Artificial intelligence has evolved significantly since its inception, becoming a powerful tool in medicine. This paper provides an overview of the core principles, applications and future directions of artificial intelligence in hand surgery. Artificial intelligence has shown promise in improving diagnostic accuracy, predicting outcomes and assisting in patient education. However, despite its potential, its application in hand surgery is still nascent, with most studies being retrospective and limited by small sample sizes. To harness the full potential of artificial intelligence in hand surgery and support broader adoption, more robust, large-scale studies are needed. Collaboration among researchers, through data sharing and federated learning, is essential for advancing artificial intelligence from experimental to clinically validated tools, ultimately enhancing patient care and clinical workflows.

Artificial Intelligence (AI) has been shown to enhance fracture-detection-accuracy, but the most effective AI-implementation in clinical practice is less well understood. In the current study, four approaches to AI-implementation are evaluated for their impact on AI-effectiveness.

Retrospective single-center study based on all consecutive, around-the-clock radiographic examinations for suspected fractures, and accompanying clinical-practice radiologist-diagnoses, between January and March 2023. These image-sets were independently analysed by a dedicated bone-fracture-detection-AI. Findings were combined with radiologist clinical-practice diagnoses to simulate the four AI-implementation methods deemed most relevant to clinical workflows: AI-standalone (radiologist-findings not consulted); AI-problem-solving (AI-findings consulted when radiologist in doubt); AI-triage (radiologist-findings consulted when AI in doubt); and AI-safety net (AI-findings consulted when radiologist diagnosis negative). Reference-standard diagnoses were established by two senior musculoskeletal-radiologists (by consensus in cases of disagreement). Radiologist- and radiologist + AI diagnoses were compared for false negatives (FN), false positives (FP) and their clinical consequences. Experience-level-subgroups radiologists-in-training-, non-musculoskeletal-radiologists, and dedicated musculoskeletal-radiologists were analysed separately.

1508 image-sets were included (1227 unique patients; 40 radiologist-readers). Radiologist results were: 2.7 % FN (40/1508), 28 with clinical consequences; 1.2 % FP (18/1508), 2 received full-fracture treatments (11.1 %). All AI-implementation methods changed overall FN and FP with statistical significance (p < 0.001): AI-standalone 1.5 % FN (23/1508; 11 consequences), 6.8 % FP (103/1508); AI-problem-solving 3.2 % FN (48/1508; 31 consequences), 0.6 % FP (9/1508); AI-triage 2.1 % FN (32/1508; 18 consequences), 1.7 % FP (26/1508); AI-safety net 0.07 % FN (1/1508; 1 consequence), 7.6 % FP (115/1508). Subgroups show similar trends, except AI-triage increased FN for all subgroups except radiologists-in-training.

Implementation methods have a large impact on AI-effectiveness. These results suggest AI should not be considered for problem-solving or triage at this time; AI standalone performs better than either and may be a source of assistance where radiologists are unavailable. Best results were obtained implementing AI as safety net, which eliminates missed fractures with serious clinical consequences; even though false positives are increased, unnecessary treatments are limited.

Artificial intelligence (AI) systems are becoming increasingly relevant in everyday clinical practice, with Food and Drug Administration-approved AI solutions now available in many specialties. This development has far-reaching implications for doctors and the future medical profession, highlighting the need for both practicing physicians and medical students to acquire the knowledge, skills, and attitudes necessary to effectively use and evaluate these technologies. Currently, however, there is limited experience with AI-focused curricular training and continuing education.

This paper first introduces a novel blended learning curriculum including one module on AI for medical students in Germany. Second, this paper presents findings from a qualitative postcourse evaluation of students' knowledge and attitudes toward AI and their overall perception of the course.

Clinical-year medical students can attend a 5-day elective course called "Medicine in the Digital Age," which includes one dedicated AI module alongside 4 others on digital doctor-patient communication; digital health applications and smart devices; telemedicine; and virtual/augmented reality and robotics. After course completion, participants were interviewed in semistructured small group interviews. The interview guide was developed deductively from existing evidence and research questions compiled by our group. A subset of interview questions focused on students' knowledge, skills, and attitudes regarding medical AI, and their overall course assessment. Responses were analyzed using Mayring's qualitative content analysis. This paper reports on the subset of students' statements about their perception and attitudes toward AI and the elective's general evaluation.

