Bone Fracture Detection Research Articles

DNG Metamaterial-Inspired Slotted Stub Antenna With Enhanced Gain, Efficiency, and Distributed Current for Early Stage Bone Fracture Detection Applications

DNG Metamaterial-Inspired Slotted Stub Antenna With Enhanced Gain, Efficiency, and Distributed Current for Early Stage Bone Fracture Detection Applications

Remote Assistance for Bone-Fractured Patients using Deep Learning Models

ABSTRACT Remote diagnosis enables healthcare professionals to evaluate and diagnose patients from a distance using telecommunication technologies, enhancing healthcare delivery by improving accessibility, especially for those in remote or underserved areas. One of the significant sustainability challenges in remote medical diagnostics is offering timely assistance to vulnerable groups like the elderly, disabled, mentally impaired individuals, and wounded military personnel in combat zones. This becomes particularly difficult in emergencies when rapid analysis of medical records is needed, especially if the data is stored on secure blockchain networks. The proposed work addresses these challenges by deploying a comprehensive framework for large-scale analysis, utilizing both document and image classification for dual validation. It integrates advanced techniques such as Inception V3, VGG-16, VGG-19, RESNET-50, and Densenet-201 for bone fracture detection, with Inception V3 achieving the highest accuracy of 95.1%. In addition, a Document Classification Analysis (DCA) method is proposed, which automatically classifies the severity of fractures. Object detection techniques are also introduced for detecting minor fractures using region-based image segmentation, ensuring precise diagnosis even for subtle injuries. This pioneering integration of technologies provides a holistic solution for remote medical diagnostics.

The Feasibility Study of Bone Fracture Assessment Based on Photoacoustic Time-of-Flight Method

Abstract Photoacoustic imaging (PAI) technology combines the advantages of acoustic resolution and optical contrast imaging within deep tissue. The commonly clinically used bone fracture detection and evaluation method is X-ray based imaging techniques. In order to study the feasibility of human radius fractures detection in non-radiation and non-invasive way, this study combined the time-of-flight (TOF) method commonly used in industrial nondestructive testing with photoacoustic technology for the detection and location of bone fractures. Firstly, the bone fracture information carried by PA signal was studied by simulating the bone models with different locations of bone fractures. The finite difference time domain (FDTD) method was used to simulate the propagation process of photoacoustic signal in bone tissue. Secondly, the PATOF diagrams formed by PA signals at different locations is obtained by using laser fixed and ultrasonic detector motion detection method. Finally, the PATOF diagrams are further analyzed and quantified. The results show that the photoacoustic bone detection method based on TOF method can effectively locate the fracture location. This study confirms the feasibility of combining PAI technique with TOF method to detect bone fractures, which indicates that PAI technique has great application prospect in bone detection field.

Artificial Intelligence-Based Applications for Bone Fracture Detection Using Medical Images: A Systematic Review.

Artificial intelligence (AI) is making notable advancements in the medical field, particularly in bone fracture detection. This systematic review compiles and assesses existing research on AI applications aimed at identifying bone fractures through medical imaging, encompassing studies from 2010 to 2023. It evaluates the performance of various AI models, such as convolutional neural networks (CNNs), in diagnosing bone fractures, highlighting their superior accuracy, sensitivity, and specificity compared to traditional diagnostic methods. Furthermore, the review explores the integration of advanced imaging techniques like 3D CT and MRI with AI algorithms, which has led to enhanced diagnostic accuracy and improved patient outcomes. The potential of Generative AI and Large Language Models (LLMs), such as OpenAI's GPT, to enhance diagnostic processes through synthetic data generation, comprehensive report creation, and clinical scenario simulation is also discussed. The review underscores the transformative impact of AI on diagnostic workflows and patient care, while also identifying research gaps and suggesting future research directions to enhance data quality, model robustness, and ethical considerations.

Very fast, high-resolution aggregation 3D detection CAM to quickly and accurately find facial fracture areas

