A Predictive Model for Assessment of Successful Outcome in Posterior Spinal Fusion Surgery

Background: Low back pain is a common problem in many people. Neurosurgeons recommend posterior spinal fusion (PSF) surgery as one of the therapeutic strategies to the patients with low back pain. Due to the high risk of this type of surgery and the critical importance of making the right decision, accurate prediction of the surgical outcome is one of the main concerns for the neurosurgeons.Methods: In this study, 12 types of multi-layer perceptron (MLP) networks and 66 radial basis function (RBF) networks as the types of artificial neural network methods and a logistic regression (LR) model created and compared to predict the satisfaction with PSF surgery as one of the most well-known spinal surgeries.Results: The most important clinical and radiologic features as twenty-seven factors for 480 patients (150 males, 330 females; mean age 52.32 ± 8.39 years) were considered as the model inputs that included: age, sex, type of disorder, duration of symptoms, job, walking distance without pain (WDP), walking distance without sensory (WDS) disorders, visual analog scale (VAS) scores, Japanese Orthopaedic Association (JOA) score, diabetes, smoking, knee pain (KP), pelvic pain (PP), osteoporosis, spinal deformity and etc. The indexes such as receiver operating characteristic–area under curve (ROC-AUC), positive predictive value, negative predictive value and accuracy calculated to determine the best model. Postsurgical satisfaction was 77.5% at 6 months follow-up. The patients divided into the training, testing, and validation data sets.Conclusion: The findings showed that the MLP model performed better in comparison with RBF and LR models for prediction of PSF surgery.Keywords: Posterior spinal fusion surgery (PSF); Prediction, Surgical satisfaction; Multi-layer perceptron (MLP); Logistic regression (LR) (PDF) A Predictive Model for Assessment of Successful Outcome in Posterior Spinal Fusion Surgery. Available from: https://www.researchgate.net/publication/325679954_A_Predictive_Model_for_Assessment_of_Successful_Outcome_in_Posterior_Spinal_Fusion_Surgery [accessed Jul 11 2019].Peer reviewe

Artificial intelligence in dentistry, orthodontics and orthognathic surgery: A literature review

Artificial intelligence is the ability of machines to work like humans. The concept initially began with the advent of mathematical models which gave calculated outputs based on inputs fed into the system. This was later modified with the introduction of various algorithms which can either give output based on overall data analysis or by selection of information within previous data. It is steadily becoming a favoured mode of treatment due to its efficiency and ability to manage complex conditions in all specialities. In dentistry, artificial intelligence has also popularised over the past few decades. They have been found useful for diagnosis in restorative dentistry, oral pathology and oral surgery. In orthodontics, they have been utilised for diagnosis, assessment of treatment needs, cephalometrics, treatment planning and orthognathic surgeries etc. The current literature review was planned to highlight the uses of artificial intelligence in dentistry, specifically in orthodontics and orthognathic surgery

Body Mass Estimation from the Human Skeleton

The established methods for estimating average body mass from the skeleton are of two types: biomechanical and morphometric. Neither technique currently addresses the extremes of body mass (e.g. emaciation or obesity). The goal of this research is to explore several different biomechanical methods, using data collected from high resolution computed tomographic scans and macroscopic analysis of 150 known modern individuals from the William M. Bass Donated Skeleton Collection at the University of Tennessee, Knoxville. This research will review the biomechanics of human gait and the biomechanical accommodations that occur with increased obesity and load bearing. The analysis will include cross-sectional geometry of the human femur at five locations along the diaphysis, bone mineral density scans of the proximal femur and a macroscopic evaluation of degenerative changes of the articulations of the spine, hip, knee and foot. The best single indicator of body mass for both males and females is the cross-sectional area of the proximal femur and BMD. By using pathologies combined, an accuracy rate of 87% for predicting obesity was achieved using a classification tree with sexes pooled. Furthermore, severe obesity has such a profound effect on the human skeleton as to leave a suite of traits affecting the load bearing elements of the lower limb and vertebral column

