Automatic Characterizations of Lumbar Multifidus Muscle and Intramuscular Fat with Fuzzy C-means based Quantization from Ultrasound Images

The Application of Image Texture Analysis Techniques on the Effects of Dry Needling versus Placebo in Low-Back Pain Patients: A Pilot-Study

Low back pain is the leading cause of disability in the world, with a significant socio-economic impact. Deep dry needling is effective in the treatment of this pain, and it is one of the techniques preferred by physiotherapists. In this field, the use of ultrasound provides information of interest such as length, thickness, diameter, cross-sectional area, or muscle volume, among others. Objective: To find out whether the tissue changes (thickness, histogram, and contraction rate) that occur in the lumbar multifidus after application of the deep dry needle are related to changes in the pain and the disability of the patient. Design: Randomized, double-blind, parallel-group clinical trial. Setting: University of Alcalá, Department of Physiotherapy. Subjects: 21 voluntary patients (women and men) with non-specific low-back pain aged 18–65 years. Intervention: Patients were randomly divided into two groups. One group received dry needling and the other group a dry needling placebo. Initial post-needling and one week post-needling assessments were performed by a therapist blinded to the intervention. Variables: Lumbar multifidus thickness measured by RUSI, contraction time measured by M-mode, histograms measured by image analysis, muscle area, pain measured by VAS, pressure pain threshold measured by pressure algometer, and disability measured by Roland–Morris questionnaire. Conclusions: The contraction speed, resting thickness, and pain demonstrated significant differences within each group, but not between groups. There were significant differences in contraction ratio and in PPT between groups. There was excellent intra-examiner reliability in image collection for histogram analysis. Histogram analysis showed no significant differences between groups and measurements, neither for the parameters nor for the parameters combined with the outcome variables. A robust method for the image texture analyses in future histogram muscle analyses has been performed.

Intelligent Automatic Segmentation of Wrist Ganglion Cysts Using DBSCAN and Fuzzy C-Means

Ganglion cysts are common soft tissue masses of the hand and wrist, and small size cysts are often hypoechoic. Thus, identifying them from ultrasonography is not an easy problem. In this paper, we propose an automatic segmentation method using two artificial intelligence algorithms in sequence. A density based unsupervised learning algorithm called DBSCAN is performed as a front-end and its result determines the number of clusters used in the Fuzzy C-Means (FCM) clustering algorithm for quantification of ganglion cyst object. In an experiment using 120 images, the proposed method shows a higher extraction rate (89.2%) and lower false positive rate compared with FCM when the ground truth is set as the human expert's decision. Such human-like behavior is more apparent when the size of the ganglion cyst is small that the quality of ultrasonography is often not very high. With this fully automatic segmentation method, the operator subjectivity that is highly dependent on the experience of the ultrasound examiner can be mitigated with high reliability.

Artificial Intelligence and Computer Vision in Low Back Pain: A Systematic Review

Chronic Low Back Pain (LBP) is a symptom that may be caused by several diseases, and it is currently the leading cause of disability worldwide. The increased amount of digital images in orthopaedics has led to the development of methods related to artificial intelligence, and to computer vision in particular, which aim to improve diagnosis and treatment of LBP. In this manuscript, we have systematically reviewed the available literature on the use of computer vision in the diagnosis and treatment of LBP. A systematic research of PubMed electronic database was performed. The search strategy was set as the combinations of the following keywords: "Artificial Intelligence", "Feature Extraction", "Segmentation", "Computer Vision", "Machine Learning", "Deep Learning", "Neural Network", "Low Back Pain", "Lumbar". Results: The search returned a total of 558 articles. After careful evaluation of the abstracts, 358 were excluded, whereas 124 papers were excluded after full-text examination, taking the number of eligible articles to 76. The main applications of computer vision in LBP include feature extraction and segmentation, which are usually followed by further tasks. Most recent methods use deep learning models rather than digital image processing techniques. The best performing methods for segmentation of vertebrae, intervertebral discs, spinal canal and lumbar muscles achieve Sørensen-Dice scores greater than 90%, whereas studies focusing on localization and identification of structures collectively showed an accuracy greater than 80%. Future advances in artificial intelligence are expected to increase systems' autonomy and reliability, thus providing even more effective tools for the diagnosis and treatment of LBP.