A Framework on a Computer Assisted and Systematic Methodology for Detection of Chronic Lower Back Pain Using Artificial Intelligence and Computer Graphics Technologies

Abstract

Abstract. Back pain is one of the major musculoskeletal pain problems that can affect many people and is considered as one of the main causes of disability all over the world. Lower back pain, which is the most common type of back pain, is estimated to affect at least 60% to 80% of the adult population in the United Kingdom at some time in their lives. Some of those patients develop a more serious condition namely Chronic Lower Back Pain in which physicians must carry out a more involved diagnostic procedure to determine its cause. In most cases, this procedure involves a long and laborious task by the physicians to visually identify abnormalities from the patient's Magnetic Resonance Images. Limited technological advances have been made in the past decades to support this process. This paper presents a comprehensive literature review on these technological advances and presents a framework of a methodology for diagnosing and predicting Chronic Lower Back Pain. This framework will combine current state-of-the-art computing technologies including those in the area of artificial intelligence, physics modelling, and computer graphics, and is argued to be able to improve the diagnosis process.

Explainable AI (XAI) Applied in Machine Learning for Pain Modeling: A Review

“…Due to numerous advances in medical image processing and AI, physicians are now able to optimize the time for their diagnosis and treatment. Around 60% to 80% of the population in the UK may have experienced back pain at any time in their life [46], and among chronic diseases worldwide, one-fourth of the population suffers back pain. Advances in AI have reduced the risk by having a fast diagnosis system, with early prevention of acute back pain becoming chronic back pain.…”

“…Advances in AI have reduced the risk by having a fast diagnosis system, with early prevention of acute back pain becoming chronic back pain. Electromyography (EMG), heart-rate variability (HRV), pelvic incidence, pelvic tilt (Figure 1), lumbar lordosis angle, sacral slope, pelvic radius, degree spondylolisthesis, pelvic slope, direct tilt, thoracic slope, cervical tilt, sacrum angle and scoliosis slope, gait features, data from pressure sensors to assess sitting posture and erector spinae muscle activity are some explainable features for back pain diagnosis using AI [46][47][48][49][50][51].…”

Segmentation of Lumbar Spine MRI Images for Stenosis Detection Using Patch-Based Pixel Classification Neural Network

“…We will also employ appropriate post-processing algorithms on the resulting segmentation to make it more suitable for medical image analysis purposes. The general plan is to use PALMSNet in our framework on computer-assisted detection of chronic lower back pain which was detailed in our previous publication [37].…”

Lumbar Spine Discs Labeling Using Axial View MRI Based on the Pixels Coordinate and Gray Level Features

“…The paper reported that 91% accuracy is achieved in detecting the herniation. More details about the related research are available in our previous publication [6].…”

“…The focus of this paper is on labeling and localizing the disc area in the lumbar spine using the axial view MR images. In our previous work [6] we have developed a framework for detecting the disc herniation in the lumbar spine which require labeling the disc area to be able to detect the herniation. Currently diagnosing the lower back pain is done by a visual observation and analysis of the lumbar spine MR images and this process could take up much of a physician time and effort.…”