Orthopedic Imaging Research Articles

Volumetric Classification: Unveiling the True Extent of Rotator Cuff Tears

Rotator cuff injury diagnosis involves comprehensive clinical, physical, and imaging assessments, with MRI being pivotal for detecting and classifying these injuries. However, the absence of a universally accepted classification system necessitates a more precise approach, advocating for the use of three-dimensional (3D) modeling to better understand and categorize rotator cuff tears. This research was conducted as a prospective, single-institution study on 62 patients exhibiting full-thickness rotator cuff tears. Utilizing preoperative 1.5T MRI, the study aimed to create a more detailed classification system based on volumetric and surface area measurements. Advanced 3D modeling software was employed to transform MRI data into precise 3D representations, facilitating a more accurate analysis of the lesions. The study unveiled a novel classification system rooted in volumetric and surface area assessments, revealing significant discrepancies in the existing two-dimensional classifications. Approximately 45% of the cases demonstrated inconsistencies between traditional classifications and 3D measurements. Notably, medium-sized lesions were often overestimated, while small and large lesions were consistently underestimated in their severity. The volumetric and surface area-based classifications provided a more accurate depiction, highlighting the limitations of relying solely on coronal plane assessments in MRI. Comparative analysis confirmed the improved accuracy of the 3D method. The integration of 3D imaging and volumetric analysis offers novel advancement in diagnosing and classifying rotator cuff injuries. This study's findings challenge the conventional reliance on 2D MRI, proposing a more detailed and accurate classification system that enhances the precision of surgical planning and potentially improves patient outcomes. The incorporation of comprehensive 3D assessments into the diagnostic process represents a significant step forward in the orthopedic imaging field.

Deep Learning-Based Workflow for Bone Segmentation and 3D Modeling in Cone-Beam CT Orthopedic Imaging

In this study, a deep learning-based workflow designed for the segmentation and 3D modeling of bones in cone beam computed tomography (CBCT) orthopedic imaging is presented. This workflow uses a convolutional neural network (CNN), specifically a U-Net architecture, to perform precise bone segmentation even in challenging anatomical regions such as limbs, joints, and extremities, where bone boundaries are less distinct and densities are highly variable. The effectiveness of the proposed workflow was evaluated by comparing the generated 3D models against those obtained through other segmentation methods, including SegNet, binary thresholding, and graph cut algorithms. The accuracy of these models was quantitatively assessed using the Jaccard index, the Dice coefficient, and the Hausdorff distance metrics. The results indicate that the U-Net-based segmentation consistently outperforms other techniques, producing more accurate and reliable 3D bone models. The user interface developed for this workflow facilitates intuitive visualization and manipulation of the 3D models, enhancing the usability and effectiveness of the segmentation process in both clinical and research settings. The findings suggest that the proposed deep learning-based workflow holds significant potential for improving the accuracy of bone segmentation and the quality of 3D models derived from CBCT scans, contributing to better diagnostic and pre-surgical planning outcomes in orthopedic practice.

Fair AI-powered orthopedic image segmentation: addressing bias and promoting equitable healthcare

AI-powered segmentation of hip and knee bony anatomy has revolutionized orthopedics, transforming pre-operative planning and post-operative assessment. Despite the remarkable advancements in AI algorithms for medical imaging, the potential for biases inherent within these models remains largely unexplored. This study tackles these concerns by thoroughly re-examining AI-driven segmentation for hip and knee bony anatomy. While advanced imaging modalities like CT and MRI offer comprehensive views, plain radiographs (X-rays) predominate the standard initial clinical assessment due to their widespread availability, low cost, and rapid acquisition. Hence, we focused on plain radiographs to ensure the utilization of our contribution in diverse healthcare settings, including those with limited access to advanced imaging technologies. This work provides insights into the underlying causes of biases in AI-based knee and hip image segmentation through an extensive evaluation, presenting targeted mitigation strategies to alleviate biases related to sex, race, and age, using an automatic segmentation that is fair, impartial, and safe in the context of AI. Our contribution can enhance inclusivity, ethical practices, equity, and an unbiased healthcare environment with advanced clinical outcomes, aiding decision-making and osteoarthritis research. Furthermore, we have made all the codes and datasets publicly and freely accessible to promote open scientific research.

Six-Degree-of-Freedom Freehand 3D Ultrasound: A Low-Cost Computer Vision-Based Approach for Orthopedic Applications.

