Computed Tomography-Based Navigation System in Current Spine Surgery: A Narrative Review

The number of spine surgeries using instrumentation has been increasing with recent advances in surgical techniques and spinal implants. Navigation systems have been attracting attention since the 1990s in order to perform spine surgeries safely and effectively, and they enable us to perform complex spine surgeries that have been difficult to perform in the past. Navigation systems are also contributing to the improvement of minimally invasive spine stabilization (MISt) surgery, which is becoming popular due to aging populations. Conventional navigation systems were based on reconstructions obtained by preoperative computed tomography (CT) images and did not always accurately reproduce the intraoperative patient positioning, which could lead to problems involving inaccurate positional information and time loss associated with registration. Since 2006, an intraoperative CT-based navigation system has been introduced as a solution to these problems, and it is now becoming the mainstay of navigated spine surgery. Here, we highlighted the use of intraoperative CT-based navigation systems in current spine surgery, as well as future issues and prospects

Computed Tomography-Based Navigation System in Current Spine Surgery: A Narrative Review

The number of spine surgeries using instrumentation has been increasing with recent advances in surgical techniques and spinal implants. Navigation systems have been attracting attention since the 1990s in order to perform spine surgeries safely and effectively, and they enable us to perform complex spine surgeries that have been difficult to perform in the past. Navigation systems are also contributing to the improvement of minimally invasive spine stabilization (MISt) surgery, which is becoming popular due to aging populations. Conventional navigation systems were based on reconstructions obtained by preoperative computed tomography (CT) images and did not always accurately reproduce the intraoperative patient positioning, which could lead to problems involving inaccurate positional information and time loss associated with registration. Since 2006, an intraoperative CT-based navigation system has been introduced as a solution to these problems, and it is now becoming the mainstay of navigated spine surgery. Here, we highlighted the use of intraoperative CT-based navigation systems in current spine surgery, as well as future issues and prospects.</jats:p

Comparative Study of Anticipatory Postural Adjustments between Normal and Cervical Myelopathy Patients

Patients with cervical spondylotic myelopathy or ossification of the posterior longitudinal ligament have been considered to be prone to falls due to lower extremity dysfunction and gait instability. Anticipatory postural adjustments (APAs) are unconscious muscular activities to counterbalance perturbation. To date, there are no reports on APAs in cervical myelopathy patients, and quantification of postural control remains difficult. Thirty participants were enrolled, of which 15 were cervical myelopathy patients and 15 were normal age- and sex-matched controls. A three-dimensional motion capture system with force plates was used, and the APA phase was defined as the time between start of movement at the center of pressure and heel-off of the step leg. The APA phase (0.47 vs. 0.39 s, p &lt; 0.05) and turning time (2.27 vs. 1.83 s, p &lt; 0.01) were significantly longer, whereas step length tended to be shorter (305.18 vs. 361.04 mm, p = 0.06) in cervical myelopathy patients. There was a significant correlation between Japanese Orthopaedic Association lower extremity motor dysfunction scores and step length (p &lt; 0.01). Cervical myelopathy patients are prone to falls due to longer APA phases with shorter step lengths. Analysis of the APA phase aids the visualization and quantification of postural control during initial gait in cervical myelopathy patients

Revolutionizing Spinal Care: Current Applications and Future Directions of Artificial Intelligence and Machine Learning

Artificial intelligence (AI) and machine learning (ML) are rapidly becoming integral components of modern healthcare, offering new avenues for diagnosis, treatment, and outcome prediction. This review explores their current applications and potential future in the field of spinal care. From enhancing imaging techniques to predicting patient outcomes, AI and ML are revolutionizing the way we approach spinal diseases. AI and ML have significantly improved spinal imaging by augmenting detection and classification capabilities, thereby boosting diagnostic accuracy. Predictive models have also been developed to guide treatment plans and foresee patient outcomes, driving a shift towards more personalized care. Looking towards the future, we envision AI and ML further ingraining themselves in spinal care with the development of algorithms capable of deciphering complex spinal pathologies to aid decision making. Despite the promise these technologies hold, their integration into clinical practice is not without challenges. Data quality, integration hurdles, data security, and ethical considerations are some of the key areas that need to be addressed for their successful and responsible implementation. In conclusion, AI and ML represent potent tools for transforming spinal care. Thoughtful and balanced integration of these technologies, guided by ethical considerations, can lead to significant advancements, ushering in an era of more personalized, effective, and efficient healthcare