Quantitative Analysis of Neural Foramina in the Lumbar Spine: An Imaging Informatics and Machine Learning Study.
Ontology highlight
ABSTRACT: Purpose
To use machine learning tools and leverage big data informatics to statistically model the variation in the area of lumbar neural foramina in a large asymptomatic population.Materials and methods
By using an electronic health record and imaging archive, lumbar MRI studies in 645 male (mean age, 50.07 years) and 511 female (mean age, 48.23 years) patients between 20 and 80 years old were identified. Machine learning algorithms were used to delineate lumbar neural foramina autonomously and measure their areas. The relationship between neural foraminal area and patient age, sex, and height was studied by using multivariable linear regression.Results
Neural foraminal areas correlated directly with patient height and inversely with patient age. The associations involved were statistically significant (P < .01).Conclusion
By using machine learning and big data techniques, a linear model encoding variation in lumbar neural foraminal areas in asymptomatic individuals has been established. This model can be used to make quantitative assessments of neural foraminal areas in patients by comparing them to the age-, sex-, and height-adjusted population averages.© RSNA, 2019Supplemental material is available for this article.
SUBMITTER: Gaonkar B
PROVIDER: S-EPMC8017393 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Biological imaging is now a quantitative technique for probing cellular structure and dynamics and is increasingly used for cell-based screens. However, the bioinformatics tools required for hypothesis-driven analysis of digital images are still immature. We are developing the Open Microscopy Environment (OME) as an informatics solution for the storage and analysis of optical microscope image data. OME aims to automate image analysis, modeling, and mining of large sets of images and specifies a flexible data model, a relational database, and an XML-encoded file standard that is usable by potentially any software tool. With this design, OME provides a first step toward biological image informatics.
| S-EPMC3522889 | biostudies-literature
Project description:BackgroundMorphological parameters of the lumbar spine are valuable in assessing lumbar spine diseases. However, manual measurement of lumbar morphological parameters is time-consuming. Deep learning has automatic quantitative and qualitative analysis capabilities. To develop a deep learning-based model for the automatic quantitative measurement of morphological parameters from anteroposterior digital radiographs of the lumbar spine and to evaluate its performance.MethodsThis study used 1,368 anteroposterior digital radiographs of the lumbar spine to train a deep learning model to measure the quantitative morphological indicators, including L1 to L5 vertebral body height (VBH) and L1-L2 to L4-L5 intervertebral disc height (IDH). The means of the manual measurements by three radiologists were used as the reference standard. The parameters predicted by the model were analyzed against the manual measurements using paired t-tests. Percentage of correct key points (PCK), intra-class correlation coefficient (ICC), Pearson correlation coefficient (r), mean absolute error (MAE), root mean square error (RMSE), and Bland-Altman plots were performed to assess the performance of the model.ResultsWithin the 3-mm distance threshold, the model had a PCK range of 99.77-99.46% for the L1 to L4 vertebrae and 77.37% for the L5 vertebrae. Except for VBH-L5 and IDH_L3-L4, IDH_L4-L5 (P<0.05), the estimated values of the model in the remaining parameters were not statistically significant compared with the reference standard (P>0.05). Except for VBH-L5 and IDH_L4-L5, the model showed good correlation and consistency with the reference standard (ICC =0.84-0.96, r=0.85-0.97, MAE =0.5-0.66, RMSE =0.66-0.95). The model outperformed other models (EfficientDet + Unet, EfficientDet + DarkPose, HRNet, and Unet) in predicting landmarks within a distance threshold of 1.5 to 5 mm.ConclusionsThe model developed in this study can automatically measure the morphological parameters of the L1 to L4 vertebrae from anteroposterior digital radiographs of the lumbar spine. Its performance is close to the level of radiologists.
| S-EPMC11320550 | biostudies-literature
Project description:RATIONALE AND OBJECTIVES:To evaluate a natural language processing (NLP) system built with open-source tools for identification of lumbar spine imaging findings related to low back pain on magnetic resonance and x-ray radiology reports from four health systems. MATERIALS AND METHODS:We used a limited data set (de-identified except for dates) sampled from lumbar spine imaging reports of a prospectively assembled cohort of adults. From N?=?178,333 reports, we randomly selected N?=?871 to form a reference-standard dataset, consisting of N?=?413 x-ray reports and N?=?458 MR reports. Using standardized criteria, four spine experts annotated the presence of 26 findings, where 71 reports were annotated by all four experts and 800 were each annotated by two experts. We calculated inter-rater agreement and finding prevalence from annotated data. We randomly split the annotated data into development (80%) and testing (20%) sets. We developed an NLP system from both rule-based and machine-learned models. We validated the system using accuracy metrics such as sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). RESULTS:The multirater annotated dataset achieved inter-rater agreement of Cohen's kappa?>?0.60 (substantial agreement) for 25 of 26 findings, with finding prevalence ranging from 3% to 89%. In the testing sample, rule-based and machine-learned predictions both had comparable average specificity (0.97 and 0.95, respectively). The machine-learned approach had a higher average sensitivity (0.94, compared to 0.83 for rules-based), and a higher overall AUC (0.98, compared to 0.90 for rules-based). CONCLUSIONS:Our NLP system performed well in identifying the 26 lumbar spine findings, as benchmarked by reference-standard annotation by medical experts. Machine-learned models provided substantial gains in model sensitivity with slight loss of specificity, and overall higher AUC.
