Artificial Intelligence-Based Applications for Bone Fracture Detection Using Medical Images: A Systematic Review
Abstract
:1. Introduction
2. Related Work
3. Methodology
- Determine the purpose and formulate research questions: The initial step involves defining the purpose and formulating the research questions for the review. This step is essential to provide clarity for readers and to enhance the efficiency of the review process.
- Draft a detailed protocol: The second step is to draft a comprehensive protocol for the review and ensure all reviewers are trained to follow it. This guarantees that everyone involved is aligned on the review procedures.
- Conduct a search for relevant articles: In the third step, a thorough search for relevant articles is conducted. Modern literature searches typically utilize electronic databases such as IEEE Xplore and ProQuest. Reviewers must be adept at using Boolean operators to perform effective searches in these databases.
- Screen articles for inclusion: The fourth step involves screening the identified articles for inclusion in the review. Reviewers must decide which articles to include or exclude and provide justifications for these decisions.
- Assess the quality of articles: In the fifth step, reviewers assess the quality of the selected articles. It is crucial to include only high-quality articles, as the overall quality of the review depends significantly on this.
- Extract data from included articles: The sixth step involves extracting relevant data from the included articles. These data will be used in the subsequent stages of the review process.
- Data synthesis: The seventh step, known as data synthesis, involves analyzing the extracted data. This process may include aggregating, organizing, comparing, and discussing the findings from the articles.
- Write the systematic review: the final step is to write the systematic review, following standard research writing principles and ensuring the review is detailed enough for others to reproduce its results.
3.1. Research Questions
- How effective are Artificial Intelligence techniques in detecting bone fractures?
- What benefits do Artificial Intelligence techniques offer for bone fracture detection?
- What applications have been developed using AI for bone fracture detection?
- What challenges do these applications face in clinical settings?
3.2. Research Objectives
3.3. Search Approach
3.4. Inclusion Criteria
- The study uses an AI model for bone fracture detection.
- The study evaluates the performance of the AI model used.
- The dataset used to evaluate the model is well described.
- The study is written in English.
3.5. Exclusion Criteria
- The full text of the article is not available online.
- The article is in the form of a poster, tutorial, abstract, or presentation.
- The article is not in English.
- The study does not evaluate the performance of the AI model used.
- The dataset used to evaluate the model is not well described.
3.6. Keywords
3.7. Data Extraction
3.8. Data Analysis
4. Results
4.1. Publication Year and Geographic Distribution
4.2. Publication Types and Venues
5. Discussion
5.1. Effectiveness
5.2. Benefits
- Accuracy improvements: AI models provide higher accuracy in detecting fractures compared to traditional methods. Studies have shown that AI can significantly reduce the rate of missed fractures, particularly in complex cases where human error is more likely [37]. This increased accuracy ensures that patients receive timely and appropriate treatment, reducing the risk of complications.
- Efficiency improvements: Faster analysis and diagnosis, reducing the workload for radiologists. By automating the initial screening process, AI systems can quickly identify and flag potential fractures, allowing radiologists to focus on more complex cases [61,69]. This leads to improved workflow efficiency and shorter patient waiting times, ultimately enhancing the overall patient experience.
- Enhanced diagnostic capabilities: Improved ability to detect subtle fractures that might be missed by human eyes. AI algorithms are trained on vast datasets, enabling them to recognize patterns and anomalies that may not be apparent to human observers [62,70]. This capability is particularly valuable in detecting hairline fractures and other minor injuries, which can be crucial for early intervention and treatment.
- Accessibility: Enhanced access to diagnostic tools in remote and underserved areas. AI-powered diagnostic tools can be deployed in regions with limited access to specialist radiologists, providing high-quality diagnostic support where it is most needed [71,72]. This democratizes healthcare and ensures that patients in all locations can benefit from advanced diagnostic technologies.
- Cost-effectiveness: While not always discussed, the potential cost savings from faster, more accurate diagnoses and reduced need for follow-up imaging and treatments can be significant. AI applications can streamline workflow, reduce overhead costs, and enhance overall healthcare efficiency [73]. This is particularly important in resource-limited settings, where cost savings can make advanced diagnostic techniques more accessible.
