A Review on Imaging Techniques and Artificial Intelligence Models for Osteoporosis Prediction

Osteoporosis causes harmful influences on both men and women of all races. Bone mass, also referred to as "bone density," is frequently used to assess the health of bone. Humans frequently experience bone fractures as a result of trauma, accidents, metabolic bone diseases, and disorders of bone strength, which are typically led by changes in mineral composition and result in conditions like osteoporosis, osteoarthritis, osteopenia, etc. Artificial intelligence holds a lot of promise for the healthcare system. Data collection and preprocessing seem to be more essential for analysis, so bone images from different modalities, such as X-ray, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI), are taken into consideration that help to recognize, classify, and evaluate the patterns in clinical images. This research presents a comprehensive overview of the performance of various image processing techniques and deep learning approaches used to predict osteoporosis through image segmentation, classification, and fault detection. This survey outlined the proposed domain-based deep learning model for image classification in addition to the initial findings. The outcome identifies the flaws in the existing literature's methodology and lays the way for future work in the deep learning-based image analysis model.

Machine Learning Based Osteoarthritis Detection Methods in Different Imaging Modalities: A Review

Osteoarthritis (OA) is a bone disease that mainly affects the cartilage. Even though there are many diseases that are commonly noticed in bones, one of the most dangerous diseases is OA. OA occurs due to the destruction of the cartilage bone. According to the survey given by the National Institute on Aging, it is revealed that most of the people in their old age are at the very advanced stage of OA. X-ray is the common imaging modality for analysing the severity of Osteoarthritis. When needed for advanced level of investigation, MRI scans and thermal images are also initialized. There are numerous methods for the analysis of OA from different modalities in the very early stage. These methods may be semi-automatic and automatic. But all the developed algorithms gave results based on the space width, and texture feature only and didn't provide any quantitative analysis based on any standard parameters. The main aim of this work is to present major research challenges in different OA detection methods, discuss different machine learning-based OA detection methods and analyse their performance. The research gap in the existing methods such as an empirical model for the detection of OA and the standard parameters for the measurement of bone marrow is discussed in the proposed paper.

Diagnosis of Osteoporosis using modified U-net architecture with attention unit in DEXA and X-ray images

BACKGROUND

Osteoporosis, a silent killing disease of fracture risk, is normally determined based on the bone mineral density (BMD) and T-score values measured in bone. However, development of standard algorithms for accurate segmentation and BMD measurement from X-ray images is a challenge in the medical field.

OBJECTIVE

The purpose of this work is to more accurately measure BMD from X-ray images, which can overcome the limitations of the current standard technique to measure BMD using Dual Energy X-ray Absorptiometry (DEXA) such as non-availability and inaccessibility of DEXA machines in developing countries. In addition, this work also attempts to analyze the DEXA scan images for better segmentation and measurement of BMD.

METHODS

This work employs a modified U-Net with Attention unit for accurate segmentation of bone region from X-Ray and DEXA images. A linear regression model is developed to compute BMD and T-score. Based on the value of T-score, the images are then classified as normal, osteopenia or osteoporosis.

RESULTS

The proposed network is experimented with the two internally collected datasets namely, DEXSIT and XSITRAY, comprised of DEXA and X-ray images, respectively. The proposed method achieved an accuracy of 88% on both datasets. The Dice score on DEXSIT and XSITRAY is 0.94 and 0.92, respectively.

CONCLUSION

Our modified U-Net with attention unit achieves significantly higher results in terms of Dice score and classification accuracy. The computed BMD and T-score values of the proposed method are also compared with the respective clinical reports for validation. Hence, using the digitized X-Ray images can be used to detect osteoporosis efficiently and accurately.