We conducted a total of 18 group interviews, in which all 35 (100%) participants (female=11, male=24) from 3 consecutive course runs participated. This produced a total of 214 statements on AI, which were assigned to the 3 main categories "Areas of Application," "Future Work," and "Critical Reflection." The findings indicate that students have a nuanced and differentiated understanding of AI. Additionally, 610 statements concerned the elective's overall assessment, demonstrating great learning benefits and high levels of acceptance of the teaching concept. All 35 students would recommend the elective to peers.

The evaluation demonstrated that the AI module effectively generates competences regarding AI technology, fosters a critical perspective, and prepares medical students to engage with the technology in a differentiated manner. The curriculum is feasible, beneficial, and highly accepted among students, suggesting it could serve as a teaching model for other medical institutions. Given the growing number and impact of medical AI applications, there is a pressing need for more AI-focused curricula and further research on their educational impact.

Artificial intelligence (AI) is increasingly being utilized to augment the practice of emergency medicine due to rapid technological advances and breakthroughs. AI applications have been used to enhance triage systems, predict disease-specific risk, estimate staffing needs, forecast patient decompensation, and interpret imaging findings in the emergency department setting. This article aims to help readers without formal training become informed end-users of AI in emergency medicine. The authors will briefly discuss the principles and key terminology of AI, the reasons for its rising popularity, its potential applications in the emergency department setting, and its limitations. Additionally, resources for further self-studying will also be provided.

Radiographic quality control is an integral component of the radiology workflow. In this study, we developed a convolutional neural network model tailored for automated quality control, specifically designed to detect and classify key attributes of wrist radiographs including projection, laterality (based on the right/left marker), and the presence of hardware and/or casts. The model's primary objective was to ensure the congruence of results with image requisition metadata to pass the quality assessment. Using a dataset of 6283 wrist radiographs from 2591 patients, our multitask-capable deep learning model based on DenseNet 121 architecture achieved high accuracy in classifying projections (F1 Score of 97.23%), detecting casts (F1 Score of 97.70%), and identifying surgical hardware (F1 Score of 92.27%). The model's performance in laterality marker detection was lower (F1 Score of 82.52%), particularly for partially visible or cut-off markers. This paper presents a comprehensive evaluation of our model's performance, highlighting its strengths, limitations, and the challenges encountered during its development and implementation. Furthermore, we outline planned future research directions aimed at refining and expanding the model's capabilities for improved clinical utility and patient care in radiographic quality control.

To assess the added value of artificial intelligence (AI) for radiologists and emergency physicians in the radiographic detection of pelvic fractures.

In this retrospective study, one junior radiologist reviewed 940 X-rays of patients admitted to emergency for a fall with suspicion of pelvic fracture between March 2020 and June 2021. The radiologist analyzed the X-rays alone and then using an AI system (BoneView). In a random sample of 100 exams, the same procedure was repeated alongside five other readers (three radiologists and two emergency physicians with 3-30 years of experience). The reference diagnosis was based on the patient's full set of medical imaging exams and medical records in the months following emergency admission.

A total of 633 confirmed pelvic fractures (64.8% from hip and 35.2% from pelvic ring) in 940 patients and 68 pelvic fractures (60% from hip and 40% from pelvic ring) in the 100-patient sample were included. In the whole dataset, the junior radiologist achieved a significant sensitivity improvement with AI assistance (Se-PELVIC = 77.25% to 83.73%; p < 0.001, Se-HIP 93.24 to 96.49%; p < 0.001 and Se-PELVIC RING 54.60% to 64.50%; p < 0.001). However, there was a significant decrease in specificity with AI assistance (Spe-PELVIC = 95.24% to 93.25%; p = 0.005 and Spe-HIP = 98.30% to 96.90%; p = 0.005). In the 100-patient sample, the two emergency physicians obtained an improvement in fracture detection sensitivity across the pelvic area + 14.70% (p = 0.0011) and + 10.29% (p < 0.007) respectively without a significant decrease in specificity. For hip fractures, E1's sensitivity increased from 59.46% to 70.27% (p = 0.04), and E2's sensitivity increased from 78.38% to 86.49% (p = 0.08). For pelvic ring fractures, E1's sensitivity increased from 12.90% to 32.26% (p = 0.012), and E2's sensitivity increased from 19.35% to 32.26% (p = 0.043).

AI improved the diagnostic performance for emergency physicians and radiologists with limited experience in pelvic fracture screening.