Background and objective:The incidence of facial fractures is on the rise globally, yet limited studies are addressing the diverse forms of facial fractures present in 3D images. In particular, due to the nature of the facial fracture, the direction in which the bone fractures vary, and there is no clear outline, it is difficult to determine the exact location of the fracture in 2D images. Thus, 3D image analysis is required to find the exact fracture area, but it needs heavy computational complexity and expensive pixel-wise labeling for supervised learning. In this study, we tackle the problem of reducing the computational burden and increasing the accuracy of fracture localization by using a weakly-supervised object localization without pixel-wise labeling in a 3D image space. Methods:We propose a Very Fast, High-Resolution Aggregation 3D Detection CAM (VFHA-CAM) model, which can detect various facial fractures. To better detect tiny fractures, our model uses high-resolution feature maps and employs Ablation CAM to find an exact fracture location without pixel-wise labeling, where we use a rough fracture image detected with 3D box-wise labeling. To this end, we extract important features and use only essential features to reduce the computational complexity in 3D image space. Results:Experimental findings demonstrate that VFHA-CAM surpasses state-of-the-art 2D detection methods by up to 20% in sensitivity/person and specificity/person, achieving sensitivity/person and specificity/person scores of 87% and 85%, respectively. In addition, Our VFHA-CAM reduces location analysis time to 76 s without performance degradation compared to a simple Ablation CAM method that takes more than 20 min. Conclusion:This study introduces a novel weakly-supervised object localization approach for bone fracture detection in 3D facial images. The proposed method employs a 3D detection model, which helps detect various forms of facial bone fractures accurately. The CAM algorithm adopted for fracture area segmentation within a 3D fracture detection box is key in quickly informing medical staff of the exact location of a facial bone fracture in a weakly-supervised object localization. In addition, we provide 3D visualization so that even non-experts unfamiliar with 3D CT images can identify the fracture status and location.

Diagnostic performance of an AI algorithm for the detection of appendicular bone fractures in pediatric patients

PurposeTo evaluate the diagnostic performance of an Artificial Intelligence (AI) algorithm, previously trained using both adult and pediatric patients, for the detection of acute appendicular fractures in the pediatric population on conventional X-ray radiography (CXR). Materials and methodsIn this retrospective study, anonymized extremities CXRs of pediatric patients (age <17 years), with or without fractures, were included. Six hundred CXRs (maintaining the positive-for-fracture and negative-for-fracture balance) were included, grouping them per body part (shoulder/clavicle, elbow/upper arm, hand/wrist, leg/knee, foot/ankle). Follow-up CXRs and/or second-level imaging were considered as reference standard. A deep learning algorithm interpreted CXRs for fracture detection on a per-patient, per-radiograph, and per-location level, and its diagnostic performance values were compared with the reference standard. AI diagnostic performance was computed by using cross-tables, and 95 % confidence intervals [CIs] were obtained by bootstrapping. ResultsThe final cohort included 312 male and 288 female with a mean age of 8.9±4.5 years. Three undred CXRs (50 %) were positive for fractures, according to the reference standard. For all fractures, the AI tool showed a per-patient 91.3% (95%CIs = 87.6–94.3) sensitivity, 76.7% (71.5-81.3) specificity, and 84% (82.1–86.0) accuracy. In the per-radiograph analysis the AI tool showed 85% (81.9–87.8) sensitivity, 88.5% (86.3–90.4) specificity, and 87.2% (85.7–89.6) accuracy. In the per-location analysis, the AI tool identified 606 bounding boxes: 472 (77.9 %) were correct, 110 (18.1 %) incorrect, and 24 (4.0 %) were not-overlapping. ConclusionThe AI algorithm provides good overall diagnostic performance for detecting appendicular fractures in pediatric patients.

A real-time human bone fracture detection and classification from multi-modal images using deep learning technique

A real-time human bone fracture detection and classification from multi-modal images using deep learning technique

Wrist fracture detection using self-supervised learning methodology

Objectives: This study aimed to assist radiologists in faster and more accurate diagnosis by automating bone fracture detection in pediatric trauma wrist radiographic images using self-supervised learning. This addresses data labeling challenges associated with traditional deep learning models in medical imaging. Methods: In this study, we trained the model backbone for feature extraction. Then, we used this backbone to train a complete classification model for classifying images as fracture or non-fracture on the publically available Kaggle and GRAZPERDWRI-DX dataset using ResNet-18 in pediatric wrist radiographs. Results: The resulting output revealed that the model was able to detect fracture and non-fracture images with 94.10% accuracy, 93.21% specificity, and an area under the receiver operating characteristics of 94.12%. Conclusion: This self-supervised model showed a promising approach and paved the way for efficient and accurate fracture detection, ultimately enhancing radiological diagnosis without relying on extensive labeled data.

Transfer Learning Empowered Bone Fracture Detection

Detection of bone fractures using modern technology has significant implications in medical analysis and artificial intelligence. This importance is especially pronounced in the realm of deep learning. Deep learning techniques find extensive application in the field of medicine and disease classification. The early identification of bone fractures is crucial for efficient treatment planning and patient care. Our research proposes a transfer learning-based model for predicting bone fractures using a dataset of bone X-ray images. These images will be classified into two categories: normal and bone fracture, based on extracted features. Our proposed model, the Bone Fracture Detection Transfer Learning Algorithm (BFDTLA), achieved an average accuracy of 97% on the dataset. The BFDTLA model demonstrated superior performance when compared to previous quantitative and qualitative research studies. This research focuses on the early detection of bone fractures using transfer learning algorithms, emphasizing the significance of accurate and timely diagnosis.