In orthopedics, X-rays and computed tomography (CT) scans play pivotal roles in diagnosing and treating bone pathologies. Machine bulkiness and the emission of ionizing radiation remain the main problems associated with these techniques. The accessibility and low risks related to ultrasound handling make it a popular 2D imaging method. Indeed, 3D ultrasound assembles 2D slices into a 3D volume. This study aimed to implement a probe-tracking method for 6 DoF 3D ultrasound. The proposed method involves a dodecahedron with ArUco markers attached, enabling computer vision tracking of the ultrasound probe's position and orientation. The algorithm focuses on the data acquisition phase but covers the basic reconstruction required for data generation and analysis. In the best case, the analysis revealed an average error norm of 2.858 mm with a standard deviation norm of 5.534 mm compared to an infrared optical tracking system used as a reference. This study demonstrates the feasibility of performing volumetric imaging without ionizing radiation or bulky systems. This marker-based approach shows promise for enhancing orthopedic imaging, providing a more accessible imaging modality for helping clinicians to diagnose pathologies regarding complex joints, such as the shoulder, replacing standard infrared tracking systems known to suffer from marker occlusion problems.

Orthopedic Image Segmentation and Recognition Based on Deep Neural Networks

In the field of medicine, especially orthopedics, accurate diagnosis is crucial. However, due to the uneven distribution of medical resources and the continuous growth of patient numbers, the diagnostic efficiency of hospitals has been significantly affected, and the problems of misdiagnosis and missed diagnosis are becoming increasingly prominent. To address these issues, this study explores a deep neural network-based orthopedic image segmentation and recognition model, aiming to improve the accuracy and efficiency of diagnosis. Firstly, we use U-Net neural network for segmentation training of orthopedic images in order to extract detailed features from the images.

Diagnostic challenges in orthopedic imaging: Uncommon periprosthetic fracture of the left hip

Periprosthetic fractures are a well-recognized complication of joint arthroplasty, with an increased risk in the long-term follow-up of joint replacements.

Imaging in pediatric traumatology and orthopedics

Conventional or digital radiography is still the basis of imaging diagnostics of the skeletal system in pediatric patients. It is considered the gold standard for diagnosis, treatment selection, and follow-up. In addition, procedures such as ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), and also nuclear medicine techniques can and should be used. It is advantageous to use trained radiology technicians who are familiar with the handling of children in X‑ray diagnostics. If there is no dedicated pediatric radiology department, it is recommended to follow the guidelines from radiology societies (as low as reasonably achievable [ALARA]) and radiation protection commissions. The present article describes how state-of-the-art tools such as dose monitoring systems and software-controlled image processing and also postprocessing can be used. The article provides information on how the various modalities can be optimally used in order to achieve the best result, i.e., diagnosis, with the least possible effort and burden for the child.

Investigating acquisition of magnetic properties and biocompatibility of Gadolinium substituted Strontium phosphosilicate for magnetic imaging in orthopaedics

The ability to acquire in situ magnetic resonance properties in biological apatites to facilitate real time MRI was investigated by substituting a series of hierarchically increasing Gadolinium over Strontium in the study. Samples were synthesized using an aqueous sol–gel pathway to ensure maximum reactivity and favour Silica-based species. Structural features were investigated using diffraction and IR spectra followed by VSM to calculate magnetization as a function of the field revealing the paramagnetic behaviour of the samples when doped. Morphological features visualized through FE-SEM showed sustained structural integrity of parent material characteristic of the P63/M space group and the EDAX spectra confirmed the hypothesised substitution when sintered as well. When immersed with SBF a loss of secondary phases and formation of amorphous calcium phosphate flakes were confirmed by diffraction and morphological studies. Biocompatibility was evaluated against first contact cells i.e. RBC via haemolytic study and L929 cells wherein Alamar assay show the samples least toxic at 25 μg/ml beyond which cell viability decreases providing enough evidence of applicability of Strontium phosphosilicate as a bioactive ceramic.

