The advances in artificial intelligence (AI) technology in recent years have been remarkable, and the field of radiology is at the forefront of applying and implementing these technologies in daily clinical practice. Radiologists must keep up with this trend and continually update their knowledge. This narrative review discusses the application of artificial intelligence in the field of musculoskeletal imaging. For image generation, we focused on the clinical application of deep learning reconstruction and the recently emerging MRI-based cortical bone imaging. For automated diagnostic support, we provided an overview of qualitative diagnosis, including classifications essential for daily practice, and quantitative diagnosis, which can serve as imaging biomarkers for treatment decision making and prognosis prediction. Finally, we discussed current issues in the use of AI, the application of AI in the diagnosis of rare diseases, and the role of AI-based diagnostic imaging in preventive medicine as part of our outlook for the future.

Prior work has demonstrated that AI access can help residents more accurately detect pediatric fractures. We wished to evaluate the effectiveness of an unsupervised AI-based training module as a pediatric fracture detection educational tool.

Two hundred forty radiographic examinations from throughout the pediatric upper extremity were split into two groups of 120 examinations. A previously developed open-source deep learning fracture detection algorithm ( www.childfx.com ) was used to annotate radiographs. Four medical students and four PGY-2 radiology residents first evaluated 120 examinations for fracture without AI assistance and subsequently reviewed AI annotations on these cases via a training module. They then interpreted 120 different examinations without AI assistance. Pre- and post-intervention fracture detection accuracy was evaluated using a chi-squared test.

Overall resident fracture detection accuracy significantly improved from 71.3% pre-intervention to 77.5% post-intervention (p = 0.032). Medical student fracture detection accuracy was not significantly changed from 56.3% pre-intervention to 57.3% post-intervention (p = 0.794). Eighty-eight percent of responding participants (7/8) would recommend this model of learning.

We found that a tailored AI-based training module increased resident accuracy for detecting pediatric fractures by 6.2%. Medical student accuracy was not improved, likely due to their limited background familiarity with the task. AI offers a scalable method for automatically generating annotated teaching cases covering varied pathology, allowing residents to efficiently learn from simulated experience.

The field of pediatric musculoskeletal imaging is undergoing significant advancements due to technological innovations and a growing emphasis on safety and patient-centered care. This review explores recent developments in imaging modalities such as advanced magnetic resonance imaging, ultrasound innovations, and artificial intelligence applications. Highlights include radiation dose-reduction techniques in radiography and computed tomography, enhanced diagnostic tools like contrast-enhanced ultrasound and ultra-high-frequency imaging, and the integration of artificial intelligence for pathology detection and workflow optimization. The adoption of advanced methods like whole-body magnetic resonance imaging and computed tomography-like magnetic resonance imaging sequences has improved diagnostic accuracy, minimized radiation exposure, and expanded the capabilities of noninvasive imaging. Emerging technologies, including photon-counting detector computed tomography and deep learning-based reconstructions, are transforming clinical practices by balancing precision and safety. Artificial intelligence applications are reshaping diagnostic approaches, automating complex assessments, and improving efficiency, although challenges such as external validation and limited scope persist. Functional imaging advancements, such as diffusion-weighted imaging and positron emission tomography-magnetic resonance imaging integration, are enhancing disease characterization and treatment planning. This review underscores the clinical impact of these innovations, emphasizing the need for standardized protocols, interdisciplinary collaboration, and continued research to address unmet needs in radiation safety and artificial intelligence integration. It aims to equip healthcare professionals with the knowledge to leverage these advancements for improved outcomes in pediatric musculoskeletal care.

Fractures are one of the most common reasons of admission to emergency department affecting individuals of all ages and regions worldwide that can be misdiagnosed during radiologic examination. Accurate and timely diagnosis of fracture is crucial for patients, and artificial intelligence that uses algorithms to imitate human intelligence to aid or enhance human performs is a promising solution to address this issue. In the last few years, numerous commercially available algorithms have been developed to enhance radiology practice and a large number of studies apply artificial intelligence to fracture detection. Recent contributions in literature have described numerous advantages showing how artificial intelligence performs better than doctors who have less experience in interpreting musculoskeletal X-rays, and assisting radiologists increases diagnostic accuracy and sensitivity, improves efficiency, and reduces interpretation time. Furthermore, algorithms perform better when they are trained with big data on a wide range of fracture patterns and variants and can provide standardized fracture identification across different radiologist, thanks to the structured report. In this review article, we discuss the use of artificial intelligence in fracture identification and its benefits and disadvantages. We also discuss its current potential impact on the field of radiology and radiomics.