5.3. Applications
- Proximal humerus fracture detection: Studies using a deep learning model known as ResNet 152 have achieved an impressive accuracy of 96%, highlighting the model’s effectiveness in identifying shoulder fractures [33]. This application is particularly beneficial in emergency settings where quick and accurate diagnosis is critical. The ability to promptly identify proximal humerus fractures can significantly reduce the time to treatment, thereby improving patient outcomes and reducing the risk of complications.
- Intertrochanteric hip fracture detection: Another deep learning model, often referred to as VGG16, has demonstrated high sensitivity and specificity, with reported sensitivity of 93.9% and specificity of 97.4% [34]. The high performance of these models makes them suitable for integration into clinical workflows, facilitating early and accurate detection of hip fractures. This early detection is crucial for timely intervention, which can enhance recovery rates and reduce the burden on healthcare systems.
- Chest radiograph interpretation: Deep learning models have shown exceptional performance in detecting various thoracic diseases, including rib fractures [37]. The ability to accurately interpret chest radiographs is invaluable in the diagnosis of trauma patients. In emergency and trauma care settings, where timely and accurate diagnosis can be life-saving, AI-enhanced chest radiograph interpretation ensures that all injuries are promptly identified and treated.
- Wrist fracture detection: The Inception V3 model, another sophisticated AI tool, has been effectively utilized to detect wrist fractures, achieving high accuracy metrics [32]. This application is particularly useful in sports medicine and orthopedics, where wrist injuries are common. By providing quick and accurate diagnoses, AI tools can help manage and treat sports injuries more effectively, potentially reducing downtime for athletes and expediting their return to activity.
- Automated reporting systems: AI models integrated into clinical workflows have significantly improved diagnostic efficiency, as evidenced by various studies [26,43]. These systems can automatically generate detailed reports, reducing the administrative burden on radiologists and allowing them to focus more on patient care. Automated reporting also ensures consistency and reduces the risk of human error in documentation, leading to more reliable and standardized diagnostic outputs.
5.4. Challenges
- Data quality and quantity: Effective AI models require high-quality, annotated datasets, which are often scarce and expensive to produce [37]. Additionally, the diversity of training data are crucial to ensure AI models perform well across different populations and clinical scenarios. Efforts to create and share extensive, diverse datasets could help address this issue.
- Generalizability: AI models trained on specific datasets might not perform well with different populations or imaging methods. Ensuring these models are robust across various clinical settings remains a significant challenge [31,43]. Validating AI models in diverse clinical environments is necessary to ensure their reliability and effectiveness. This includes multicenter studies and data from various geographic and demographic backgrounds.
- Integration into clinical workflows: Integrating AI models into current clinical workflows involves overcoming technical, regulatory, and acceptance barriers. It is crucial to ensure smooth integration without disrupting clinical practices [45,46]. This includes technical integration and training healthcare providers to use AI tools effectively and trust their outputs. Successful integration requires collaboration between AI developers, healthcare providers, and regulatory authorities.
- Ethical and legal considerations: The use of AI in healthcare raises ethical and legal issues, including patient privacy, data security, and potential biases in AI algorithms. Addressing these concerns is essential for the broad adoption of AI technologies in medical diagnostics [28]. Regulatory frameworks must be established to ensure AI applications meet legal standards and ethical guidelines. Transparency and accountability in AI decision-making processes are also crucial for maintaining trust.
- User acceptance: For AI technologies to be successfully implemented, healthcare professionals must accept them. Training and education are necessary to build trust and confidence in AI-assisted diagnostic tools [78]. Resistance to change and unfamiliarity with AI technologies can impede adoption. Strategies to increase user acceptance include demonstrating the reliability and effectiveness of AI tools through clinical trials and providing ongoing education and support to healthcare providers.
- Technical challenges: Variations in imaging protocols and quality can impact the performance of AI models. Ensuring consistency in image acquisition and addressing technical variations are essential for reliable AI performance [33]. Developing robust AI models capable of handling a wide range of imaging conditions and integrating quality control measures into AI workflows can help mitigate these challenges.