| S-EPMC6162177 | biostudies-literature
Project description:PurposeTo characterize the inter- and intraobserver variability of qualitative, non-disk contour degenerative findings of the lumbar spine at magnetic resonance (MR) imaging.Materials and methodsThe case accrual method used to perform this institutional review board-approved, HIPAA-compliant retrospective study was the random selection of 111 interpretable MR examination cases of subjects from the Spine Patient Outcomes Research Trial. The subjects were aged 18-87 years (mean, 53 years +/- 16 [standard deviation]). Four independent readers rated the cases according to defined criteria. A subsample of 40 MR examination cases was selected for reevaluation at least 1 month later. The following findings were assessed: spondylolisthesis, disk degeneration, marrow endplate abnormality (Modic changes), posterior anular hyperintense zone (HIZ), and facet arthropathy. Inter- and intraobserver agreement in rating the data was summarized by using weighted kappa statistics.ResultsInterobserver agreement was good (kappa = 0.66) in rating disk degeneration and moderate in rating spondylolisthesis (kappa = 0.55), Modic changes (kappa = 0.59), facet arthropathy (kappa = 0.54), and posterior HIZ (kappa = 0.44). Interobserver agreement in rating the extent of Modic changes was moderate: kappa Values were 0.43 for determining superior anteroposterior extent, 0.47 for determining superior craniocaudal extent, 0.57 for determining inferior anteroposterior extent, and 0.48 for determining inferior craniocaudal extent. Intraobserver agreement was good in rating spondylolisthesis (kappa = 0.66), disk degeneration (kappa = 0.74), Modic changes (kappa = 0.64), facet arthropathy (kappa = 0.69), and posterior HIZ (kappa = 0.67). Intraobserver agreement in rating the extent of Modic changes was moderate, with kappa values of 0.54 for superior anteroposterior, 0.60 for inferior anteroposterior, 0.50 for superior craniocaudal, and 0.60 for inferior craniocaudal extent determinations.ConclusionThe interpretation of general lumbar spine MR characteristics has sufficient reliability to warrant the further evaluation of these features as potential prognostic indicators.
| S-EPMC2657480 | biostudies-literature
Project description:OBJECTIVES:With the objective of bringing clinical decision support systems to reality, this article reviews histopathological whole-slide imaging informatics methods, associated challenges, and future research opportunities. TARGET AUDIENCE:This review targets pathologists and informaticians who have a limited understanding of the key aspects of whole-slide image (WSI) analysis and/or a limited knowledge of state-of-the-art technologies and analysis methods. SCOPE:First, we discuss the importance of imaging informatics in pathology and highlight the challenges posed by histopathological WSI. Next, we provide a thorough review of current methods for: quality control of histopathological images; feature extraction that captures image properties at the pixel, object, and semantic levels; predictive modeling that utilizes image features for diagnostic or prognostic applications; and data and information visualization that explores WSI for de novo discovery. In addition, we highlight future research directions and discuss the impact of large public repositories of histopathological data, such as the Cancer Genome Atlas, on the field of pathology informatics. Following the review, we present a case study to illustrate a clinical decision support system that begins with quality control and ends with predictive modeling for several cancer endpoints. Currently, state-of-the-art software tools only provide limited image processing capabilities instead of complete data analysis for clinical decision-making. We aim to inspire researchers to conduct more research in pathology imaging informatics so that clinical decision support can become a reality.
| S-EPMC3822114 | biostudies-literature
Project description:We aimed to assess feasibility of a support vector machine (SVM) texture classifier to discriminate pathologic infiltration patterns from the normal bone marrows in MRI. This retrospective study included 467 cases, which were split into a training (n = 360) and a test set (n = 107). A sagittal T1-weighted lumbar spinal MR image was normalized by an intervertebral disk, and bone marrows were segmented. The various kernel functions and SVM input dimensions were experimented to construct the most optimal classifier model. The accuracy and sensitivity increased as the number of training set sizes increased from 180 to 360. The test set was analyzed by SVM and two independent readers, and the accuracy and sensitivity of the SVM classifier, reader 1 and reader 2 were 82.2% and 85.5%, 79.4% and 82.3%, and 82.2% and 83.9%, respectively. The area under receiver operating characteristic curve (AUC) of the SVM classifier, reader 1 and reader 2 were 0.895, 0.879 and 0.880, respectively. The SVM texture classifier produced comparable performance to radiologists in isolating the hematologic diseases, which could support inexperienced physicians with spinal MRI to screen patients with marrow diseases, who need further diagnostic work-ups to make final decisions.