Intelligent Diagnosis Method for Orthopedic Diseases Based on Medical Images

<p>This article proposes an orthopedic image recognition method based on an improved convolutional neural network to address the traditional diagnostic methods of orthopedic diseases and the diverse types of orthopedic diseases in the diagnostic process. This method uses a fixed convolutional number to extract key features of orthopedic diseases and reduce the number of features. The article takes the diagnosis of gout in bone diseases as an example and designs an evaluation method based on visualization technology and quantitative indicator calculation. The quantitative indicator calculation obtains the total volume information of urate crystals, thereby assisting doctors in gout diagnosis. The experimental results show that the diagnostic accuracy reaches 98.7%, which can meet the actual diagnostic requirements.</p> <p> </p>

Current techniques of 3D reconstruction in computed tomography and magnetic resonance in diagnostic imaging of the spine

This review article presents an analysis of 36 scientific papers focusing on modern three-dimensional (3D) reconstruction techniques in computed tomography (CT ) and magnetic resonance imaging (MRI) for their applications in medical diagnostics. The objective of this review is to present the current state of knowledge regarding the development and utilization of 3D reconstruction techniques, as well as to identify key trends and challenges in this field. The first part of the study focuses on the advancements in MRI and CT . The analysis reveals the major trends in the evolution of these diagnostic methods, such as increased accessibility of CT and MRI examinations for patients, reduced scan duration, greater utilization of artificial intelligence, and expanded applications in interventional radiology.The second part of the article highlights the potential and effectiveness of 3D modelling in diagnostic imaging. Creating 3D models of anatomical structures is a complex and multi-step process. Through the review, it was determined that 3D models derived from MRI can be equally accurate and diagnostically valuable compared to the more commonly used CT -based reconstructions. In the future, fusion imaging of MRI/CT is expected to play an increasingly significant role in orthopaedic imaging. The review demonstrates the significant potential of 3D modelling in diagnostic imaging. However, further research is still required to better understand the capabilities of 3D modelling in diagnosing complex anatomical structures. The integration of information technology in medicine will be crucial in advancing this field.

Seven general radiography x-ray detectors with pixel sizes ranging from 175 to 76 μm: technical evaluation with the focus on orthopaedic imaging

Aim. Flat panel detectors with small pixel sizes general can potentially improve imaging performance in radiography applications requiring fine detail resolution. This study evaluated the imaging performance of seven detectors, covering a wide range of pixel sizes, in the frame of orthopaedic applications. Material and methods. Pixel sizes ranged from 175 (detector A175) to 76 μm (detector G76). Modulation transfer function (MTF) and detective quantum efficiency (DQE) were measured using International Electrotechnical Commission (IEC) RQA3 beam quality. Threshold contrast (C T) and a detectability index (d′) were measured at three air kerma/image levels. Rabbit shoulder images acquired at 60 kV, over five air kerma levels, were evaluated in a visual grading study for anatomical sharpness, image noise and overall diagnostic image quality by four radiologists. The detectors were compared to detector E124. Results. The 10% point of the MTF ranged from 3.21 to 4.80 mm−1, in going from detector A175 to detector G76. DQE(0.5 mm−1) measured at 2.38 μGy/image was 0.50 ± 0.05 for six detectors, but was higher for F100 at 0.62. High frequency DQE was superior for the smaller pixel detectors, however C T for 0.25 mm discs correlated best with DQE(0.5 mm−1). Correlation between C T and the detectability model was good (R 2 = 0.964). C T for 0.25 mm diameter discs was significantly higher for D150 and F100 compared to E124. The visual grading data revealed higher image quality ratings for detectors D125 and F100 compared to E124. An increase in air kerma was associated with improved perceived sharpness and overall quality score, independent of detector. Detectors B150, D125, F100 and G76, performed well in specific tests, however only F100 consistently outperformed the reference detector. Conclusion. Pixel size alone was not a reliable predictor of small detail detectability or even perceived sharpness in a visual grading analysis study.

A Novel Tool for Collaborative and Blinded Orthopedic Image Analysis.

Image analysis plays a central role in orthopedics and research but comes with many challenges, including anonymization, bias, and achieving efficient analyses using multiple independent observers. Appropriate software is still lacking. Tyche is a free online tool that displays images in a random order without showing any metadata. Additionally, when using Tyche, observers can store results in the same window, and the final results are immediately visible to the project manager. In this study, we compared results from Tyche with those from a validated tool. One hundred pelvic radiographs were analyzed separately by five orthopedic surgeons using both Tyche and the validated software. Common orthopedic measurement modalities and scores were determined. The methods were compared using intra-class correlations and Fleiss' kappa coefficients as well as Bland-Altman plots. Significant correlations ranging from r = 0.17 (Kallgren and Lawrence Score) to r = 0.99 (area measurements) were calculated for inter- and intraobserver agreements between the two tools for all measurements. The Bland-Altman plots indicated the non-inferiority of either tool. The images were analyzed significantly faster when Tyche was used. We conclude that Tyche is a valid tool for use in orthopedic image analysis. Tyche could be utilized for determining inter- and intraobserver agreements, in multicenter studies and for score validations.