5.5. Research Contributions
- Comprehensive synthesis: this review provides a detailed synthesis of data from various studies, offering a thorough overview of the current state of AI in bone fracture detection, focusing on its effectiveness, benefits, applications, and challenges.
- Identification of key trends: it highlights key trends in AI research, such as the growing use of deep learning models like CNN, ResNet, and VGG16, which show superior performance in various fracture detection tasks.
- Emphasis on data quality: the review underscores the essential role of high-quality, annotated datasets in training effective AI models and the need for initiatives to create and share extensive, diverse datasets.
- Addressing generalizability issues: by discussing the challenges related to the generalizability of AI models, the review emphasizes the importance of validating AI models in diverse clinical settings to ensure their reliability and effectiveness.
- Focus on integration and acceptance: the review highlights the importance of seamlessly integrating AI into clinical workflows and the need for training and education to build trust and confidence in AI-assisted diagnostic tools among healthcare professionals.
- Ethical and legal considerations: by addressing ethical and legal considerations, the review calls for the establishment of regulatory frameworks to ensure that AI applications comply with legal standards and ethical guidelines, ensuring transparency and accountability in AI decision-making processes.
- Future research directions: the review suggests future research directions, including further validation studies, improving data quality, enhancing model robustness, facilitating seamless integration into clinical workflows, and addressing ethical and legal concerns.
5.6. Limitations
- Database selection: The review was conducted using specific databases recommended for their strong indexing of high-impact, high-quality articles in healthcare and information technology. However, the exclusion of broad multidisciplinary databases like WoS and Scopus may have led to the omission of some relevant studies. Future research could benefit from a more comprehensive search strategy that includes these databases to ensure broader coverage of the literature.
- Search strategy and keywords: The search strategy primarily focused on general AI and machine learning terms, such as “Artificial Intelligence” and “Machine Learning,” without including specific algorithms like Neural Networks, Decision Trees, Random Forests, KNN, and Bayes. This was an intentional decision to capture a broad spectrum of AI applications rather than narrow the focus to incremental performance improvements of specific models. However, this approach may have led to the exclusion of studies that focus on the detailed performance of particular machine learning techniques. Future reviews could consider incorporating these specific terms to ensure a more exhaustive collection of relevant studies.
- Study heterogeneity: The included studies vary significantly in terms of sample size, imaging modalities, and AI models used. This heterogeneity makes it challenging to directly compare results across studies. The lack of standardized reporting on AI model performance and the diverse clinical settings may also affect the generalizability of the findings.
- Lack of meta-analysis: Unlike some other systematic reviews, this study did not perform a meta-analysis due to the variability in study designs and outcome measures. This limits the ability to quantitatively synthesize the results and draw more definitive conclusions about the overall effectiveness of AI in bone fracture detection.
- Rapidly evolving field: The field of AI in medical imaging is rapidly evolving, with new techniques and models being developed continuously. As a result, some of the findings presented in this review may become outdated as newer studies are published. Continuous updates to the review are necessary to keep pace with the latest advancements.
