Artificial intelligence and machine learning in spine research
F Galbusera, G Casaroli, T Bassani - JOR spine, 2019 - Wiley Online Library
Artificial intelligence (AI) and machine learning (ML) techniques are revolutionizing several
industrial and research fields like computer vision, autonomous driving, natural language …
industrial and research fields like computer vision, autonomous driving, natural language …
Using deep transfer learning to detect scoliosis and spondylolisthesis from X-ray images
Recent years have witnessed wider prevalence of vertebral column pathologies due to
lifestyle changes, sedentary behaviors, or injuries. Spondylolisthesis and scoliosis are two of …
lifestyle changes, sedentary behaviors, or injuries. Spondylolisthesis and scoliosis are two of …
Detection of lumbar spondylolisthesis from X-ray images using deep learning network
Spondylolisthesis refers to the displacement of a vertebral body relative to the vertrabra
below it, which can cause radicular symptoms, back pain or leg pain. It usually occurs in the …
below it, which can cause radicular symptoms, back pain or leg pain. It usually occurs in the …
The classification of medical and botanical data through majority voting using artificial neural network
Data classification has many approaches in data mining and machine learning. The artificial
neural network (ANN) is applied to classify the data that might belong to various domains …
neural network (ANN) is applied to classify the data that might belong to various domains …
Developing nonlinear k-nearest neighbors classification algorithms to identify patients at high risk of increased length of hospital stay following spine surgery
OBJECTIVE Spondylolisthesis is a common operative disease in the United States, but
robust predictive models for patient outcomes remain limited. The development of models …
robust predictive models for patient outcomes remain limited. The development of models …
A decision support system to improve medical diagnosis using a combination of k-medoids clustering based attribute weighting and SVM
M Peker - Journal of medical systems, 2016 - Springer
The use of machine learning tools has become widespread in medical diagnosis. The main
reason for this is the effective results obtained from classification and diagnosis systems …
reason for this is the effective results obtained from classification and diagnosis systems …
Diagnosis of vertebral column pathologies using concatenated resampling with machine learning algorithms
Medical diagnosis through the classification of biomedical attributes is one of the
exponentially growing fields in bioinformatics. Although a large number of approaches have …
exponentially growing fields in bioinformatics. Although a large number of approaches have …
Feature-reduction fuzzy co-clustering approach for hyper-spectral image analysis
Fuzzy co-clustering algorithms are the effective techniques for multi-dimensional clustering
in which all features are considered of equal importance (relevance). In fact, the features' …
in which all features are considered of equal importance (relevance). In fact, the features' …
Feature weighting and selection with a Pareto-optimal trade-off between relevancy and redundancy
Feature Selection (FS) is an important pre-processing step in machine learning and it
reduces the number of features/variables used to describe each member of a dataset. Such …
reduces the number of features/variables used to describe each member of a dataset. Such …
Validity of humerus fracture classification in the Swedish fracture register
D Wennergren, S Stjernström, M Möller… - BMC musculoskeletal …, 2017 - Springer
Background The ability to correctly classify fractures is of importance for choosing the
appropriate treatment and for providing appropriate data for research and quality registers …
appropriate treatment and for providing appropriate data for research and quality registers …