4.6 Article

Predicting the anterior slippage of vertebral lumbar spine using Densenet-201

Related references

Note: Only part of the references are listed.
Article Biology

PSTCNN: Explainable COVID-19 diagnosis using PSO-guided self-tuning CNN

Wei Wang et al.

Summary: Since 2019, the COVID-19 pandemic has posed a significant threat to the global economy and human health. Deep learning-based computer-aided diagnosis models can effectively alleviate the challenges of diagnosing COVID-19 due to limited healthcare resources. To overcome the time-consuming and unstable nature of traditional hyperparameter tuning methods, we propose a Particle Swarm Optimization-guided Self-Tuning Convolution Neural Network (PSTCNN) that automatically adjusts the model's hyperparameters.

BIOCELL (2023)

Article Radiology, Nuclear Medicine & Medical Imaging

Intelligent Evaluation of Global Spinal Alignment by a Decentralized Convolutional Neural Network

Thong Phi Nguyen et al.

Summary: Degenerative changes of the spine can cause spinal misalignment, leading to back pain and reduced mobility. This paper presents a method using a decentralized convolutional neural network to automatically measure spinal alignment parameters, which has been validated to be accurate through testing.

JOURNAL OF DIGITAL IMAGING (2022)

Article Clinical Neurology

Diagnostic triage in patients with central lumbar spinal stenosis using a deep learning system of radiographs

Tackeun Kim et al.

Summary: In this study, a deep learning model using a convolutional neural network (CNN) was developed to diagnose severe central LSS using radiography, and it showed high diagnostic accuracy. The model accurately localized stenotic lesions and assisted physicians in diagnosing LSS.

JOURNAL OF NEUROSURGERY-SPINE (2022)

Article Chemistry, Analytical

Localization and Edge-Based Segmentation of Lumbar Spine Vertebrae to Identify the Deformities Using Deep Learning Models

Malaika Mushtaq et al.

Summary: The lumbar spine plays a vital role in load transfer and mobility. Localization and segmentation of vertebrae are valuable for detecting spinal deformities and fractures. Automated medical imagery understanding is crucial for assisting doctors in time-consuming manual or semi-manual diagnosis. This paper presents methods to help clinicians confidently grade the severity of diseases.

SENSORS (2022)

Article Multidisciplinary Sciences

Using deep transfer learning to detect scoliosis and spondylolisthesis from x-ray images

Mohammad Fraiwan et al.

Summary: In recent years, there has been an increasing prevalence of vertebral column pathologies due to lifestyle changes, sedentary behaviors, or injuries. This study utilizes advances in deep transfer learning to diagnose spondylolisthesis and scoliosis from X-ray images without the need for any measurements. The results show a high accuracy in diagnosing vertebral column disorders and can serve as a supporting tool for physicians to reduce workload and errors.

PLOS ONE (2022)

Article Orthopedics

Can a Deep-learning Model for the Automated Detection of Vertebral Fractures Approach the Performance Level of Human Subspecialists?

Yi-Chu Li et al.

Summary: Recent studies have shown that artificial intelligence can perform as well as humans in detecting osteoporotic fractures. The AI model's performance is affected by patients' clinical data and the severity of the fracture.

CLINICAL ORTHOPAEDICS AND RELATED RESEARCH (2021)

Article Computer Science, Interdisciplinary Applications

Automatic detection and segmentation of lumbar vertebrae from X-ray images for compression fracture evaluation

Kang Cheol Kim et al.

Summary: An automatic X-ray image segmentation technique combining deep-learning and level-set methods is proposed for compression fracture detection and evaluation. This structured hierarchical segmentation method utilizes pose-driven learning and M-net to accurately identify lumbar vertebrae and segment individual vertebrae. Fine tuning segmentation is achieved by combining the level-set method with the obtained segmentation results, resulting in accurate and robust identification of each lumbar vertebra.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2021)

Article Multidisciplinary Sciences

Deep learning approach for automatic landmark detection and alignment analysis in whole-spine lateral radiographs

Yu-Cheng Yeh et al.

Summary: This study demonstrates the use of deep learning models for automatic localization of spinal anatomical landmarks and generation of radiographic parameters with favorable correlations and high accuracy. The comparison between human and artificial intelligence shows that the deep learning model matches the reliability of doctors for most parameters.

SCIENTIFIC REPORTS (2021)

Article Sport Sciences

Lumbosacral Spondylolysis and Spondylolisthesis

Christopher C. Chung et al.

CLINICS IN SPORTS MEDICINE (2021)

Article Computer Science, Information Systems

RTFN: A robust temporal feature network for time series classification

Zhiwen Xiao et al.

Summary: Time series data contains both local and global patterns, but existing feature networks focus on local features and neglect the relationships among them. Therefore, a novel RTFN method is proposed for feature extraction in time series, consisting of TFN and LSTMaN. Experimental results show that the RTFN-based structures achieve excellent performance on multiple datasets.

INFORMATION SCIENCES (2021)

Article Computer Science, Interdisciplinary Applications

The effect of non -fusion dynamic stabilization on biomechanical responses of the implanted lumbar spine during whole -body vibration

Wei Fan et al.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2020)

Article Clinical Neurology

Intervertebral disc classification by its degree of degeneration from T2-weighted magnetic resonance images

Isaac Castro-Mateos et al.

EUROPEAN SPINE JOURNAL (2016)