| S-EPMC6465258 | biostudies-literature
Project description:Optoacoustic (OA) imaging is a promising modality for quantifying blood oxygen saturation (sO2) in various biomedical applications - in diagnosis, monitoring of organ function, or even tumor treatment planning. We present an accurate and practically feasible real-time capable method for quantitative imaging of sO2 based on combining multispectral (MS) and multiple illumination (MI) OA imaging with learned spectral decoloring (LSD). For this purpose we developed a hybrid real-time MI MS OA imaging setup with ultrasound (US) imaging capability; we trained gradient boosting machines on MI spectrally colored absorbed energy spectra generated by generic Monte Carlo simulations and used the trained models to estimate sO2 on real OA measurements. We validated MI-LSD in silico and on in vivo image sequences of radial arteries and accompanying veins of five healthy human volunteers. We compared the performance of the method to prior LSD work and conventional linear unmixing. MI-LSD provided highly accurate results in silico and consistently plausible results in vivo. This preliminary study shows a potentially high applicability of quantitative OA oximetry imaging, using our method.
| S-EPMC9203099 | biostudies-literature
Project description:Abnormalities and defects that can cause lumbar spinal stenosis often occur in the Intervertebral Disc (IVD) of the patient's lumbar spine. Their automatic detection and classification require an application of an image analysis algorithm on suitable images, such as mid-sagittal images or traverse mid-height intervertebral disc slices, as inputs. Hence the process of selecting and separating these images from other medical images in the patient's set of scans is necessary. However, the technological progress in making this process automated is still lagging behind other areas in medical image classification research. In this paper, we report the result of our investigation on the suitability and performance of different approaches of machine learning to automatically select the best traverse plane that cuts closest to the half-height of an IVD from a database of lumbar spine MRI images. This study considers images features extracted using eleven different pre-trained Deep Convolution Neural Network (DCNN) models. We investigate the effectiveness of three dimensionality-reduction techniques and three feature-selection techniques on the classification performance. We also investigate the performance of five different Machine Learning (ML) algorithms and three Fully Connected (FC) neural network learning optimizers which are used to train an image classifier with hyperparameter optimization using a wide range of hyperparameter options and values. The different combinations of methods are tested on a publicly available lumbar spine MRI dataset consisting of MRI studies of 515 patients with symptomatic back pain. Our experiment shows that applying the Support Vector Machine algorithm with a short Gaussian kernel on full-length image features extracted using a pre-trained DenseNet201 model is the best approach to use. This approach gives the minimum per-class classification performance of around 0.88 when measured using the precision and recall metrics. The median performance measured using the precision metric ranges from 0.95 to 0.99 whereas that using the recall metric ranges from 0.93 to 1.0. When only considering the L3/L4, L4/L5, and L5/S1 classes, the minimum F1-Scores range between 0.93 to 0.95, whereas the median F1-Scores range between 0.97 to 0.99.
| S-EPMC8758114 | biostudies-literature
Project description:Recent years have seen a dramatic increase in the application of organoids to developmental biology, biomedical and translational studies. Organoids are large structures with high phenotypic complexity and are imaged on a wide range of platforms, from simple benchtop stereoscopes to high-content confocal-based imaging systems. The large volumes of images, resulting from hundreds of organoids cultured at once, are becoming increasingly difficult to inspect and interpret. Hence, there is a pressing demand for a coding-free, intuitive and scalable solution that analyses such image data in an automated yet rapid manner. Here, we present MOrgAna, a Python-based software that implements machine learning to segment images, quantify and visualize morphological and fluorescence information of organoids across hundreds of images, each with one object, within minutes. Although the MOrgAna interface is developed for users with little to no programming experience, its modular structure makes it a customizable package for advanced users. We showcase the versatility of MOrgAna on several in vitro systems, each imaged with a different microscope, thus demonstrating the wide applicability of the software to diverse organoid types and biomedical studies.
| S-EPMC8451065 | biostudies-literature
Project description:Brain imaging genomics is an emerging data science field, where integrated analysis of brain imaging and genomics data, often combined with other biomarker, clinical and environmental data, is performed to gain new insights into the phenotypic, genetic and molecular characteristics of the brain as well as their impact on normal and disordered brain function and behavior. It has enormous potential to contribute significantly to biomedical discoveries in brain science. Given the increasingly important role of statistical and machine learning in biomedicine and rapidly growing literature in brain imaging genomics, we provide an up-to-date and comprehensive review of statistical and machine learning methods for brain imaging genomics, as well as a practical discussion on method selection for various biomedical applications.
| S-EPMC6941751 | biostudies-literature