AIMC Topic: Spine

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Validation of a novel artificial intelligence model (SpinePose) to automatically and accurately predict spinopelvic parameters using scoliosis radiographs in an external cohort.

Neurosurgical focus
OBJECTIVE: SpinePose was developed in 2024 as a novel artificial intelligence (AI) tool to automatically predict spinopelvic parameters with high accuracy and without the need for manual entry. The authors' published results demonstrated excellent pe...

ICPPNet: A semantic segmentation network model based on inter-class positional prior for scoliosis reconstruction in ultrasound images.

Journal of biomedical informatics
OBJECTIVE: Considering the radiation hazard of X-ray, safer, more convenient and cost-effective ultrasound methods are gradually becoming new diagnostic approaches for scoliosis. For ultrasound images of spine regions, it is challenging to accurately...

Deep learning-based identification of vertebral fracture and osteoporosis in lateral spine radiographs and DXA vertebral fracture assessment to predict incident fracture.

Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research
Deep learning (DL) identification of vertebral fractures and osteoporosis in lateral spine radiographs and DXA vertebral fracture assessment (VFA) images may improve fracture risk assessment in older adults. In 26 299 lateral spine radiographs from 9...

Dynamic bone recognition for robotic vertebral plate cutting via unit energy consumption and SVM optimized by PSO.

Scientific reports
Current orthopedic robots lack the ability to dynamically sense or accurately recognize bone layers during vertebral plate decompression surgery, limiting their ability to adjust actions in real time as skilled surgeons do. This study aims to improve...

Artificial Intelligence in Spine Imaging: A Paradigm Shift in Diagnosis and Care.

Magnetic resonance imaging clinics of North America
Recent advancements in artificial intelligence (AI) can significantly improve radiologists' workflow, improving efficiency and diagnostic accuracy. Current AI applications within spine imaging are approved to accelerate image acquisition time, improv...

Effect of Laminectomy Methods on the Surgical Safety of Automatic Laminectomy Robot.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: The efficacy of laminectomy procedures is contingent on the method of resection. The objective of this study was to investigate the impact of different methods of resection on the surgical safety of automated laminectomy robots, an area t...

A prospectively deployed deep learning-enabled automated quality assurance tool for oncological palliative spine radiation therapy.

The Lancet. Digital health
BACKGROUND: Palliative spine radiation therapy is prone to treatment at the wrong anatomic level. We developed a fully automated deep learning-based spine-targeting quality assurance system (DL-SpiQA) for detecting treatment at the wrong anatomic lev...

Vertebrae Segmentation with Generative Adversarial Networks for Automatic Cobb Angle Measurement.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Scoliosis is a lateral deformity of the spine, usually diagnosed in patients with a Cobb angle (CA) greater than 10°. Accurate measurements of the CA are necessary for timely intervention and subsequent effective treatment of scoliosis. However, the ...

EUFormer: Learning Driven 3D Spine Deformity Assessment with Orthogonal Optical Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
In clinical settings, the screening, diagnosis, and monitoring of adolescent idiopathic scoliosis (AIS) typically involve physical or radiographic examinations. However, physical examinations are subjective, while radiographic examinations expose pat...

A Three-Stage Semi-Supervised Learning Approach to Spine Image Segmentation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Spine segmentation in computed tomography (CT) images is critical for automatic analysis, especially when focusing on varied spinal anatomy. Despite having comprehensive annotations for normal vertebrae, many datasets do not encompass labeled fractur...