A deep learning system for automated, multi-modality 2D segmentation of vertebral bodies and intervertebral discs

A Suri, BC Jones, G Ng, N Anabaraonye, P Beyrer… - Bone, 2021 - Elsevier
Purpose Fractures in vertebral bodies are among the most common complications of
osteoporosis and other bone diseases. However, studies that aim to predict future fractures …

Spinopelvic measurements of sagittal balance with deep learning: systematic review and critical evaluation

T Vrtovec, B Ibragimov - European Spine Journal, 2022 - Springer
Purpose To summarize and critically evaluate the existing studies for spinopelvic
measurements of sagittal balance that are based on deep learning (DL). Methods Three …

Recent advances and new discoveries in the pipeline of the treatment of primary spinal tumors and spinal metastases: a scoping review of registered clinical studies …

JC Furlan, JR Wilson, EM Massicotte, A Sahgal… - Neuro …, 2022 - academic.oup.com
The field of spinal oncology has substantially evolved over the past decades. This review
synthesizes and appraises what was learned and what will potentially be discovered from …

Interpretable machine learning models to predict short-term postoperative outcomes following posterior cervical fusion

M Karabacak, K Margetis - Plos one, 2023 - journals.plos.org
By predicting short-term postoperative outcomes before surgery, patients who undergo
posterior cervical fusion (PCF) surgery may benefit from more precise patient care plans that …

Machine learning identifies chronic low back pain patients from an instrumented trunk bending and return test

P Thiry, M Houry, L Philippe, O Nocent, F Buisseret… - Sensors, 2022 - mdpi.com
Nowadays, the better assessment of low back pain (LBP) is an important challenge, as it is
the leading musculoskeletal condition worldwide in terms of years of disability. The objective …

Current applications of machine learning for spinal cord tumors

K Katsos, SE Johnson, S Ibrahim, M Bydon - Life, 2023 - mdpi.com
Spinal cord tumors constitute a diverse group of rare neoplasms associated with significant
mortality and morbidity that pose unique clinical and surgical challenges. Diagnostic …

Development of a machine-learning based model for predicting multidimensional outcome after surgery for degenerative disorders of the spine

D Müller, D Haschtmann, TF Fekete, F Kleinstück… - European Spine …, 2022 - Springer
Background It is clear that individual outcomes of spine surgery can be quite
heterogeneous. When consenting a patient for surgery, it is important to be able to offer an …

Automatic annotation of cervical vertebrae in videofluoroscopy images via deep learning

Z Zhang, S Mao, J Coyle, E Sejdić - Medical image analysis, 2021 - Elsevier
Judging swallowing kinematic impairments via videofluoroscopy represents the gold
standard for the detection and evaluation of swallowing disorders. However, the efficiency …

Stem cell imaging through convolutional neural networks: current issues and future directions in artificial intelligence technology

RR Ramakrishna, Z Abd Hamid, WMDW Zaki… - PeerJ, 2020 - peerj.com
Stem cells are primitive and precursor cells with the potential to reproduce into diverse
mature and functional cell types in the body throughout the developmental stages of life …

Predicting vertebral bone strength using finite element analysis for opportunistic osteoporosis screening in routine multidetector computed tomography scans—a …

NM Rayudu, M Dieckmeyer, MT Löffler… - Frontiers in …, 2021 - frontiersin.org
Purpose To investigate the feasibility of using routine clinical multidetector computed
tomography (MDCT) scans for conducting finite element (FE) analysis to predict vertebral …