6. Conclusions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Database | Query Strings |
---|---|
Academic Search Complete | (“Bone Fracture” OR “Fracture”) AND (“Detection” OR “Diagnosis”) AND (“X-ray” AND “Diagnostic Performance”) OR (“CT scans”) |
Applied Science & Technology Source | (“Bone Fracture” OR “Fracture”) AND (“Detection” OR “Diagnosis”) AND (“Machine Learning” OR “Deep Learning” OR “Imaging”) |
Springer Nature Journals | (“Bone Fracture” OR “Fracture”) AND (“Detection” OR “Diagnosis”) AND (“Machine Learning” OR “Deep Learning” OR “Imaging”) |
ScienceDirect | (“Bone Fracture” OR “Fracture”) AND (“Detection” OR “Diagnosis”) AND (“Machine Learning”) |
Journals@OVID | (“Fracture Diagnosis” AND “Imaging” AND Bone Fracture) |
Directory of Open Access Journals | (“Bone Fracture” OR “Fracture”) AND (“Detection” OR “Diagnosis”) |
Radiological Society of North America | (“Bone Fracture” OR “Fracture”) AND (“Detection” OR “Diagnosis”) AND (“Machine Learning”) |
MEDLINE | (“Bone Fracture” OR “Fracture”) AND (“Detection” OR “Diagnosis”) AND (“X-ray” AND “Diagnostic Performance”) AND (“CT scans”) |
JSTOR Journals | (“Fracture Diagnosis” AND “Imaging” AND Bone Fracture) |
Data Item | Description |
---|---|
Title | Title of the paper |
Author(s) | Author name(s) |
Type | e.g., conference/workshop/journal |
Date | Publishing year |
Country | Country of authors |
Study Design | Type of study (e.g., retrospective, prospective) |
Sample Size | Number of subjects or images analyzed |
AI Technique Used | Specific AI methods (e.g., deep learning, machine learning algorithms) |
Performance Metrics | Accuracy, sensitivity, specificity, precision, recall, F1 score, ROC-AUC |
Comparison with Other Methods | Performance compared to traditional methods or other AI techniques |
Validation Methods | Cross-validation, external validation, or use of independent test sets |
Statistical Significance | P-values or confidence intervals to determine significance |
Efficiency Improvements | Speed of detection, time savings for radiologists |
Accuracy Improvements | Increased diagnostic accuracy, reduction in human error |
Cost-Effectiveness | Cost savings in the diagnostic process |
Accessibility | Improved access to diagnostics in remote or underserved areas |
Enhanced Diagnostic Capabilities | Detection of fractures that are difficult to identify with traditional methods |
Software Applications | Names and descriptions of software developed |
Commercial Products | AI-based products available on the market |
Integration with Medical Systems | How applications integrate with existing medical imaging systems (e.g., PACS, RIS) |
Use Cases | Examples of applications in clinical practice |
Technical Challenges | Issues related to algorithm performance, data quality, and computational requirements |
Regulatory and Ethical Issues | Regulatory hurdles, ethical concerns regarding AI use in healthcare |
User Acceptance | Acceptance and trust by medical professionals |
Integration Challenges | Difficulty in integrating AI applications with existing hospital systems |
Data Privacy and Security | Concerns about patient data privacy and cybersecurity |
Cost and Resource Requirements | Costs of implementation, need for specialized resources or training |
Future Areas | Areas of future investigation |
Comments | Remarks about the quality of the paper |
No. | Reference | Year | Dataset | Modality | Model | Parts | Description | Performance |
---|---|---|---|---|---|---|---|---|
1 | Olczak et al. [59] | 2017 | The dataset consists of 256,000 radiographs of the wrist, hand, and ankle. | Radiographic images | AlexNet, NIN, VGGs | Various Parts | This research demonstrates that deep learning can exceed human performance. | Accuracy = 0.83 |
2 | Kim et al. [32] | 2018 | There are 695 wrist radiographs with fractures and 694 without fractures. | Radiographic images | Inception V3 | Wrist | The author validated that using transfer learning from CNNs for fracture detection on radiographs can achieve top-tier performance. | AUC = 95.4, Sensitivity = 90, Specificity = 88 |