The application of artificial intelligence (AI) in healthcare has seen widespread implementation, with numerous studies highlighting the development of robust algorithms. However, limited attention has been given to the secure utilization of raw data for medical model training, and its subsequent impact on clinical decision-making and real-world applications. This study aims to assess the feasibility and effectiveness of an advanced diagnostic model that integrates blockchain technology and AI for the identification of tibial plateau fractures (TPFs) in emergency settings.

In this study, blockchain technology was utilized to construct a distributed database for trauma orthopedics, images collected from three independent hospitals for model training, testing, and internal validation. Then, a distributed network combining blockchain and deep learning was developed for the detection of TPFs, with model parameters aggregated across multiple nodes to enhance accuracy. The model's performance was comprehensively evaluated using metrics including accuracy, sensitivity, specificity, F1 score, and the area under the receiver operating characteristic curve (AUC). In addition, the performance of the centralized model, the distributed AI model, clinical orthopedic attending physicians, and AI-assisted attending physicians was tested on an external validation dataset.

In the testing set, the accuracy of our distributed model was 0.9603 [95% CI (0.9598, 0.9605)] and the AUC was 0.9911 [95% CI (0.9893, 0.9915)] for TPF detection. In the external validation set, the accuracy reached 0.9636 [95% CI (0.9388, 0.9762)], was slightly higher than that of the centralized YOLOv8n model at 0.9632 [95% CI (0.9387, 0.9755)] (p > 0.05), and exceeded the orthopedic physician at 0.9291 [95% CI (0.9002, 0.9482)] and radiology attending physician at 0.9175 [95% CI (0.8891, 0.9393)], with a statistically significant difference (p < 0.05). Additionally, the centralized model (4.99 ± 0.01 min) had shorter diagnosis times compared to the orthopedic attending physician (25.45 ± 1.92 min) and the radiology attending physician (26.21 ± 1.20 min), with a statistically significant difference (p < 0.05).

The model based on the integration of blockchain technology and AI can realize safe, collaborative, and convenient assisted diagnosis of TPF. Through the aggregation of training parameters by decentralized algorithms, it can achieve model construction without data leaving the hospital and may exert clinical application value in the emergency settings.

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.

The potential intricacy of skull fractures as well as the complexity of underlying anatomy poses diagnostic hurdles for radiologists evaluating computed tomography (CT) scans. The necessity for automated diagnostic tools has been brought to light by the shortage of radiologists and the growing demand for rapid and accurate fracture diagnosis. Convolutional Neural Networks (CNNs) are a potential new class of medical imaging technologies that use deep learning (DL) to improve diagnosis accuracy. The objective of this systematic review and meta-analysis is to assess how well CNN models diagnose skull fractures on CT images.

PubMed, Scopus, and Web of Science were searched for studies published before February 2024 that used CNN models to detect skull fractures on CT scans. Meta-analyses were conducted for area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Egger's and Begg's tests were used to assess publication bias.

Meta-analysis was performed for 11 studies with 20,798 patients. Pooled average AUC for implementing pre-training for transfer learning in CNN models within their training model's architecture was 0.96 ± 0.02. The pooled averages of the studies' sensitivity and specificity were 1.0 and 0.93, respectively. The accuracy was obtained 0.92 ± 0.04. Studies showed heterogeneity, which was explained by differences in model topologies, training models, and validation techniques. There was no significant publication bias detected.

CNN models perform well in identifying skull fractures on CT scans. Although there is considerable heterogeneity and possibly publication bias, the results suggest that CNNs have the potential to improve diagnostic accuracy in the imaging of acute skull trauma. To further enhance these models' practical applicability, future studies could concentrate on the utility of DL models in prospective clinical trials.

Distal radius fractures (DRFs) are often initially assessed by junior doctors under time constraints, with limited supervision, risking significant consequences if missed. Convolutional Neural Networks (CNNs) can aid in diagnosing fractures. This study aims to internally and externally validate an open source algorithm for the detection and localization of DRFs.

Patients from a level 1 trauma center from Adelaide, Australia that presented between 2016 and 2020 with wrist trauma were retrospectively included. Radiographs were reviewed confirming the presence or absence of a fracture, as well as annotating radius, ulna, and fracture location. An internal validation dataset from the same hospital was created. An external validation set was created with two other level 1 trauma centers, from Groningen and Rotterdam, the Netherlands. Three surgeons reviewed both sets for DRFs.