Fully automatic image analysis framework for cervical vertebra in X-ray images

Despite the advancement in imaging technologies, a fifth of the injuries in the cervical spine remain unnoticed in the X-ray radiological exam. About a two-third of the subjects with unnoticed injuries suffer tragic consequences. Based on the success of computer-aided systems in several medical image modalities to enhance clinical interpretation, we have proposed a fully automatic image analysis framework for cervical vertebrae in X-ray images. The framework takes an X-ray image as input and highlights different vertebral features at the output. To the best of our knowledge, this is the first fully automatic system in the literature for the analysis of the cervical vertebrae. The complete framework has been built by cascading specialized modules, each of which addresses a specific computer vision problem. This dissertation explores data-driven supervised machine learning solutions to these problems. Given an input X-ray image, the first module localizes the spinal region. The second module predicts vertebral centers from the spinal region which are then used to generate vertebral image patches. These patches are then passed through machine learning modules that detect vertebral corners, highlight vertebral boundaries, segment vertebral body and predict vertebral shapes. In the process of building the complete framework, we have proposed and compared different solutions to the problems addressed by each of the modules. A novel region-aware dense classification deep neural network has been proposed for the first module to address the spine localization problem. The proposed network outperformed the standard dense classification network and random forestbased methods. Location of the vertebral centers and corners vary based on human interpretation and thus are better represented by probability maps than single points. To learn the mapping between the vertebral image patches and the probability maps, a novel neural network capable of predicting a spatially distributed probabilistic distribution has been proposed. The network achieved expert-level performance in localizing vertebral centers and outperform the Harris corner detector and Hough forest-based methods for corner localization. The proposed network has also shown its capability for detecting vertebral boundaries and produced visually better results than the dense classification network-based boundary detectors. Segmentation of the vertebral body is a crucial part of the proposed framework. A new shapeaware loss function has been proposed for training a segmentation network to encourage prediction of vertebra-like structures. The segmentation performance improved significantly, however, the pixel-wise nature of proposed loss function was not able to constrain the predictions adequately. To solve the problem a novel neural network was proposed which predicts vertebral shapes and trains on a loss function defined in the shape space. The proposed shape predictor network was capable of learning better topological information about the vertebra than the shape-aware segmentation network. The methods proposed in this dissertation have been trained and tested on a challenging dataset of X-ray images collected from medical emergency rooms. The proposed, first-of-its-kind, fully automatic framework produces state-of-the-art results both quantitatively and qualitatively

Machine learning in orthopedics: a literature review

In this paper we present the findings of a systematic literature review covering the articles published in the last two decades in which the authors described the application of a machine learning technique and method to an orthopedic problem or purpose. By searching both in the Scopus and Medline databases, we retrieved, screened and analyzed the content of 70 journal articles, and coded these resources following an iterative method within a Grounded Theory approach. We report the survey findings by outlining the articles\u2019 content in terms of the main machine learning techniques mentioned therein, the orthopedic application domains, the source data and the quality of their predictive performance

Machine Learning in Orthopedics: A Literature Review

In this paper we present the findings of a systematic literature review covering the articles published in the last two decades in which the authors described the application of a machine learning technique and method to an orthopedic problem or purpose. By searching both in the Scopus and Medline databases, we retrieved, screened and analyzed the content of 70 journal articles, and coded these resources following an iterative method within a Grounded Theory approach. We report the survey findings by outlining the articles' content in terms of the main machine learning techniques mentioned therein, the orthopedic application domains, the source data and the quality of their predictive performance

Three-Dimensional Measurement of Spinal Kinematics and Whole-Body Activity Recognition

Back pain is one of the leading causes of disability, being the second largest contributor to work days missed, and sixth largest disability when expressed in terms of an overall burden measured in disability-adjusted life years. Back pain is a large economic burden, where indirect costs from work days missed far outweigh the direct costs due to treatment. As such, it is economically better to prevent back pain from occurring, rather than treating it after the onset of pain. Some risk factors of back pain which can be monitored to help in the prevention of pain include poor posture and prolonged sedentary behaviour. Inactivity, being similar to prolonged sedentary behaviour, is also a risk factor for some of the major non-communicable diseases responsible for death including heart diseases, stroke, breast and colon cancer, and diabetes. The aims of the thesis were to: 1) compare a number of commonly used measurement systems, including a low-cost wearable sensor, in their ability to measure motion typically seen in the human spine; 2) develop an activity classification model capable of predicting everyday activities including standing, sitting, lying, and walking; 3) create a new, inexpensive device that can simultaneously track user spine posture/kinematics and activity; and 4) validate the device to have accuracy within ±5° for spine posture, and an average positive activity classification rate of 90% or above. This research demonstrates the accuracy of a low-cost wearable sensor in its ability to track motion similar to that of the human spine under typical conditions and compare this to more expensive systems. Using two accelerometers and machine learning, a new activity recognition model was created with the ability to track 13 distinct activities commonly used in daily living, being: standing, sitting, prone, supine, right-side, and left-side lying, walking, jogging, jumping, stair ascending, stair descending, walking on an incline, and transitions. From this new knowledge, a new concept inertial-sensor-based device was created with the capabilities of measuring spinal kinematics and whole-body activity tracking. The device has been developed to measure spinal motions with mean errors of ±2.5°, and therefore meeting the aim to have an accuracy within ±5°, while also showing that the more superior the position on the spine an inertial sensor is placed, the higher the errors in measurement. The device can also predict standing, sitting, lying, and walking with an average accuracy of 95.6%, and therefore above the desired accuracy of 90%. When including all activities, the classifier has an average accuracy of 90.3%. To reduce the global effect of back pain, the developed device has the capabilities to aid in the prevention, management, and rehabilitation of back pain by focussing on two risk factors: poor posture and inactivity. For use in this research, the definition of a good posture is one that compromises between minimising spinal load and minimise muscle activity, therefore a poor posture is one that doesn’t adhere to this requirement which could significantly increase the risk of the onset of back pain. For widespread use, the device created in this research has been developed to be as inexpensive as possible. To meet these goals, the future work of the device has been outlined, including size and cost reduction, as well as increasing the aesthetic appeal, thus making it a more appealing product to the general population.Thesis (Ph.D.) -- University of Adelaide, School of Mechanical Engineering, 201