3 | Chung et al. [33] | 2018 | The dataset includes 1891 plain shoulder AP radiographs (1376 with proximal humerus fractures and 515 normal shoulders) from 1891 patients, comprising 591 men and 1300 women. | Radiographic images | Resnet 152 | Humeral | The authors introduced a model for identifying and categorizing fractures from AP shoulder radiographic images. | Accuracy = 96, Sensitivity = 0.99, Specificity = 0.97, AUC = 0.996 |
4 | Urakawa et al. [34] | 2018 | The dataset includes 3346 hip images, with 1773 fractured and 1573 non-fractured, collected from the Department of Orthopedic Surgery, Tsuruo. | Radiographic images | VGG16 | Hips | This study presents a performance comparison between CNNs and orthopedic surgeons. | Accuracy = 95.5, Sensitivity = 93.9, Specificity = 97.40, AUC = 0.984 |
5 | Yu et al. [35] | 2019 | There are 307 patients with APFFs and 310 normal patients. | Radiographic images | InceptionV3 | Hips | The proposed algorithm excelled in detecting APFF but struggled with precise fracture localization. | Accuracy = 96.9, AUC = 0.994, Sensitivity = 97.1, Specificity = 96.7 |
6 | Choi et al. [36] | 2019 | The dataset comprises 1266 pairs of AP and lateral elbow radiographs examined between January 2013 and December 2017 at a single institution. | Radiographic images | Resnet 50 | Elbow | The authors focused on developing a dual-input CNN-based model for the automated detection of supracondylar fractures. | AUC = 0.985, Sensitivity = 93.9, Specificity = 92.2 |
7 | Majkowska et al. [37] | 2020 | The study utilized two datasets: DS1 with 759,611 images from a multicity hospital network, and ChestX-ray14, a publicly available dataset with 112,120 images. Natural language processing and expert review labeled 657,954 training images. The test sets included 1818 images from DS1 and 1962 from ChestX-ray14. | Radiographic images | Xception | Chest | The authors created a model to identify opacity, pneumothorax, mass or nodule, and fractures. | AUC = 0.86, Sensitivity = 59.9, Specificity = 99.4 |
8 | Johari et al. [38] | 2016 | The dataset contains 240 radiographs of teeth: 120 with no VRFs and 120 with vertical fractures. Each category is split equally between endodontically treated and untreated teeth. | Radiographic images | Probabilistic neural network (PNN) CBCT-G1/2/3, PA-G1/2/3 | Vertical Roots | This study supports the preliminary detection of vertical root fractures. | Accuracy = 96.6, Sensitivity = 93.3, Specificity = 100, CMPIs THRESHOLD = 0.79 |
9 | Heimer et al. [39] | 2018 | The dataset includes 84 skull fracture cases, with 5 excluded due to severe destruction and 4 removed because of surgical material. For each of the 75 included cases, a corresponding case without documented skull fractures was retrieved. | Images are extracted from Postmortem computed tomography (PMCT) | Deep neural networks | Skull | The study aims to classify and detect skull fractures using curved maximum intensity projections (CMIP) and deep neural networks. | Specificity = 87.5, Sensitivity = 91.4, CMPIs THRESHOLD = 0.75 |
10 | Kitamura et al. [40] | 2019 | There are 298 normal and 298 fractured ankle studies identified by parsing radiology reports. | Radiographic images | Seven models: Inception V3, ResNet, Xception | Ankle | The study evaluated the efficiency of CNNs on small datasets. | Best performance by Ensemble_A, Accuracy = 83, Sensitivity = 80, Specificity = 81 |
11 | Gan et al. [41] | 2019 | The training dataset includes 2040 images (1341 with DRFs and 699 without DRFs) and a test dataset of 300 images (150 with DRFs and 150 without DRFs). | Radiographic images | Inception V4 | Wrist | The authors implemented an algorithm to detect distal radius fractures. | Accuracy = 93, AUC = 0.961, Sensitivity = 90, Specificity = 96 |
12 | Lindsey et al. [42] | 2018 | The dataset comprises 135,845 radiographs of various body parts. The remaining 100,855 radiographs cover 11 body parts. The shoulder has the most radiographs (26,042), while the spine has the least (885). | Radiographic images | Unet | Wrist | This study involves using deep learning to help doctors distinguish between fractured and normal wrists. | AUC = 97.5%, Sensitivity = 93.9%, Specificity = 94.5% |
