6 results about "Lamina terminalis" patented technology

Described herein are various implementations of systems and methods for treating back pain (e.g., chronic low back pain) caused by different (e.g., independent) sources of pain, such as pain originating or stemming from intervertebral discs, from vertebral endplates, and / or from intraosseous locations within one or more vertebral bodies. For examples, methods for treating back pain (e.g., chronic low back pain) may involve both vertebral fusion (e.g., arthrodesis or spondylodesis to fuse adjacent vertebrae) and neuromodulation (for example, ablation of nerves within or surrounding one or more of the adjacent vertebrae). The neuromodulation may facilitate treatment of pain that is generated by insertion of fusion hardware.

Expandable spinal fusion devices, systems, and methods of using them are provided, and they can be inserted in a subject in a collapsed state through a small surgical corridor, and the expand cephalocaudal only, transverse only, or in both directions, in which direction of expansion can also be obtained independently, if desired, after the insertion. These inventions are valuable in reducing risk and surgical complexity, allowing for an on-the-fly selection of a desirable width footprint, a desired control of height expansion through a gradual cephalocaudal expansion, and a desired control of the alignment of the adjacent vertebral bodies. Devices, systems, and methods are also offered to provide a desired control of the contact area desired between the device and the upper and lower vertebral endplates achieved, for example, using an interdigitated endplate system.

Described herein are various implementations of systems and methods for determining likelihood of a patient favorably responding to a neuromodulation procedure based on a quantitative or objective score or determination based on a plurality of indicators of pain (e.g., chronic low back pain stemming from one or more vertebral bodies or vertebral endplates of a patient). The systems and methods may involve application of artificial intelligence techniques (e.g., trained algorithms, machine learning or deep learning algorithms, and / or trained neural networks).

The invention discloses a centrum collapse end plate area identification method, which comprises the following steps: S1, acquiring a vertebral section image of a single vertebral section, and performing centrum hole center segmentation along the left-right direction of a human body to obtain a centrum image; s2, recognizing the front and rear edge end face directions of the vertebral body according to the vertebral body image obtained in the S1; s3, respectively defining a front edge plane and a rear edge plane according to the front edge end face direction and the rear edge end face direction of the cone and a point on the front edge plane and the rear edge plane obtained in the S2, and respectively translating a set distance into the cone along the corresponding normal directions by taking the defined front edge plane and the defined rear edge plane as references to obtain a front edge interception plane and a rear edge interception plane; and S4, respectively obtaining areas formed by points on the front end plate and the rear end plate of the cone obtained in the step S3, wherein the distance between the points and the corresponding front edge interception plane and the rear edge interception plane is greater than a set collapse distance threshold value, and the areas are corresponding collapse areas of the front end plate and the rear end plate. According to the invention, the corresponding front and rear end plate collapse areas on the front and rear end plates of the vertebral body can be automatically identified, the operation efficiency can be effectively improved, the treatment effect is optimized, and the pain of a patient is relieved.

Described herein are various implementations of systems and methods for determining likelihood of a patient favorably responding to a neuromodulation procedure based on a quantitative or objective score or determination based on a plurality of indicators of pain (e.g., chronic low back pain stemming from one or more vertebral bodies or vertebral endplates of a patient). The systems and methods may involve application of artificial intelligence techniques (e.g., trained algorithms, machine learning or deep learning algorithms, and / or trained neural networks).

Described herein are various implementations of systems and methods for treating back pain (e.g., chronic low back pain) caused by different (e.g., independent) sources of pain, such as pain originating or stemming from intervertebral discs, from vertebral endplates, and / or from intraosseous locations within one or more vertebral bodies. For example, methods for treating back pain (e.g., chronic low back pain) may involve both vertebral fusion (e.g., arthrodesis or spondylodesis to fuse adjacent vertebrae) and neuromodulation (for example, ablation of nerves within or surrounding one or more of the adjacent vertebrae). The neuromodulation may facilitate treatment of pain that is generated by insertion of fusion hardware.