A Robotic Neuro-Musculoskeletal Simulator for Spine Research

An influential conceptual framework advanced by Panjabi represents the living spine as a complex neuromusculoskeletal system whose biomechanical functioning is rather finely dependent upon the interactions among and between three principal subsystems: the passive musculoskeletal subsystem (osteoligamentous spine plus passive mechanical contributions of the muscles), the active musculoskeletal subsystem (muscles and tendons), and the neural and feedback subsystem (neural control centers and feedback elements such as mechanoreceptors located in the soft tissues) [1]. The interplay between subsystems readily encourages thought experiments of how pathologic changes in one subsystem might influence another--for example, prompting one to speculate how painful arthritic changes in the facet joints might affect the neuromuscular control of spinal movement. To answer clinical questions regarding the interplay between these subsystems the proper experimental tools and techniques are required. Traditional spine biomechanical experiments are able to provide comprehensive characterization of the structural properties of the osteoligamentous spine. However, these technologies do not incorporate a simulated neural feedback from neural elements, such as mechanoreceptors and nociceptors, into the control loop. Doing so enables the study of how this feedback--including pain-related--alters spinal loading and motion patterns. The first such development of this technology was successfully completed in this study and constitutes a Neuro-Musculoskeletal Simulator. A Neuro-Musculoskeletal Simulator has the potential to reduce the gap between bench and bedside by creating a new paradigm in estimating the outcome of spine pathologies or surgeries. The traditional paradigm is unable to estimate pain and is also unable to determine how the treatment, combined with the natural pain avoidance of the patient, would transfer the load to other structures and potentially increase the risk for other problems. The novel Neuro-Musculo

Recent Advances in Forensic Anthropological Methods and Research

Forensic anthropology, while still relatively in its infancy compared to other forensic science disciplines, adopts a wide array of methods from many disciplines for human skeletal identification in medico-legal and humanitarian contexts. The human skeleton is a dynamic tissue that can withstand the ravages of time given the right environment and may be the only remaining evidence left in a forensic case whether a week or decades old. Improved understanding of the intrinsic and extrinsic factors that modulate skeletal tissues allows researchers and practitioners to improve the accuracy and precision of identification methods ranging from establishing a biological profile such as estimating age-at-death, and population affinity, estimating time-since-death, using isotopes for geolocation of unidentified decedents, radiology for personal identification, histology to assess a live birth, to assessing traumatic injuries and so much more

UGA Anatomy and Physiology 1 Lab Manual, 3rd Edition

This lab manual was created for Anatomy and Physiology I at the University of Georgia under a Textbook Transformation Grant and revised through a Scaling Up OER Pilot Grant. The manual contains the following labs: Introduction to Anatomy & Physiology Cells Histology – Epithelial & Connective Tissues Histology – Muscle & Nervous Tissues The Integumentary System Introduction to the Skeletal System Introduction Joints The Lower Limb – Bones The Lower Limb – Muscles The Lower Limb – Joints The Lower Limb – Nerves The Lower Limb – Movement The Upper Limb – Bones The Upper Limb – Muscles The Upper Limb – Joints The Upper Limb – Nerves The Upper Limb – Movement Muscle Physiology Axial Skeleton Axial Musculature Intervertebral Discs Central Nervous System – The Spinal Cord Central Nervous System – The Brain Motor Control The Senses – Vision The Senses - Hearing Accessible files with optical character recognition (OCR) and auto-tagging provided by the Center for Inclusive Design and Innovation.https://oer.galileo.usg.edu/biology-textbooks/1013/thumbnail.jp