Classification of micro-CT images using 3D characterization of bone canal patterns in human osteogenesis imperfecta

Abidin, Anas Z.; Jameson, John R.; Molthen, Robert C.; Wismüller, Axel

doi:10.1117/12.2254421

Cited by 4 publications

(4 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With these cortical characteristics they successfully performed a machine learning task to distinguish between OI and healthy bone. They conclude that this technique could potentially be used as a future biomarker to detect and quantify the severity of OI, respectively ( 91 ).…”

Section: Resultsmentioning

confidence: 99%

A Clinical Perspective on Advanced Developments in Bone Biopsy Assessment in Rare Bone Disorders

et al. 2020

View full text Add to dashboard Cite

Introduction: Bone biopsies have been obtained for many centuries and are one of the oldest known medical procedures in history. Despite the introduction of new noninvasive radiographic imaging techniques and genetic analyses, bone biopsies are still valuable in the diagnosis of bone diseases. Advanced techniques for the assessment of bone quality in bone biopsies, which have emerged during the last decades, allows in-depth tissue analyses beyond structural changes visible in bone histology. In this review, we give an overview of the application and advantages of the advanced techniques for the analysis of bone biopsies in the clinical setting of various rare metabolic bone diseases. Method: A systematic literature search on rare metabolic bone diseases and analyzing techniques of bone biopsies was performed in PubMed up to 2019 week 34. Results: Advanced techniques for the analysis of bone biopsies were described for rare metabolic bone disorders including Paget's disease of bone, osteogenesis imperfecta, fibrous dysplasia, Fibrodysplasia ossificans progressiva, PLS3 X-linked osteoporosis, Loeys-Diets syndrome, osteopetrosis, Erdheim-Chester disease, and Cherubism. A variety of advanced available analytical techniques were identified that may help to provide additional detail on cellular, structural, and compositional characteristics in rare bone diseases complementing classical histopathology. Discussion: To date, these techniques have only been used in research and not in daily clinical practice. Clinical application of bone quality assessment techniques depends upon several aspects such as availability of the technique in hospitals, the existence of reference data, and a cooperative network of researchers and clinicians. The evaluation of rare metabolic bone disorders requires a repertoire of different methods, owing to their distinct bone tissue characteristics. The broader use of bone material obtained from biopsies could provide much more information about pathophysiology or treatment options and establish bone biopsies as a valuable tool in rare metabolic bone diseases.

show abstract

Section: Resultsmentioning

confidence: 99%

A Clinical Perspective on Advanced Developments in Bone Biopsy Assessment in Rare Bone Disorders

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Morphological MRI of PD patients, PSP patients, and healthy control subjects was used as the input of a supervised ML algorithm based on the combination of PCA as a feature extraction technique and SVM as a classification algorithm. The authors in [ 56 ] characterized the 3D structure of the cortical bone in high-resolution micro-CT images to analyze the micro-structural properties of bone in cases of osteogenesis imperfecta (OI), a genetic disorder of connective tissues caused by an abnormality in the synthesis or processing of collagen. Numerous features computed from the image were used in an SVM model to classify between healthy and OI bone.…”

Section: Ai Application In Rare Diseasesmentioning

confidence: 99%

The Impact of Artificial Intelligence in the Odyssey of Rare Diseases

et al. 2023

View full text Add to dashboard Cite

Emerging machine learning (ML) technologies have the potential to significantly improve the research and treatment of rare diseases, which constitute a vast set of diseases that affect a small proportion of the total population. Artificial Intelligence (AI) algorithms can help to quickly identify patterns and associations that would be difficult or impossible for human analysts to detect. Predictive modeling techniques, such as deep learning, have been used to forecast the progression of rare diseases, enabling the development of more targeted treatments. Moreover, AI has also shown promise in the field of drug development for rare diseases with the identification of subpopulations of patients who may be most likely to respond to a particular drug. This review aims to highlight the achievements of AI algorithms in the study of rare diseases in the past decade and advise researchers on which methods have proven to be most effective. The review will focus on specific rare diseases, as defined by a prevalence rate that does not exceed 1–9/100,000 on Orphanet, and will examine which AI methods have been most successful in their study. We believe this review can guide clinicians and researchers in the successful application of ML in rare diseases.

show abstract

“…Bone morphology has also been studied in patients suffering from OI [138], presenting BMD, trabecular volumetric BMD, total bone area, and cortical bone area lower in OI than in healthy age-and gender-matched controls [139]. Higher tissue mineral density was found for OI bone, a dramatically rise in cortical porosity, canal diameter, and connectivity [140] along with a lower elasticity [141]. That way, compared to control group, the cortical thickness seems to be thinner in OI patients [142].…”

Section: Bone Morphology In Osteogenesis Imperfectamentioning

confidence: 99%

Deciphering the Relevance of Bone ECM Signaling

Alcorta-Sevillano

Macías

Infante

et al. 2020

Cells

View full text Add to dashboard Cite

Bone mineral density, a bone matrix parameter frequently used to predict fracture risk, is not the only one to affect bone fragility. Other factors, including the extracellular matrix (ECM) composition and microarchitecture, are of paramount relevance in this process. The bone ECM is a noncellular three-dimensional structure secreted by cells into the extracellular space, which comprises inorganic and organic compounds. The main inorganic components of the ECM are calcium-deficient apatite and trace elements, while the organic ECM consists of collagen type I and noncollagenous proteins. Bone ECM dynamically interacts with osteoblasts and osteoclasts to regulate the formation of new bone during regeneration. Thus, the composition and structure of inorganic and organic bone matrix may directly affect bone quality. Moreover, proteins that compose ECM, beyond their structural role have other crucial biological functions, thanks to their ability to bind multiple interacting partners like other ECM proteins, growth factors, signal receptors and adhesion molecules. Thus, ECM proteins provide a complex network of biochemical and physiological signals. Herein, we summarize different ECM factors that are essential to bone strength besides, discussing how these parameters are altered in pathological conditions related with bone fragility.

show abstract

Classification of micro-CT images using 3D characterization of bone canal patterns in human osteogenesis imperfecta

Cited by 4 publications

References 25 publications

A Clinical Perspective on Advanced Developments in Bone Biopsy Assessment in Rare Bone Disorders

A Clinical Perspective on Advanced Developments in Bone Biopsy Assessment in Rare Bone Disorders

The Impact of Artificial Intelligence in the Odyssey of Rare Diseases

Deciphering the Relevance of Bone ECM Signaling

Contact Info

Product

Resources

About