Pediatric elbow fractures are a common injury among children. Recent advancements in artificial intelligence (AI), particularly deep learning (DL), have shown promise in diagnosing these fractures. This study systematically evaluated the performance of DL models in detecting pediatric elbow fractures.

A comprehensive search was conducted in PubMed (Medline), EMBASE, and IEEE Xplore for studies published up to October 20, 2023. Studies employing DL models for detecting elbow fractures in patients aged 0 to 16 years were included. Key performance metrics, including sensitivity, specificity, and area under the curve (AUC), were extracted. The study was registered in PROSPERO (ID: CRD42023470558).

The search identified 22 studies, of which six met the inclusion criteria for the meta-analysis. The pooled sensitivity of DL models for pediatric elbow fracture detection was 0.93 (95% CI: 0.91-0.96). Specificity values ranged from 0.84 to 0.92 across studies, with a pooled estimate of 0.89 (95% CI: 0.85-0.92). The AUC ranged from 0.91 to 0.99, with a pooled estimate of 0.95 (95% CI: 0.93-0.97). Further analysis highlighted the impact of preprocessing techniques and the choice of model backbone architecture on performance.

DL models demonstrate exceptional accuracy in detecting pediatric elbow fractures. For optimal performance, we recommend leveraging backbone architectures like ResNet, combined with manual preprocessing supervised by radiology and orthopedic experts.

Accurate identification of radiographic landmarks is fundamental to characterizing glenohumeral relationships before and sequentially after shoulder arthroplasty, but manual annotation of these radiographs is laborious. We report on the use of artificial intelligence, specifically computer vision and deep learning models (DLMs), in determining the accuracy of DLM-identified and surgeon identified (SI) landmarks before and after anatomic shoulder arthroplasty.

240 true anteroposterior radiographs were annotated using 11 standard osseous landmarks to train a deep learning model. Radiographs were modified to allow for a training model consisting of 2,260 images. The accuracy of DLM landmarks was compared to manually annotated radiographs using 60 radiographs not used in the training model. In addition, we also performed 14 different measurements of component positioning and compared these to measurements made based on DLM landmarks.

The mean deviation between DLM vs. SI cortical landmarks was 1.9 ± 1.9 mm. Scapular landmarks had slightly lower deviations compared to humeral landmarks (1.5 ± 1.8 mm vs. 2.1 ± 2.0 mm, p < 0.001). The DLM was also found to be accurate with respect to 14 measures of scapular, humeral, and glenohumeral measurements with a mean deviation of 2.9 ± 2.7 mm.

An accelerated deep learning model using a base of only 240 annotated images was able to achieve low levels of deviation in identifying common humeral and scapular landmarks on preoperative and postoperative radiographs. The reliability and efficiency of this deep learning model represents a powerful tool to analyze preoperative and postoperative radiographs while avoiding human observer bias.

IV.

Accurate image interpretation is essential in the field of radiology to the healthcare team in order to provide optimal patient care. This article discusses the use of artificial intelligence (AI) confidence levels to enhance the accuracy and dependability of its radiological diagnoses. The current advances in AI technologies have changed how radiologists and clinicians make the diagnoses of pathological conditions such as aneurysms, hemorrhages, pneumothorax, pneumoperitoneum, and particularly fractures. To enhance the utility of these AI models, radiologists need a more comprehensive understanding of the model's levels of confidence and certainty behind the results they produce. This allows radiologists to make more informed decisions that have the potential to drastically change a patient's clinical management. Several AI models, especially those utilizing deep learning models (DL) with convolutional neural networks (CNNs), have demonstrated significant potential in identifying subtle findings in medical imaging that are often missed by radiologists. It is necessary to create standardized levels of confidence metrics in order for AI systems to be relevant and reliable in the clinical setting. Incorporating AI into clinical practice does have certain obstacles like the need for clinical validation, concerns regarding the interpretability of AI system results, and addressing confusion and misunderstandings within the medical community. This study emphasizes the importance of AI systems to clearly convey their level of confidence in radiological diagnosis. This paper highlights the importance of conducting research to establish AI confidence level metrics that are limited to a specific anatomical region or lesion type. KEY POINT OF THE VIEW: Accurate fracture diagnosis relies on radiologic certainty, where Artificial intelligence (AI), especially convolutional neural networks (CNNs) and deep learning (DL), shows promise in enhancing X-ray interpretation amidst a shortage of radiologists. Overcoming integration challenges through improved AI interpretability and education is crucial for widespread acceptance and better patient outcomes.