Orthopedic Injury Diagnosis Using Digital Radiography: A Retrospective Study

Background: Hundred patients were examined to determine the effectiveness of digital radiography in detecting Orthopedic injuries. The decisive findings showed that digital radiography was quite efficient, with a 91% diagnostic success rate. As a result, digital radiography is an effective and cost-effective method for identifying Orthopedic injuries. Aim: The comparison of digital radiography with other Orthopedic imaging modalities is intended to ascertain how well it can diagnose injuries and how much it costs. figuring out the factors that determine how accurate it is at detecting Orthopedic injuries. Methods: We did a retrospective analysis at the radiology department of the Khyber Teaching Hospital in Peshawar from Feb 2016 to Feb 2017. The medical files and radiographic pictures of hundred individuals who underwent digital radiography for orthopedic injuries were the subject of our study. In the course of our investigation, we also gathered demographic information including age and sex. Two radiologists separately analysed the radiography pictures to get the most reliable findings. The agreement between the final diagnoses provided by the attending Orthopedic surgeon and the radiologists was our definition of accurate diagnosis. Finally, we evaluated how much the digital radiography would cost. Results: Comparing digital radiography to more expensive imaging techniques like CT and MRI revealed considerable cost reductions. [91%] of the patients received the right diagnosis after the use of digital radiography. Results were inconclusive or incorrect for the remaining [9%]. Conclusion: This research found that the diagnosis of Orthopedic injuries using digital radiography was both economical and accurate. The accuracy rate was satisfactory, with patient correct diagnostic rates of[ 91%]. In addition, compared to other imaging methods, digital radiography was much less expensive. Keywords: Orthopedic injuries, diagnosis, accuracy, cost-effectiveness, digital radiography

What to Teach in Small Animal Veterinary Orthopedics: A Survey of Practicing Veterinarians to Inform Curriculum Development.

Competency-based veterinary education focuses on the knowledge and clinical skills required to generate a productive and confident practitioner. Accurate identification of clinically relevant core competencies enables academic institutions to prioritize which new and foundational information to cover in the limited time available. The goal of this study was to aggregate the opinions of veterinary practitioners about small animal core competencies in veterinary orthopedics. An online 20-question survey was distributed with questions regarding respondent demographics, education, practice type, caseload, involvement in orthopedic procedures, access to referral hospitals, frequency of orthopedic condition presentation and procedure performance, and proposed percent allocation of various orthopedic curriculum topics. Responses were included from 721 respondents, largely first-opinion veterinarians (81%, n = 580/721). The majority (58%; n = 418/721) of respondents performed less than 10% of the orthopedic surgeries themselves and, 37% (n = 266/721) reported never performing orthopedic surgery; of those performing surgeries, 78% (n = 354/455) performed less than six orthopedic procedures monthly. The five most common orthopedic conditions seen included generalized osteoarthritis, patellar luxation, cranial cruciate ligament disease, hip dysplasia/arthritis, and muscle/tendon injuries. Median respondent scores for the percentage that a topic should compose in an ideal orthopedic curriculum were 20% each for "orthopedic exam" and for "non-surgical orthopedic knowledge," 15% each for "non-surgical orthopedic skills," "orthopedic imaging (radiographs)," and "surgical orthopedic knowledge," 10% for "surgical orthopedic skills," and 2% for "advanced orthopedic imaging." Based on these results, a curriculum focusing on the most clinically relevant orthopedic conditions with an emphasis on diagnosis establishment and non-surgical treatments is proposed.

ORTHO AI : The Dawn Of A New Era: Artificial Intelligence In Orthopaedics

ORTHO AI : The Dawn Of A New Era: Artificial Intelligence In Orthopaedics

Artificial intelligence in orthopaedic and trauma surgery imaging

Artificial intelligence (AI) is playing an increasing role in radiological imaging in orthopaedics and trauma surgery. The algorithms available to date are predominantly used in the detection of (occult) fractures and in length and angle measurements in conventional X‑ray images. However, current AI solutions also enable the analysis and pattern recognition of CT datasets, e.g. in the detection of rib or vertebral body fractures. Aspecial application is EOS™ (ATEC Spine Group, Paris, France), which enables a3‑D simulation of the axial skeleton and semi-automatic length and angle calculations based on adigital 2‑D X‑ray image. In this paper, the current spectrum of AI applications for orthopaedics and trauma surgery is presented and discussed.

