSIONS: The location of the sternal notch most frequently corresponds to the T2 or T3 vertebral body, while the sternal angle is most frequently located at the T4 or T5 vertebral body. Increased patient weight is associated with relatively cephalad sternal landmarks. Patient height, sex, and ethnicity are not associated with either sternal landmark.

Diagnostic and Management Applications of ChatGPT in Structured Otolaryngology Clinical Scenarios.

Objective: To evaluate the clinical applications and limitations of chat generative pretrained transformer (ChatGPT) in otolaryngology. Study Design: Cross-sectional survey. Setting: Tertiary academic center. Methods: ChatGPT 4.0 was queried for diagnoses and management plans for 20 physician-written clinical vignettes in otolaryngology. Attending physicians were then asked to rate the difficulty of the clinical vignettes and agreement with the differential diagnoses and management plans of ChatGPT responses on a 5-point Likert scale. Summary statistics were calculated. Univariate ordinal regression was then performed between vignette difficulty and quality of the diagnoses and management plans. Results: Eleven attending physicians completed the survey (61% response rate). Overall, vignettes were rated as very easy to neutral difficulty (range of median score: 1.00-4.00; overall median 2.00). There was a high agreement with the differential diagnosis provided by ChatGPT (range of median score: 3.00-5.00; overall median: 5.00). There was also high agreement with treatment plans (range of median score: 3.00-5.00; overall median: 5.00). There was no association between vignette difficulty and agreement with differential diagnosis or treatment. Lower diagnosis scores had greater odds of having lower treatment scores. Conclusion: Generative artificial intelligence models like ChatGPT are being rapidly adopted in medicine. Performance with curated, easy-to-moderate difficulty otolaryngology scenarios indicate high agreement with physicians for diagnosis and management. However, a decreased quality in diagnosis is associated with decreased quality in management. Further research is necessary on ChatGPT's ability to handle unstructured clinical information.