The algorithm was trained on 659 radiographs. The internal validation set included 190 patients, showing an accuracy of 87% and an AUC of 0.93 for DRF detection. The external validation set consisted of 188 patients, with an accuracy and AUC were 82% and 0.88 respectively. Radial and ulnar bone segmentation on the internal validation was excellent with an AP50 of 99 and 98, but moderate for fracture segmentation with an AP50 of 29. For external validation the AP50 was 92, 89 and 25 for radius, ulna, and fracture respectively.

This open-source algorithm effectively detects DRFs with high accuracy and localizes them with moderate accuracy. It can assist clinicians in diagnosing suspected DRFs and is the first radiograph-based CNN externally validated on patients from multiple hospitals.

Perilunate/lunate injuries are frequently misdiagnosed. We hypothesize that utilization of a machine learning algorithm can improve human detection of perilunate/lunate dislocations.

Participants from emergency medicine, hand surgery, and radiology were asked to evaluate 30 lateral wrist radiographs for the presence of a perilunate/lunate dislocation with and without the use of a machine learning algorithm, which was used to label the lunate. Human performance with and without the machine learning tool was evaluated using sensitivity, specificity, accuracy, and F1 score.

A total of 137 participants were recruited, with 55 respondents from emergency medicine, 33 from radiology, and 49 from hand surgery. Thirty-nine participants were attending physicians or fellows, and 98 were residents. Use of the machine learning tool improved specificity from 88% to 94%, accuracy from 89% to 93%, and F1 score from 0.89 to 0.92. When stratified by training level, attending physicians and fellows had an improvement in specificity from 93% to 97%. For residents, use of the machine learning tool resulted in improved accuracy from 86% to 91% and specificity from 86% to 93%. The performance of surgery and radiology residents improved when assisted by the tool to achieve similar accuracy to attendings, and their assisted diagnostic performance reaches levels similar to that of the fully automated artificial intelligence tool.

Use of a machine learning tool improves resident accuracy for radiographic detection of perilunate dislocations, and improves specificity for all training levels. This may help to decrease misdiagnosis of perilunate dislocations, particularly when subspecialist evaluation is delayed.

Missed or delayed diagnosis of occult fractures of tibial plateau may cause adverse effects on patients. The objective of this study was to evaluate the diagnostic performance of texture analysis (TA) of knee joint radiographs combined with machine learning (ML) in identifying patients at risk of tibial plateau occult fractures.

A total of 169 patients with negative fracture on knee X-ray films from 2018 to 2022 who were diagnosed with occult tibial plateau fractures or no fractures by subsequent magnetic resonance imaging (MRI) examination were retrospectively enrolled. The X-ray images of the patient's knee joint were used for texture feature extraction. A total of 9 ML feature selection methods (including 6 mainstream methods and 3 methods provided by MaZda software) combined with 3 classification methods were used to build the best diagnostic model. The performance of each model was evaluated by accuracy, F1-value, and area under the curve (AUC).

The least absolute shrinkage and selection operator (LASSO) method had the best performance of the 6 mainstream methods, with an accuracy of 0.81, an F1 value of 0.80, and an AUC of 0.920, all of which were higher than those of the other five methods (accuracy range: 0.65-0.80, F1 score range: 0.61-0.79, AUC range: 0.722-0.895). Among the three feature selection models in MaZda software, the most ideal method for accuracy measurement was the MI method, reaching 0.77. In the measurement of the F1 value and AUC, MaZda's best method was Fisher, reaching 0.78 and 0.888, respectively. All indicators were lower than those of the LASSO method. The combination of LASSO and support vector machine (SVM) yielded the best classification performance, while the performance of the combination of LASSO and logistic regression was slightly inferior, but the difference was not statistically significant.

TA of knee joint radiography combined with ML has achieved high performance in identifying patients at risk of occult fractures of the tibial plateau. Considering both the model performance and computational complexity, the LASSO feature selection method combined with the logistic regression classifier yielded the best classification performance in this process.

