Quantitative Analysis of Spinal Canal Areas in the Lumbar Spine: An Imaging Informatics and Machine Learning Study.

BACKGROUND AND PURPOSE: Quantitative imaging biomarkers have not been established for the diagnosis of spinal canal stenosis. This work aimed to lay the groundwork to establish such biomarkers by leveraging the developments in machine learning and medical imaging informatics. MATERIALS AND METHODS: Machine learning algorithms were trained to segment lumbar spinal canal areas on axial views and intervertebral discs on sagittal views of lumbar MRIs. These were used to measure spinal canal areas at each lumbar level (L1 through L5). Machine-generated delineations were compared with 2 sets of human-generated delineations to validate the proposed techniques. Then, we use these machine learning methods to delineate and measure lumbar spinal canal areas in a normative cohort and to analyze their variation with respect to age, sex, and height using a variable-intercept mixed model. RESULTS: We established that machine-generated delineations are comparable with human-generated segmentations. Spinal canal areas as measured by machine are statistically significantly correlated with height (P < .05) but not with age or sex. CONCLUSIONS: Our machine learning methodology demonstrates that this important anatomic structure can be accurately detected and quantitatively measured without human input in a manner comparable with that of human raters. Anatomic deviations measured against the normative model established here could be used to flag spinal stenosis in the future.

The prevalence of wrist ganglia in an asymptomatic population: magnetic resonance evaluation.

Magnetic resonance imaging (MRI) was performed on the wrists of 103 asymptomatic volunteers. The images were evaluated independently by two musculoskeletal radiologists and one orthopaedic surgeon. Wrist ganglia were identified in 53 out of the 103 wrists. The average long and short axes measurements were 8 mm (range 3-22) and 3 mm (range 2-10), respectively. Seventy per cent of the ganglia originated from the palmar capsule in the region of the interval between the radioscaphocapitate ligament and the long radiolunate ligament. Fourteen per cent of the ganglia were dorsal and originated from the dorsal, distal fibres of the scapholunate ligament. Two ganglia had surrounding soft tissue oedema and one had an associated intraosseous component. Unlike previous surgical and pathological series, our study showed that palmar wrist ganglia are more common than dorsal wrist ganglia. The vast majority of these asymptomatic ganglia occur without associated ligamentous disruption, soft tissue oedema or intraosseous communication.