A Deep Learning Framework for Timely Bone Fracture Detection and Prevention

Accurate bone fracture identification remains critical in medical diagnostics, motivating researchers to investigate deep-learning systems to address this difficulty. The performance of convolutional neural networks (CNN) and region-based convolutional neural networks (RCNN) in fracture diagnosis is investigated in this paper. Algorithmic efficacy was investigated using a complete set of experiments involving various epochs, batch sizes, and optimization techniques (Adam and SGD). As discussed in the discussion, the results continuously highlight the superiority of the RCNN algorithm, which demonstrated amazing performance across many experimental settings. Algorithms trained using the Adam optimizer regularly demonstrated high levels of accuracy, precision, recall, and F1 score. Nonetheless, the effect of epoch counts and batch size on performance variability was seen, necessitating careful consideration to avoid overfitting and ensure generalization. These findings support careful algorithm selection guided by optimization techniques and fine-tuned hyperparameters. The RCNN algorithm's impressive results, proven by its constant superiority, underline its potential to revolutionize bone fracture diagnosis. Further research should focus on hyperparameter tuning and comprehensive validation across several datasets, fostering accurate and efficient solutions for bone fracture diagnoses in medical practice.

Radiographic Detection of Post-Traumatic Bone Fractures: Contribution of Artificial Intelligence Software to the Analysis of Senior and Junior Radiologists.

The aims of this study were: (a) to evaluate the performance of an artificial intelligence (AI) software package (Boneview Trauma, Gleamer) for the detection of post-traumatic bone fractures in radiography as a standalone; (b) used by two radiologists (osteoarticular senior and junior); and (c) to determine to whom AI would be most helpful. Within 14 days of a trauma, 101 consecutive patients underwent radiographic examination of the upper or lower limbs. The definite diagnosis for identifying fractures was: (a) radio-clinical consensus between the radiologist on-call who analyzed the images and the orthopedist (Group 1); (b) Cone Beam computed tomography (CBCT) exploration of the area of interest, in case of doubts or absence of consensus (Group 2). Independently of this diagnosis for both groups, the radiographic images were separately analyzed by two radiologists (osteoarticular senior: SR; junior: JR) prior without, and thereafter with the results of AI. AI performed better than the radiologists in detecting common fractures (Group 1), but not subtle fractures (Group 2). In association with AI, both radiologists increased their overall performances in both groups, whereas this increase was significantly higher for the JR (p < 0.05). AI is reliable for common radiographic fracture identification and is a useful learning tool for radiologists in training. However, the software's overall performance does not exceed that of an osteoarticular senior radiologist, particularly in case of subtle lesions.

Detection of Hand Bone Fractures in X-Ray Images Using Hybrid YOLO NAS

Detection of Hand Bone Fractures in X-Ray Images Using Hybrid YOLO NAS

Automated Bone Fracture Detection in X-ray Imaging to Improve Orthopaedic Diagnostics in Healthcare

The healthcare system in India faces an acute shortage of medical staff, significantly impacting the nation's ability to provide sufficient healthcare services. X-ray reports are paramount to orthopaedic surgeons for making critical patient care decisions. Implementing automated systems for detecting bone fractures offers a potentially efficient solution to supplement the limited workforce. However, these systems encounter substantial hurdles, such as inconsistent image quality and the risk of incorrect diagnosis due to the limitations of current technology. The research provides a specialised tool for identifying bone fractures with high precision (97% accuracy) and an excellent overall rating (F1 score of 0.98), showing it can reliably detect fractures. The assessment of this model is augmented by visual tools like heatmaps and performance graphs, providing an in-depth analysis of its operational effectiveness. Furthermore, creating a user-friendly interface is intended to streamline the adoption of this tool in clinical settings. Notwithstanding its demonstrated precision, the model must navigate data-related challenges, precisely data complexity and the representation of different categories, to prevent skewed outcomes. Clarifying the model's decision-making process and adaptability to various clinical situations are also critical. The study emphasises the need for thorough testing to ensure the model's dependability in diagnosing fractures before being deployed effectively in the medical field.