13 | Adams et al. [43] | 2019 | Various dataset sizes (200, 320, and 640 images) are split into training (80%) and validation (20%), with an additional 160 images used as the final test set. | Radiographic images | AlexNet and GoogLeNet | Femur | The author aimed to assess the accuracy of DCNN for femur fracture detection. | Accuracy AlexNet = 89.4%, GoogLeNet = 94.4% |
14 | Rahmaniar et al. [44] | 2018 | The dataset includes 815 coronal images, 777 transverse images, and 618 sagittal images. | A computerized tomography (CT) images | Computerized system | Calcaneal Fractures | In this study, the author focused on detecting femoral neck fractures using genetic and deep learning methods. | Accuracy = 0.86, precision rate = 0.86, recall = 0.89 |
15 | Tomita et al. [45] | 2018 | The dataset consists of 1432 CT scans, comprising 10,546 two-dimensional images in sagittal view, with a test set of 129 CT scans. | A computerized tomography (CT) images | Deep convolutional neural network (CNN) | vertebra | This study aims at the early detection of osteoporotic vertebral fractures. | Accuracy = 89.2%, F1 score = 90.8%, sensitivity = 85.2%, specificity = 95.8% |
16 | Muehlematter et al. [46] | 2019 | Standard CT scans of 58 patients with insufficiency fractures of the spine were performed between 2006 and 2013. | A computerized tomography (CT) images | Machine-learning algorithms | vertebra | The author aimed to evaluate the performance of bone texture analysis with a machine learning algorithm. | AUC = 0.64 |
17 | Wang et al. [47] | 2022 | The dataset includes 222 training images, 56 validation images, and 408 testing images of CT scans. | A computerized tomography (CT) images | U-Net and ResNet | Mandibule | The author developed a novel method for classifying and detecting mandibular fractures. | Accuracy = 90%, AUC = 0.956 |
18 | Pranata et al. [48] | 2019 | Two datasets were used: the first contains 255 fractured and 732 normal images (totaling 987), and the second includes 428 fractured and 516 normal images (totaling 944). | A computerized tomography (CT) images | ResNet and VGG | Femoral Neck | The author aimed at detecting femoral neck fractures using genetic and deep learning techniques. | Accuracy = 0.793, Specificity = 0.729, Sensitivity = 0.829 |
19 | Cheng et al. [60] | 2019 | The dataset comprises 25,505 hip radiographs. | Radiographic images | DenseNet-121 | limb | The goal of this study was to localize and classify hip fractures using deep learning. | accuracy = 91%, sensitivity = 98%, false-negative rate = 2%, AUC = 0.98 |
Year Range | Number of Publications |
---|---|
2016–2017 | 2 |
2018–2019 | 13 |
2020–2021 | 1 |
2022–2023 | 1 |
Country | Number of Publications |
---|---|
United States | 6 |
China | 4 |
Japan | 3 |
United Kingdom | 2 |
Iran | 2 |
Switzerland | 2 |
India | 1 |
Australia | 1 |
South Korea | 1 |
Type | Number of Publications |
---|---|
Journal | 18 |
Conference | 2 |
Journal/Conference Name | Number of Publications |
---|---|
Diagnostics | 2 |
Clinical Radiology | 2 |
Acta Orthopaedica | 2 |
Skeletal Radiology | 1 |
Dentomaxillofacial Radiology | 1 |
Computers in Biology and Medicine | 1 |
European Radiology | 1 |
Clinical Oral Investigations | 1 |
Journal of Medical Imaging and Radiation Oncology | 1 |
IEEE Transactions on Medical Imaging | 1 |
Proceedings of the National Academy of Sciences | 1 |
Journal of Medical Imaging | 1 |
Radiology | 1 |
Artificial Intelligence in Medicine | 1 |
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Kutbi, M. Artificial Intelligence-Based Applications for Bone Fracture Detection Using Medical Images: A Systematic Review. Diagnostics 2024, 14, 1879. https://doi.org/10.3390/diagnostics14171879
Kutbi M. Artificial Intelligence-Based Applications for Bone Fracture Detection Using Medical Images: A Systematic Review. Diagnostics. 2024; 14(17):1879. https://doi.org/10.3390/diagnostics14171879
Chicago/Turabian StyleKutbi, Mohammed. 2024. "Artificial Intelligence-Based Applications for Bone Fracture Detection Using Medical Images: A Systematic Review" Diagnostics 14, no. 17: 1879. https://doi.org/10.3390/diagnostics14171879