Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Background: Measuring spinal alignment with radiological parameters is essential in patients with spinal conditions likely to be treated surgically. These evaluations are not usually included in the radiological report. As a result, spinal surgeons commonly perform the measurement, which is time-consuming and subject to errors. We aim to develop a fully automated artificial intelligence (AI) tool to assist in measuring alignment parameters in whole-spine lateral radiograph (WSL X-rays).
Methods: We developed a tool called Vertebrai that automatically calculates the global spinal parameters (GSPs): Pelvic incidence, sacral slope, pelvic tilt, L1-L4 angle, L4-S1 lumbo-pelvic angle, T1 pelvic angle, sagittal vertical axis, cervical lordosis, C1-C2 lordosis, lumbar lordosis, mid-thoracic kyphosis, proximal thoracic kyphosis, global thoracic kyphosis, T1 slope, C2-C7 plummet, spino-sacral angle, C7 tilt, global tilt, spinopelvic tilt, and hip odontoid axis. We assessed human-AI interaction instead of AI performance alone. We compared the time to measure GSP and inter-rater agreement with and without AI assistance. Two institutional datasets were created with 2267 multilabel images for classification and 784 WSL X-rays with reference standard landmark labeled by spinal surgeons.
Results: Vertebrai significantly reduced the measurement time comparing spine surgeons with AI assistance and the AI algorithm alone, without human intervention (3 minutes vs. 0.26 minutes; P < 0.05). Vertebrai achieved an average accuracy of 83% in detecting abnormal alignment values, with the sacral slope parameter exhibiting the lowest accuracy at 61.5% and spinopelvic tilt demonstrating the highest accuracy at 100%. Intraclass correlation analysis revealed a high level of correlation and consistency in the global alignment parameters.
Conclusions: Vertebrai's measurements can accurately detect alignment parameters, making it a promising tool for measuring GSP automatically.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.wneu.2024.04.091 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Nuclear receptors in metabolic, inflammatory, and oncologic diseases: mechanisms, therapeutic advances, and future directions.
Eur J Med Res
September 2025
Department of Zoology, Faculty of Science, Ain Shams University, Abbassia, Cairo, 11566, Egypt.
Nuclear receptors (NRs) are a superfamily of ligand-activated transcription factors that regulate gene expression in response to metabolic, hormonal, and environmental signals. These receptors play a critical role in metabolic homeostasis, inflammation, immune function, and disease pathogenesis, positioning them as key therapeutic targets. This review explores the mechanistic roles of NRs such as PPARs, FXR, LXR, and thyroid hormone receptors (THRs) in regulating lipid and glucose metabolism, energy expenditure, cardiovascular health, and neurodegeneration.
View Article and Find Full Text PDFTwo step approach for detecting and segmenting the second mesiobuccal canal of maxillary first molars on cone beam computed tomography (CBCT) images via artificial intelligence.
BMC Oral Health
September 2025
Oral and Maxillofacial Radiology Department, Cairo university, Cairo, Egypt.
Aim: The purpose of this study was to assess the accuracy of a customized deep learning model based on CNN and U-Net for detecting and segmenting the second mesiobuccal canal (MB2) of maxillary first molar teeth on cone beam computed tomography (CBCT) scans.
Methodology: CBCT scans of 37 patients were imported into 3D slicer software to crop and segment the canals of the mesiobuccal (MB) root of the maxillary first molar. The annotated data were divided into two groups: 80% for training and validation and 20% for testing.
A systematic review of EEG-based machine learning classifications for obsessive-compulsive disorder: current status and future directions.
BMC Psychiatry
September 2025
Department of Cognitive Neuroscience, Faculty of Biology, Bielefeld University, Bielefeld, Germany.
Obsessive-compulsive disorder (OCD) is a chronic and disabling condition affecting approximately 3.5% of the global population, with diagnosis on average delayed by 7.1 years or often confounded with other psychiatric disorders.
View Article and Find Full Text PDFUse of artificial intelligence for classification of fractures around the elbow in adults according to the 2018 AO/OTA classification system.
BMC Musculoskelet Disord
September 2025
Department of Clinical Sciences at Danderyds Hospital, Department of Orthopedic Surgery, Karolinska Institutet, Stockholm, 182 88, Sweden.
Background: This study evaluates the accuracy of an Artificial Intelligence (AI) system, specifically a convolutional neural network (CNN), in classifying elbow fractures using the detailed 2018 AO/OTA fracture classification system.
Methods: A retrospective analysis of 5,367 radiograph exams visualizing the elbow from adult patients (2002-2016) was conducted using a deep neural network. Radiographs were manually categorized according to the 2018 AO/OTA system by orthopedic surgeons.
The imitation game: large language models versus multidisciplinary tumor boards: benchmarking AI against 21 sarcoma centers from the ring trial.
J Cancer Res Clin Oncol
September 2025
Department of Surgery, Mannheim School of Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
Purpose: The study aims to compare the treatment recommendations generated by four leading large language models (LLMs) with those from 21 sarcoma centers' multidisciplinary tumor boards (MTBs) of the sarcoma ring trial in managing complex soft tissue sarcoma (STS) cases.
Methods: We simulated STS-MTBs using four LLMs-Llama 3.2-vison: 90b, Claude 3.