Background: Wild-type transthyretin amyloidosis (ATTRwt) is an underdiagnosed and potentially fatal disease. Interestingly, ATTRwt deposits have been found to deposit in the ligamentum flavum (LF) of patients with lumbar spinal stenosis prior to the development of systemic and cardiac amyloidosis. In order to study this phenomenon and its possible relationship with LF thickening and systemic amyloidosis, a precise method of quantifying amyloid deposits in histological slides of LF is critical. However, such a method is currently unavailable. Here, we present a machine learning quantification method with Trainable Weka Segmentation (TWS) to assess amyloid deposition in histological slides of LF.
Methods: Images of ligamentum flavum specimens stained with Congo red are obtained from spinal stenosis patients undergoing laminectomies and confirmed to be positive for ATTRwt. Amyloid deposits in these specimens are classified and quantified by TWS through training the algorithm via user-directed annotations on images of LF. TWS can also be automated through exposure to a set of training images with user-directed annotations, and then application to a set of new images without additional annotations. Additional methods of color thresholding and manual segmentation are also used on these images for comparison to TWS.
Results: We develop the use of TWS in images of LF and demonstrate its potential for automated quantification. TWS is strongly correlated with manual segmentation in the training set of images with user-directed annotations (R = 0.98; p = 0.0033) as well as in the application set of images where TWS was automated (R = 0.94; p = 0.016). Color thresholding was weakly correlated with manual segmentation in the training set of images (R = 0.78; p = 0.12) and in the application set of images (R = 0.65; p = 0.23).
Conclusion: TWS machine learning closely correlates with the gold standard comparator of manual segmentation and outperforms the color thresholding method. This novel machine learning method to quantify amyloid deposition in histological slides of ligamentum flavum is a precise, objective, accessible, high throughput, and powerful tool that will hopefully pave the way towards future research and clinical applications.
Background:
Spine surgeons rarely consider metal allergies when placing hardware, as implants are thought to be inert.
Case Description:
A 32-year-old male presented with a skin rash attributed to the trace metal in his spinal fusion instrumentation. Patch testing revealed sensitivities to cobalt, manganese, and chromium. He underwent hardware removal and replacement with constructs of commercially pure titanium. His skin findings resolved at 2 weeks after surgery and were stable at 6 weeks.
Conclusion:
Hypersensitivity to metal (i.e., metal allergy) should be considered before performing instrumented spinal fusions.
Dr. William Beecher Scoville (1906–1984) is a giant figure in the history of neurosurgery, well known by the public for his operation on Patient H.M. He developed dozens of neurosurgical instruments and techniques, with many tools named after him that are still widely used today. He founded numerous neurosurgical societies around the world. He led the movement in psychosurgery, developing the technique of selective orbital undercutting and performing hundreds of lobotomies throughout his career. However, his many contributions to the advancement of neurosurgery have not been well described in the medical literature. To bridge the knowledge gap, this article seeks to detail the life and career of William Beecher Scoville and bring to attention the enduring impact of his work.
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