Unveiling the Spectrum: Versatile Image Processing Techniques in Bone Fracture Detection - A Comprehensive Review

India has more than 3,000 accidents every day, according to a global data analysis released by the World Health Organization. Because the bones are rigid, they provide protection for the vital organs of the human body, such as the heart, brain, lungs, and other internal organs. Treating people who have broken bones requires locating the fractures in their bones. Fragility fractures, which result from weakening bones, are linked to a reduction in quality of life and a loss of independence. One of the most popular methods for identifying problems with the human body's organs and bones is to get X-ray Images. Furthermore, more recent methods like magnetic resonance imaging (MRI), microwave imaging (MWI), and computer tomography (CT) are studied. It is the goal of this review paper to educate the research community about the different kinds of image processing techniques available for bone fracture detection and the facilities that hospitals have available for accurate diagnosis. It also serves as a guide for future researchers to understand the procedures needed for the detection of bone fracture.

Computed tomography of intracranial hemorrhages in injured infants and little children aged from 0 months till 3 years

Introduction. The most common reason for young children to seek medical aid in hospitals is head injuries caused by falls from a small height. Currently, computed tomography (CT) of the head is a preferred method for rapid detection of bone fractures and brain injuries in children.Purpose. To investigate specific features of CT signs of intracranial hemorrhages in children with TBI under three years of age.Material and methods. 1334 children aged less than one month to 3 years with isolated TBI were examined at CT scanning. 128-slice scanner "Ingenuity CT" (Philips) was used for the examination. Scanning of the area of interest (head + cervical spine) was performed at the lowest possible values to reduce radiation exposure, including the O-MAR program, with step 0.75 mm at slice thickness 0.75 mm; reconstruction interval was 2 mm. The voltage applied to an X-ray tube during scanning (kV), current strength and time (mAS) were selected depending on patient's weight and age. The effective dose range was from 1.27 mSv to 1.91 mSv.Results. In 510 out of 1334 injured children (38.2%), there were traumatic injuries of various degree, from uncomplicated cephalohematomas and linear fractures to massive intracranial hematomas and total cerebral edema; the rest 61.8% (n=824) had concussion. The performed CT scanning revealed that 87.84% (448/510) children had skull fractures, of which only 18.3% (82/448) had “isolated skull fractures”; the others (366 = 81.7%) had accompanying intracranial injuries.Discussion. Pathological changes in children, aged 0 mon-3 years, after TBI are significantly different of those in children of other age groups. CT is the basic primary diagnostic instrument and should be used in all children with TBI no later than the first three hours. Radiation diagnostics play a key role in putting a correct diagnosis, if physicians use the information obtained at CT and know TBI mechanism in infants and little children.Conclusion. CT is an imaging method of choice for acute TBI in little children to accurately identify and therefore treat intracranial lesions. In addition, CT is an effective diagnostic tool in detecting secondary traumatic injuries.

Skeletal Fracture Detection with Deep Learning: A Comprehensive Review.

Deep learning models have shown great promise in diagnosing skeletal fractures from X-ray images. However, challenges remain that hinder progress in this field. Firstly, a lack of clear definitions for recognition, classification, detection, and localization tasks hampers the consistent development and comparison of methodologies. The existing reviews often lack technical depth or have limited scope. Additionally, the absence of explainable facilities undermines the clinical application and expert confidence in results. To address these issues, this comprehensive review analyzes and evaluates 40 out of 337 recent papers identified in prestigious databases, including WOS, Scopus, and EI. The objectives of this review are threefold. Firstly, precise definitions are established for the bone fracture recognition, classification, detection, and localization tasks within deep learning. Secondly, each study is summarized based on key aspects such as the bones involved, research objectives, dataset sizes, methods employed, results obtained, and concluding remarks. This process distills the diverse approaches into a generalized processing framework or workflow. Moreover, this review identifies the crucial areas for future research in deep learning models for bone fracture diagnosis. These include enhancing the network interpretability, integrating multimodal clinical information, providing therapeutic schedule recommendations, and developing advanced visualization methods for clinical application. By addressing these challenges, deep learning models can be made more intelligent and specialized in this domain. In conclusion, this review fills the gap in precise task definitions within deep learning for bone fracture diagnosis and provides a comprehensive analysis of the recent research. The findings serve as a foundation for future advancements, enabling improved interpretability, multimodal integration, clinical decision support, and advanced visualization techniques.

New-Generation 0.55 T MRI of the Knee-Initial Clinical Experience and Comparison With 3 T MRI.

