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Publication Category: AI-based Analysis

Advances in cartilage imaging techniques

By Chondrometrics-admin

Articular cartilage is crucial for joint function; however, it has limited regenerative capacity when damaged, a hallmark of many rheumatic diseases. Non-invasive imaging is essential for early diagnosis, therapeutic monitoring and prognostication. MRI remains the reference standard, offering detailed assessment of both morphological and compositional cartilage changes. Technological advances, including high-resolution and compositional MRI techniques … Continued

Magnetic resonance imaging (MRI) transverse relaxation time (T2) for non-invasive assessment of cartilage composition

By Chondrometrics-admin

MRI has transformed medical imaging and research by enabling detailed 3D visualization of soft tissue anatomy. But it also provides insight into biophysical, histological, and mechanical properties via quantitative MRI relaxometry (qMRI). Cartilage transverse relaxation time (T2) has emerged as probably the most robust non-invasive marker of cartilage composition and histological assessment, and of cartilage … Continued

Clinical validation of fully automated (peri-)articular tissue analysis for assessing osteoarthritis progression: a narrative review

By Chondrometrics-admin

Numerous studies have presented fully automated techniques for assessing structural osteoarthritis (OA) progression, with recent work increasingly relying on deep learning (DL)-based methods. The objective of this narrative review was to summarize findings from studies comparing the validity of fully automated methods for assessing progression in (peri-) articular joint tissues with reference measures (e.g., manual … Continued

Multifaceted imaging strategies for clinical trials of knee osteoarthritis: a tightly interlinked value and precision chain

By Chondrometrics-admin

Osteoarthritis is a debilitating, whole-organ disease that involves all osteoarticular tissues. No effective treatments have yet been approved by the regulatory agencies for modifying the natural history of this disease and its structural progression. In this whitepaper, we will summarize concepts of “multi-faceted” (multi-contrast) magnetic resonance imaging (MRI), with a focus on its application in … Continued

Comparison between coronal FLASH and sagittal double echo steady state MRI in detecting longitudinal cartilage thickness change by fully automated segmentation – data from the FNIH biomarker cohort

By Chondrometrics-admin

Objective: Artificial intelligence (AI-) based automated cartilage analysis demonstrated similar sensitivity to change and only slighty inferior differentiation between radiographic progressors and non-progressors compared with manual segmentation. However, this finding was based on DESS MRI from the Osteoarthritis Initiative (OAI), whereas the vast majority of multicenter clinical trials rely on T1-weighted gradient echo (e.g. FLASH). Here … Continued

A fully-automated technique for cartilage morphometry in knees with severe radiographic osteoarthritis – method development and validation

By Chondrometrics-admin

Objective: Denuded areas of subchondral bone (dAB) pose a challenge for fully automated segmentation of articular cartilage and subchondral bone in knees with severe radiographic osteoarthritis using convolutional neural networks (CNNs). Here we propose an automated post-processing relying on a selection-based multi-atlas registration for reconstructing the total area of subchondral bone (tAB) to overcome this issue. … Continued

Clinical validation of fully automated cartilage transverse relaxation time (T2) and thickness analysis using quantitative DESS magnetic resonance imaging

By Chondrometrics-admin

Objective: To clinically validate a fully automated cartilage segmentation technique from quantitative double-echo steady-state (qDESS) MRI supporting simultaneous estimation of cartilage T2 and morphology. Here, we test whether laminar (superficial and deep layer) T2 results from convolutional neural network (CNN) segmentations are consistent with those from manual expert segmentations. Materials and methods: The 3D qDESS sequence was … Continued

Detection of differences in longitudinal cartilage thickness loss using a Deep-Learning automated segmentation algorithm: data from the foundation for the national institutes of health biomarkers study of the Osteoarthritis Initiative.

By Chondrometrics-admin

Objective: To study the longitudinal performance of fully automated cartilage segmentation in knees with radiographic osteoarthritis (OA), we evaluated the sensitivity to change in progressor knees from the Foundation for the National Institutes of H…

Accuracy and longitudinal reproducibility of quantitative femorotibial cartilage measures derived from automated U-Net-based segmentation of two different MRI contrasts: data from the Osteoarthritis Initiative healthy reference cohort.

By Chondrometrics-admin

Objective: To evaluate the agreement, accuracy, and longitudinal reproducibility of quantitative cartilage morphometry from 2D U-Net-based automated segmentations for 3T coronal fast low angle shot (corFLASH) and sagittal double echo at steady-state …