During the last decade, artificial intelligence (AI) was applied to nearly all domains of human activity, including scientific research. It is thus warranted to ask whether AI thinking should be durably involved in biomedical research. This problem was addressed by examining three complementary questions (i) What are the major barriers currently met by biomedical investigators? It is suggested that during the last 2 decades there was a shift towards a growing need to elucidate complex systems, and that this was not sufficiently fulfilled by previously successful methods such as theoretical modeling or computer simulation (ii) What is the potential of AI to meet the aforementioned need? it is suggested that recent AI methods are well-suited to perform classification and prediction tasks on multivariate systems, and possibly help in data interpretation, provided their efficiency is properly validated. (iii) Recent representative results obtained with machine learning suggest that AI efficiency may be comparable to that displayed by human operators. It is concluded that AI should durably play an important role in biomedical practice. Also, as already suggested in other scientific domains such as physics, combining AI with conventional methods might generate further progress and new applications, involving heuristic and data interpretation.

Missed fractures are the most common radiologic error in clinical practice, and erroneous classification could lead to inappropriate treatment and unfavorable prognosis. Here, we developed a fully automated deep learning model to detect and classify femoral neck fractures using plain radiographs, and evaluated its utility for diagnostic assistance and physician training.

1527 plain pelvic and hip radiographs obtained between April 2014 and July 2023 at our Hospital were selected for the model training and evaluation. Faster R-CNN was used to locate the femoral neck. DenseNet-121 was used for Garden classification of the femoral neck fracture, while an additional segmentation method used to visualize the probable fracture area. The model was assessed by the area under the receiver operating characteristic curve (AUC). The accuracy, sensitivity, and specificity for clinicians fracture detection in the diagnostic assistance and physician training experiments were determined.

The accuracy of the model for fracture detection was 94.1 %. The model achieved AUCs of 0.99 for no femoral neck fractures, 0.94 for Garden I/II fractures, and 0.99 for Garden III/IV fractures. In the diagnostic assistance study, the emergency physicians had an average accuracy of 86.33 % unaided and 92.03 % aided, sensitivity of 85.94 % unaided and 91.78 % aided, and specificity of 87.88 % unaided and 93.13 % aided in detecting fractures. In the physician training study, the accuracy, sensitivity, and specificity of the trainees for fracture classification were 81.83 %, 77.28 %, and 84.85 %, respectively, before training, compared with 90.65 %, 88.31 %, and 92.21 %, respectively, after training.

The model represents a valuable tool for physicians to better visualize fractures and improve training outcomes, indicating deep learning algorithms as a promising approach to improve clinical practice and medical education.

Distal radius fracture (DRF) is one of the most common types of wrist fractures. We aimed to construct a model for the automatic segmentation of wrist radiographs using a deep learning approach and further perform automatic identification and classification of DRF. A total of 2240 participants with anteroposterior wrist radiographs from one hospital between January 2015 and October 2021 were included. The outcomes were automatic segmentation of wrist radiographs, identification of DRF, and classification of DRF (type A, type B, type C). The Unet model and Fast-RCNN model were used for automatic segmentation. The DenseNet121 model and ResNet50 model were applied to DRF identification of DRF. The DenseNet121 model, ResNet50 model, VGG-19 model, and InceptionV3 model were used for DRF classification. The area under the curve (AUC) with 95% confidence interval (CI), accuracy, precision, and F1-score was utilized to assess the effectiveness of the identification and classification models. Of these 2240 participants, 1440 (64.3%) had DRF, of which 701 (48.7%) were type A, 278 (19.3%) were type B, and 461 (32.0%) were type C. Both the Unet model and the Fast-RCNN model showed good segmentation of wrist radiographs. For DRF identification, the AUCs of the DenseNet121 model and the ResNet50 model in the testing set were 0.941 (95%CI: 0.926-0.965) and 0.936 (95%CI: 0.913-0.955), respectively. The AUCs of the DenseNet121 model (testing set) for classification type A, type B, and type C were 0.96, 0.96, and 0.96, respectively. The DenseNet121 model may provide clinicians with a tool for interpreting wrist radiographs.

To evaluate the performance of convolutional neural networks (CNNs) for the automated detection and classification of mandibular fractures on multislice spiral computed tomography (MSCT).

MSCT data from 361 patients with mandibular fractures were retrospectively collected. Two experienced maxillofacial surgeons annotated the images as ground truth. Fractures were detected utilizing the following models: YOLOv3, YOLOv4, Faster R-CNN, CenterNet, and YOLOv5-TRS. Fracture sites were classified by the following models: AlexNet, GoogLeNet, ResNet50, original DenseNet-121, and modified DenseNet-121. The performance was evaluated for accuracy, sensitivity, specificity, and area under the curve (AUC). AUC values were compared using the Z-test and P values <.05 were considered to be statistically significant.