We wished to evaluate if an open-source artificial intelligence (AI) algorithm ( https://www.childfx.com ) could improve performance of (1) subspecialized musculoskeletal radiologists, (2) radiology residents, and (3) pediatric residents in detecting pediatric and young adult upper extremity fractures.

A set of evaluation radiographs drawn from throughout the upper extremity (elbow, hand/finger, humerus/shoulder/clavicle, wrist/forearm, and clavicle) from 240 unique patients at a single hospital was constructed (mean age 11.3 years, range 0-22 years, 37.9% female). Two fellowship-trained musculoskeletal radiologists, three radiology residents, and two pediatric residents were recruited as readers. Each reader interpreted each case initially without and then subsequently 3-4 weeks later with AI assistance and recorded if/where fracture was present.

Access to AI significantly improved area under the receiver operator curve (AUC) of radiology residents (0.768 [0.730-0.806] without AI to 0.876 [0.845-0.908] with AI, P < 0.001) and pediatric residents (0.706 [0.659-0.753] without AI to 0.844 [0.805-0.883] with AI, P < 0.001) in identifying fracture, respectively. There was no evidence of improvement for subspecialized musculoskeletal radiology attendings in identifying fracture (AUC 0.867 [0.832-0.902] to 0.890 [0.856-0.924], P = 0.093). There was no evidence of difference between overall resident AUC with AI and subspecialist AUC without AI (resident with AI 0.863, attending without AI AUC 0.867, P = 0.856). Overall physician radiograph interpretation time was significantly lower with AI (38.9 s with AI vs. 52.1 s without AI, P = 0.030).

An openly accessible AI model significantly improved radiology and pediatric resident accuracy in detecting pediatric upper extremity fractures.

Artificial intelligence (AI) is increasingly recognized for its transformative potential in radiology; yet, its application in pediatric radiology is relatively limited when compared to the whole of radiology. This manuscript introduces pediatric radiologists to essential AI concepts, including topics such as use case, data science, machine learning, deep learning, natural language processing, and generative AI as well as basics of AI training and validating. We outline the unique challenges of applying AI in pediatric imaging, such as data scarcity and distinct clinical characteristics, and discuss the current uses of AI in pediatric radiology, including both image interpretive and non-interpretive tasks. With this overview, we aim to equip pediatric radiologists with the foundational knowledge needed to engage with AI tools and inspire further exploration and innovation in the field.

To 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).

In 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.

The 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.

The AI algorithm provides good overall diagnostic performance for detecting appendicular fractures in pediatric patients.

Deep learning is a subset of artificial intelligence (AI) with enormous potential to transform orthopaedic surgery. As has already become evident with the deployment of Large Language Models (LLMs) like ChatGPT (OpenAI Inc.), deep learning can rapidly enter clinical and surgical practices. As such, it is imperative that orthopaedic surgeons acquire a deeper understanding of the technical terminology, capabilities and limitations associated with deep learning models. The focus of this series thus far has been providing surgeons with an overview of the steps needed to implement a deep learning-based pipeline, emphasizing some of the important technical details for surgeons to understand as they encounter, evaluate or lead deep learning projects. However, this series would be remiss without providing practical examples of how deep learning models have begun to be deployed and highlighting the areas where the authors feel deep learning may have the most profound potential. While computer vision applications of deep learning were the focus of Parts I and II, due to the enormous impact that natural language processing (NLP) has had in recent months, NLP-based deep learning models are also discussed in this final part of the series. In this review, three applications that the authors believe can be impacted the most by deep learning but with which many surgeons may not be familiar are discussed: (1) registry construction, (2) diagnostic AI and (3) data privacy. Deep learning-based registry construction will be essential for the development of more impactful clinical applications, with diagnostic AI being one of those applications likely to augment clinical decision-making in the near future. As the applications of deep learning continue to grow, the protection of patient information will become increasingly essential; as such, applications of deep learning to enhance data privacy are likely to become more important than ever before. Level of Evidence: Level IV.