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

Локализация позвонков человека на рентгеновских изображениях с использованием Darknet YOLО

Предлагается алгоритм, позволяющий с использованием сверточной нейронной сети на основе регионов Darknet YOLO осуществлять локализацию позвонков на рентгеновских изображениях с последующим определением геометрических параметров с помощью библиотеки компьютерного зрения OpenCV.Technology that allows to localize vertebrae on X-ray images and then determine geometric parameters using the OpenCV computer vision library using a convolutional neural network Darknet YOLO based on regions is proposed

ЛОКАЛИЗАЦИЯ ПОЗВОНКОВ ЧЕЛОВЕКА НА РЕНТГЕНОВСКИХ ИЗОБРАЖЕНИЯХ С ИСПОЛЬЗОВАНИЕМ DARKNET YOLO

Technology that allows to localize vertebrae on X-ray images and then determine geometric parameters using the OpenCV computer vision library using a convolutional neural network Darknet YOLO based on regions is proposed.Предлагается алгоритм, позволяющий с использованием сверточной нейронной сети на основе регионов Darknet YOLO осуществлять локализацию позвонков на рентгеновских изображениях с последующим определением геометрических параметров с помощью библиотеки компьютерного зрения OpenCV

Machine Learning towards General Medical Image Segmentation

The quality of patient care associated with diagnostic radiology is proportionate to a physician\u27s workload. Segmentation is a fundamental limiting precursor to diagnostic and therapeutic procedures. Advances in machine learning aims to increase diagnostic efficiency to replace single applications with generalized algorithms. We approached segmentation as a multitask shape regression problem, simultaneously predicting coordinates on an object\u27s contour while jointly capturing global shape information. Shape regression models inherent point correlations to recover ambiguous boundaries not supported by clear edges and region homogeneity. Its capabilities was investigated using multi-output support vector regression (MSVR) on head and neck (HaN) CT images. Subsequently, we incorporated multiplane and multimodality spinal images and presented the first deep learning multiapplication framework for shape regression, the holistic multitask regression network (HMR-Net). MSVR and HMR-Net\u27s performance were comparable or superior to state-of-the-art algorithms. Multiapplication frameworks bridges any technical knowledge gaps and increases workflow efficiency

Quantitative body shape analysis for obesity evaluation

Obesity is a public health concern as it is associated with a number of diseases, such as diabetes mellitus type 2, cardiovascular disease, some forms of renal failure and certain types of cancers. Growing evidence suggests that it is not only the amount of fat, but also its distribution in the body that is important to predict metabolic risk factors and adverse changes in organs. In this respect, it is necessary to develop convenient and inexpensive measures to characterize human body fat distribution and to investigate the unknown linkage between intrinsic adiposity and external body shape. This dissertation research aims to improve the obesity assessment by developing new quantitative measurements that comprehensively characterize body shape, and are highly relevant to intrinsic abdominal adiposity conditions. The proposed body shape descriptors were defined based on three-dimensional body images reconstructed from a custom-made stereovision body imaging system, which is particularly suitable for clinical use as an obesity monitoring equipment for its high portability and affordability. In this study, we developed a fully-automated algorithm to process T1-weighted magnetic resonance imaging (MRI) slices for abdominal adiposity measurements. This algorithm dramatically reduces the processing time and workload compared with traditional manual or semi-automatic methods for MRI processing, and greatly improves the repeatability and objectivity of fat assessments. A new obesity categorization method was then defined based on MRI adiposity data to depict characteristics of abdominal fat distribution, and the associations between the body shape descriptors and the MRI abdominal adiposity were explored. It was shown that the proposed body shape descriptors are able to capture the body shape differences between the subjects with dissimilar internal fat distribution (i.e., different categories), and to provide excellent prediction for the category of fat distribution through an optimized support-vector-machine classifier. The predictive models established in this dissertation demonstrate that the novel body shape descriptors were also effective for prediction of the volumes of abdominal visceral fat and subcutaneous fat accumulated in male and female adults. This dissertation introduces an innovative approach to assess obesity and fat distribution based on newly defined shape descriptors, and provides new findings that reveal the associations of intrinsic fat distribution with external body shapes, which enable both qualitative and quantitative assessment of obesity from body shape measurements.Biomedical Engineerin

Automatic Segmentation and Identification of Spinous Processes on Sagittal X-Rays Based on Random Forest Classification and Dedicated Contextual Features

X-ray based quantitative analysis of spine parameters is required in routine diagnosis or treatment planning. Existing tools commonly require manual intervention. Attempts towards automation of the whole procedure have mainly focused on vertebral bodies, whereas other regions such as the posterior arch also bear considerable amount of useful information. In this study, we combine a specific design of contextual visual features with a multi-class Random Forest classifier to perform pixel-wise segmentation and identification of all cervical spine spinous processes, on sagittal radiographs. Segmentations were evaluated on 62 radiographs, comparing to manual tracing. Correct identification was obtained for all subjects, and segmentation returned mean SD values of: Dice coefficient =88 8%; Hausdorff distance =2.1 1.4 mm and; mean surface distance =0.6 0.4 mm. The derived geometric parameters can be used to reduce the amount of manual intervention needed for spine modeling or to measure clinical indices

CT Scanning

Since its introduction in 1972, X-ray computed tomography (CT) has evolved into an essential diagnostic imaging tool for a continually increasing variety of clinical applications. The goal of this book was not simply to summarize currently available CT imaging techniques but also to provide clinical perspectives, advances in hybrid technologies, new applications other than medicine and an outlook on future developments. Major experts in this growing field contributed to this book, which is geared to radiologists, orthopedic surgeons, engineers, and clinical and basic researchers. We believe that CT scanning is an effective and essential tools in treatment planning, basic understanding of physiology, and and tackling the ever-increasing challenge of diagnosis in our society