AIMC Topic: Spinal Fusion

Clear Filters Showing 1 to 10 of 164 articles

Identifying patients at risk of increased health utilization following lumbar spine surgery.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
BACKGROUND: Adequate preoperative identification of patients at risk of significant healthcare utilization after surgery could help guide preoperative decision-making as well as postoperative patient management. While several studies have proposed me...

A modular cage may prevent endplate damage and improve spinal deformity correction.

Clinical biomechanics (Bristol, Avon)
BACKGROUND: Anterior lumbar interbody fusion is performed to fuse pathological spinal segments, generally, with a monobloc cage inserted by impact forces. Recently developed three-part modular cages attempt to reduce the impact forces, minimize the d...

Evaluating AI-generated patient education materials for spinal surgeries: Comparative analysis of readability and DISCERN quality across ChatGPT and deepseek models.

International journal of medical informatics
BACKGROUND: Access to patient-centered health information is essential for informed decision-making. However, online medical resources vary in quality and often fail to accommodate differing degrees of health literacy. This issue is particularly evid...

The Role of Claude 3.5 Sonet and ChatGPT-4 in Posterior Cervical Fusion Patient Guidance.

World neurosurgery
BACKGROUND: This study evaluates the role of ChatGPT-4 and Claude 3.5 Sonet in postoperative management for patients undergoing posterior cervical fusion. It focuses on their ability to provide accurate, clear, and relevant responses to patient conce...

Machine-learning models for the prediction of ideal surgical outcomes in patients with adult spinal deformity.

The bone & joint journal
AIMS: Adult spinal deformity (ASD) surgery can reduce pain and disability. However, the actual surgical efficacy of ASD in doing so is far from desirable, with frequent complications and limited improvement in quality of life. The accurate prediction...

Preoperative anemia is an unsuspecting driver of machine learning prediction of adverse outcomes after lumbar spinal fusion.

The spine journal : official journal of the North American Spine Society
BACKGROUND CONTEXT: Preoperative risk assessment remains a challenge in spinal fusion operations. Predictive modeling provides data-driven estimates of postsurgical outcomes, guiding clinical decisions and improving patient care. Moreover, automated ...

Deep learning based decision-making and outcome prediction for adolescent idiopathic scoliosis patients with posterior surgery.

Scientific reports
With the emergence of numerous classifications, surgical treatment for adolescent idiopathic scoliosis (AIS) can be guided more effectively. However, surgical decision-making and optimal strategies still lack standardization and personalized customiz...

Machine learning models for predicting dysphonia following anterior cervical discectomy and fusion: a Swedish Registry Study.

The spine journal : official journal of the North American Spine Society
BACKGROUND: Dysphonia is one of the more common complications following anterior cervical discectomy and fusion (ACDF). ACDF is the gold standard for treating degenerative cervical spine disorders, and identifying high-risk patients is therefore cruc...

Development of machine learning model for predicting prolonged operation time in lumbar stenosis undergoing posterior lumbar interbody fusion: a multicenter study.

The spine journal : official journal of the North American Spine Society
BACKGROUND CONTEXT: Longer posterior lumbar interbody fusion (PLIF) surgeries for individuals with lumbar spinal stenosis are linked to more complications and negatively affect recovery after the operation. Therefore, there is a critical need for a m...