Of all of the detection models, YOLOv5-TRS obtained the greatest mean accuracy (96.68%). Among all of the fracture subregions, body fractures were the most reliably detected (with accuracies of 88.59%-99.01%). For classification models, the AUCs for body fractures were higher than those of condyle and angle fractures, and they were all above 0.75, with the highest AUC at 0.903. Modified DenseNet-121 had the best overall classification performance with a mean AUC of 0.814.

The modified CNN-based models demonstrated high reliability for the diagnosis of mandibular fractures on MSCT.

Human or time resources can sometimes fall short in medical image diagnostics, and analyzing images in full detail can be a challenging task. With recent advances in artificial intelligence, an increasing number of systems have been developed to assist clinicians in their work. In this study, the objective was to train a model that can distinguish between various fracture types on different levels of hierarchical taxonomy and detect them on 2D-image representations of volumetric postmortem computed tomography (PMCT) data. We used a deep learning model based on the ResNet50 architecture that was pretrained on ImageNet data, and we used transfer learning to fine-tune it to our specific task. We trained our model to distinguish between "displaced," "nondisplaced," "ad latus," "ad longitudinem cum contractione," and "ad longitudinem cum distractione" fractures. Radiographs with no fractures were correctly predicted in 95-99% of cases. Nondisplaced fractures were correctly predicted in 80-86% of cases. Displaced fractures of the "ad latus" type were correctly predicted in 17-18% of cases. The other two displaced types of fractures, "ad longitudinem cum contractione" and "ad longitudinem cum distractione," were correctly predicted in 70-75% and 64-75% of cases, respectively. The model achieved the best performance when the level of hierarchical taxonomy was high, while it had more difficulties when the level of hierarchical taxonomy was lower. Overall, deep learning techniques constitute a reliable solution for forensic pathologists and medical practitioners seeking to reduce workload.

Early and accurate assessment of distal radius fractures (DRFs) is crucial for optimal prognosis. Identifying fractures likely to lose threshold alignment (instability) in a cast is vital for treatment decisions, yet prediction tools' accuracy and reliability remain challenging. Artificial intelligence (AI), particularly Convolutional Neural Networks (CNNs), can evaluate radiographic images with high performance. This systematic review aims to summarize studies utilizing CNNs to detect, classify, or predict loss of threshold alignment of DRFs.

A literature search was performed according to the PRISMA. Studies were eligible when the use of AI for the detection, classification, or prediction of loss of threshold alignment was analyzed. Quality assessment was done with a modified version of the methodologic index for non-randomized studies (MINORS).

Of the 576 identified studies, 15 were included. On fracture detection, studies reported sensitivity and specificity ranging from 80 to 99% and 73-100%, respectively; the AUC ranged from 0.87 to 0.99; the accuracy varied from 82 to 99%. The accuracy of fracture classification ranged from 60 to 81% and the AUC from 0.59 to 0.84. No studies focused on predicting loss of thresholds alignement of DRFs.

AI models for DRF detection show promising performance, indicating the potential of algorithms to assist clinicians in the assessment of radiographs. In addition, AI models showed similar performance compared to clinicians. No algorithms for predicting the loss of threshold alignment were identified in our literature search despite the clinical relevance of such algorithms.

Artificial intelligence (AI) is an interdisciplinary field that combines computer technology, mathematics, and several other fields. Recently, with the rapid development of machine learning (ML) and deep learning (DL), significant progress has been made in the field of AI. As one of the fastest-growing branches, DL can effectively extract features from big data and optimize the performance of various tasks. Moreover, with advancements in digital imaging technology, DL has become a key tool for processing high-dimensional medical image data and conducting medical image analysis in clinical applications. With the development of this technology, the diagnosis of orthopedic diseases has undergone significant changes. In this review, we describe recent research progress on DL in fracture diagnosis and discuss the value of DL in this field, providing a reference for better integration and development of DL technology in orthopedics.

