Imaging features of the postoperative spine: a guide to basic understanding of spine surgical procedures

Artificial intelligence-based orthopaedic perpetual design

Background and aims

Integrating Artificial Intelligence (AI) methodologies in orthopaedic surgeries is becoming increasingly important as it optimises implant designs and treatment procedures. This research article introduces an innovative approach using an AI-driven algorithm, focusing on the humerus bone anatomy. The primary focus of this work is to determine implant dimensions tailored to individual patients.

Methodology

We have utilised Python's DICOM library, which extracts rich information from medical images obtained through CT and MRI scans. The algorithm generates precise three-dimensional reconstructions of the bone, enabling a comprehensive understanding of its morphology.

Results

Using algorithms that reconstructed 3D bone models to propose optimal implant geometries that adhere to patients' unique anatomical intricacies and cater to their functional requirements. Integrating AI techniques promotes enhanced implant designs that facilitate enhanced integration with the host bone, promoting improved patient outcomes.

Conclusion

A notable breakthrough in this research is the ability of the algorithm to predict implant physical dimensions based on CT and MRI data. The algorithm can infer implant specifications that align with patient-specific bone characteristics by training the AI model on a diverse dataset. This approach could revolutionise orthopaedic surgery, reducing patient waiting times and the duration of medical interventions.

[Imaging Evaluation of Early and Long-Term Complications Associated with the Postoperative Spine]

As the number of spinal surgeries being performed expands, the number of medical imaging procedures such as radiography, CT, and MRI is also increasing, and the importance of their interpretation is becoming more significant. Herein, we present the radiological findings of a variety of complications that can occur after spinal surgery and discuss how effectively and accurately they can be diagnosed through imaging. In particular, this study details the characteristic imaging findings specific to the early and long-term postoperative periods. Early complications of spinal surgery include improper placement of surgical instruments (instrument malpositioning), seromas, hematomas, pseudomeningoceles, and infections in the region surrounding the surgical site. Conversely, long-term complications may include osteolysis around surgical instruments, failure of fusion, adjacent segment disease, and the formation of epidural fibrosis or scar tissue. A precise understanding of the imaging assessments related to complications arising after spinal surgery is crucial to ensure timely and accurate diagnosis, which is necessary to achieve effective treatment.

The Role of Magnetic Resonance Imaging and Computed Tomography in Spinal Cord Injury

Traumatic injuries of the spine are associated with long-term morbidity and mortality. Timely diagnosis and appropriate management of mechanical instability and spinal cord injury are important to prevent further neurologic deterioration. Spine surgeons require an understanding of the essential imaging techniques concerning the diagnosis, management, and prognosis of spinal cord injury. We present a review in the role of computed tomography (CT) including advancements in multidetector CT (MDCT), dual energy CT (DECT), and photon counting CT, and how it relates to spinal trauma. We also review magnetic resonance imaging (MRI) and some of the developed MRI based classifications for prognosticating the severity and outcome of spinal cord injury, such as diffusion weighted imaging (DWI), diffusion tractography (DTI), functional MRI (fMRI), and perfusion MRI.