CNN Attention Guidance for Improved Orthopedics Radiographic Fracture Classification.

Convolutional neural networks (CNNs) have gained significant popularity in orthopedic imaging in recent years due to their ability to solve fracture classification problems. A common criticism of CNNs is their opaque learning and reasoning process, making it difficult to trust machine diagnosis and the subsequent adoption of such algorithms in clinical setting. This is especially true when the CNN is trained with limited amount of medical data, which is a common issue as curating sufficiently large amount of annotated medical imaging data is a long and costly process. While interest has been devoted to explaining CNN learnt knowledge by visualizing network attention, the utilization of the visualized attention to improve network learning has been rarely investigated. This paper explores the effectiveness of regularizing CNN network with human-provided attention guidance on where in the image the network should look for answering clues. On two orthopedics radiographic fracture classification datasets, through extensive experiments we demonstrate that explicit human-guided attention indeed can direct correct network attention and consequently significantly improve classification performance. The development code for the proposed attention guidance is publicly available on https://github.com/zhibinliao89/fracture_attention_guidance.

Small animal Review

Introduction: Computed tomography is an advanced imaging modality, giving detailed three-dimensional images with good differentiation between tissue densities, useful for orthopaedic and soft tissue imaging. The use of contrast improves its accuracy when imaging soft tissues and allows angiography. Computed tomography has become much more widely available in practice in the UK in recent years, with most referral centres and some larger primary practices now boasting their own facilities, often used by other practices when referring cases for outpatient scans. Studies regarding the use of computed tomography are thus becoming more relevant to the general practitioner as well as the referral clinician, and this month's Small Animal Review presents three studies on its use in different conditions.

Gray scale inversion imaging (GSI) in Trauma and Orthopaedics

Radiology plays a crucial part in the diagnosis and management of patients. Several techniques have been used to decrease the diagnostic error rate. We discuss the concept, use and advantages of grayscale inversion imaging in orthopedics.

Effect of Weight Bearing Variations & Heel Height on Forefoot Width for Orthopaedic Imaging

Category:Basic Sciences/Biologics; Midfoot/ForefootIntroduction/Purpose:The biomechanics of a foot when weight bearing change significantly due to the changes in the function through the gait cycle. Variations in heel height are known to influence load distribution in weightbearing, thus affecting change in forefoot widths. Weight bearing in radiographs are commonly used to identify these changes, which may have clinical relevance. However, there are limited studies that accurately quantify the extent of these differences in the forefoot to be analysed effectively. Hence, there is a need to investigate different weight bearing conditions and its influence on forefoot width changes required for accurate analysis of imaging. This study's aim is to evaluate the significance and degree of foot splaying with different modes of weight-bearing and heel height to achieve optimum increase in forefoot width.Methods:32 volunteers with no clinical issues with their feet were selected for this study. Weight bearing photographs of their feet were reviewed by 2 foot and ankle surgeons (under blinded conditions) to ensure they were normal morphologically. The forefoot width is measured using sliding callipers between the medial margin of the first metatarsal head and lateral margin of the fifth metatarsal head when non weight bearing - NWB, partial weight bearing - PWB (dual leg stance) and full weight bearing - FWB (single leg stance). Single leg stance measurement is also taken at increasing heel heights of 2cm (FWB1), 4cm (FWB2) and 6cm (FWB3) using polyurethrane blocks. Each individual foot non weight-bearing width will act as its own control for the changes in the differing positions measured.Results:Statistical analysis of data demonstrated the results with the use of student t-test analysis via SPSS which showed statistically significant differences in forefoot width in both genders(p<0.05). There is an increasing trend of forefoot width as weightbearing conditions increase up to 2cm of elevated heel height. However, forefoot width decreases as heel height increases beyond 2cm in both genders. Female subjects have a higher trend of percentage of increase in mean forefoot width compared to males in all weightbearing conditions except for full weightbearing with 6cm heel height elevation.Conclusion:In conclusion, there is a linear trend of increase in forefoot width with increase from non-weightbearing up to 2cm heel height inclination. Full weightbearing with 2cm heel height inclination produces the widest foot splaying in this study. Larger future studies will need to be conducted to validate these findings.