The aim of this study was to compare the detection rate of and reader confidence in 0.55 T knee magnetic resonance imaging (MRI) findings with 3 T knee MRI in patients with acute trauma and knee pain. In this prospective study, 0.55 T and 3 T knee MRI of 25 symptomatic patients (11 women; median age, 38 years) with suspected internal derangement of the knee was obtained in 1 setting. On the 0.55 T system, a commercially available deep learning image reconstruction algorithm was used (Deep Resolve Gain and Deep Resolve Sharp; Siemens Healthineers), which was not available on the 3 T system. Two board-certified radiologists reviewed all images independently and graded image quality parameters, noted MRI findings and their respective reporting confidence level for the presence or absence, as well as graded the bone, cartilage, meniscus, ligament, and tendon lesions. Image quality and reader confidence levels were compared ( P < 0.05 = significant), and clinical findings were correlated between 0.55 T and 3 T MRI by calculation of the intraclass correlation coefficient (ICC). Image quality was rated higher at 3 T compared with 0.55 T studies (each P ≤ 0.017). Agreement between 0.55 T and 3 T MRI for the detection and grading of bone marrow edema and fractures, ligament and tendon lesions, high-grade meniscus and cartilage lesions, Baker cysts, and joint effusions was perfect for both readers. Overall identification and grading of cartilage and meniscal lesions showed good agreement between high- and low-field MRI (each ICC > 0.76), with lower agreement for low-grade cartilage (ICC = 0.77) and meniscus lesions (ICC = 0.49). There was no difference in readers' confidence levels for reporting lesions of bone, ligaments, tendons, Baker cysts, and joint effusions between 0.55 T and 3 T (each P > 0.157). Reader reporting confidence was higher for cartilage and meniscal lesions at 3 T (each P < 0.041). New-generation 0.55 T knee MRI, with deep learning-aided image reconstruction, allows for reliable detection and grading of joint lesions in symptomatic patients, but it showed limited accuracy and reader confidence for low-grade cartilage and meniscal lesions in comparison with 3 T MRI.

French community grid for the evaluation of radiological artificial intelligence solutions (DRIM France Artificial Intelligence Initiative)

PurposeThe purpose of this study was to validate a national descriptive and analytical grid for artificial intelligence (AI) solutions in radiology. Materials and methodsThe RAND-UCLA Appropriateness Method was chosen by expert radiologists from the DRIM France IA group for this statement paper. The study, initiated by the radiology community, involved seven steps including literature review, template development, panel selection, pre-panel meeting survey, data extraction and analysis, second and final panel meeting, and data reporting. ResultsThe panel consisted of seven software vendors, three for bone fracture detection using conventional radiology and four for breast cancer detection using mammography. A consensus was reached on various aspects, including general target, main objective, certification marking, integration, expression of results, forensic aspects and cybersecurity, performance and scientific validation, description of the company and economic details, possible usage scenarios in the clinical workflow, database, specific objectives and targets of the AI tool. ConclusionThe study validates a descriptive and analytical grid for radiological AI solutions consisting of ten items, using breast cancer and bone fracture as an experimental guide. This grid would assist radiologists in selecting relevant and validated AI solutions. Further developments of the grid are needed to include other organs and tasks.

"Exploring Radio Frequency Techniques for Bone Fracture Detection: A Comprehensive Review of Low Frequency and Microwave Approaches"

"Exploring Radio Frequency Techniques for Bone Fracture Detection: A Comprehensive Review of Low Frequency and Microwave Approaches"

MDCT evaluation of distal radius fractures and their association with carpal and distal ulnar fractures.

The purpose of this study was to evaluate the characteristics of distal radius fractures (DRFs) in patients undergoing multi-detector computed tomography (MDCT) and their association with carpal and distal ulnar fractures. This retrospective study analyzed 120 patients, who underwent MDCT for evaluation of DRFs. Two radiologists independently evaluated the data for various fracture characteristics and for associated carpal and distal ulnar fractures. Out of 120 DRFs, 74 were complete articular, 40 were partial articular and only 6 were extra-articular. Displacement was present in 99 fractures and intra-articular step off was present in 73 fractures. A total of 81 carpal bone fractures were identified in 46 (38.3%) patients, with more than one carpal bone fracture in 21 patients. Distal ulnar fractures were detected in 79 patients (65.8%), out of which 67 involved the ulnar styloid. DRFs with intra-articular step off were more frequently associated with carpal bone fracture (p value: 0.021), while displaced DRFs were more frequently associated with distal ulnar fracture (p value <0.001). Interobserver agreement for detection of carpal bone fractures (κ = 0.807) and distal ulnar fractures (κ = 0.923) was excellent. Majority of DRFs in patients referred for MDCT were complete articular with high incidence of displacement and intra-articular step off. Associated carpal bone and distal ulna fractures were not uncommon.