Artificial Intelligence (AI) has demonstrated exceptional performance in automating critical healthcare tasks, such as diagnostic imaging analysis and predictive modeling, often surpassing human capabilities. The integration of AI in healthcare promises substantial improvements in patient outcomes, including faster diagnosis and personalized treatment plans. However, AI models frequently lack interpretability, leading to significant challenges concerning their performance and generalizability across diverse patient populations. These opaque AI technologies raise serious patient safety concerns, as non-interpretable models can result in improper treatment decisions due to misinterpretations by healthcare providers. Our systematic review explores various AI applications in healthcare, focusing on the critical assessment of model interpretability and accuracy. We identify and elucidate the most significant limitations of current AI systems, such as the black-box nature of deep learning models and the variability in performance across different clinical settings. By addressing these challenges, our objective is to provide healthcare providers with well-informed strategies to develop innovative and safe AI solutions. This review aims to ensure that future AI implementations in healthcare not only enhance performance but also maintain transparency and patient safety.

Fragility fractures associated with osteoporosis and osteopenia are a common cause of morbidity and mortality. Current methods of diagnosing low bone mineral density require specialized dual x-ray absorptiometry (DXA) scans. Plain hand radiographs may have utility as an alternative screening tool, although optimal diagnostic radiographic parameters are unknown, and measurement is prone to human error. The aim of the present study was to develop and validate an artificial intelligence algorithm to screen for osteoporosis and osteopenia using standard hand radiographs.

A cohort of patients with both a DXA scan and a plain hand radiograph within 12 months of one another was identified. Hand radiographs were labeled as normal, osteopenia, or osteoporosis based on corresponding DXA hip T-scores. A deep learning algorithm was developed using the ResNet-50 framework and trained to predict the presence of osteoporosis or osteopenia on hand radiographs using labeled images. The results from the algorithm were validated using a separate balanced validation set, with the calculation of sensitivity, specificity, accuracy, and receiver operating characteristic curve using definitions from corresponding DXA scans as the reference standard.

There was a total of 687 images in the normal category, 607 images in the osteopenia category, and 130 images in the osteoporosis category for a total of 1,424 images. When predicting low bone density (osteopenia or osteoporosis) versus normal bone density, sensitivity was 88.5%, specificity was 65.4%, overall accuracy was 80.8%, and the area under the curve was 0.891, at the standard threshold of 0.5. If optimizing for both sensitivity and specificity, at a threshold of 0.655, the model achieved a sensitivity of 84.6% at a specificity of 84.6%.

The findings represent a possible step toward more accessible, cost-effective, automated diagnosis and therefore earlier treatment of osteoporosis/osteopenia.

Diagnostic II.

Artificial intelligence (AI) is becoming increasingly integral in clinical practice, such as during imaging tasks associated with the diagnosis and evaluation of blunt chest trauma (BCT). Due to significant advances in imaging-based deep learning, recent studies have demonstrated the efficacy of AI in the diagnosis of BCT, with a focus on rib fractures, pulmonary contusion, hemopneumothorax and others, demonstrating significant clinical progress. However, the complicated nature of BCT presents challenges in providing a comprehensive diagnosis and prognostic evaluation, and current deep learning research concentrates on specific clinical contexts, limiting its utility in addressing BCT intricacies. Here, we provide a review of the available evidence surrounding the potential utility of AI in BCT, and additionally identify the challenges impeding its development. This review offers insights on how to optimize the role of AI in the diagnostic evaluation of BCT, which can ultimately enhance patient care and outcomes in this critical clinical domain.

The integration of artificial intelligence (AI) in radiology has brought about substantial advancements and transformative potential in diagnostic imaging practices. This study presents an overview of the current research on the application of AI in radiology, highlighting key insights from recent studies and surveys. These recent studies have explored the expected impact of AI, encompassing machine learning and deep learning, on the work volume of diagnostic radiologists. The present and future role of AI in radiology holds great promise for enhancing diagnostic capabilities, improving workflow efficiency, and ultimately, advancing patient care.

The field of neuropathology, a subspecialty of pathology which studies the diseases affecting the nervous system, is experiencing significant changes due to advancements in artificial intelligence (AI). Traditionally reliant on histological methods and clinical correlations, neuropathology is now experiencing a revolution due to the development of AI technologies like machine learning (ML) and deep learning (DL). These technologies enhance diagnostic accuracy, optimize workflows, and enable personalized treatment strategies. AI algorithms excel at analyzing histopathological images, often revealing subtle morphological changes missed by conventional methods. For example, deep learning models applied to digital pathology can effectively differentiate tumor grades and detect rare pathologies, leading to earlier and more precise diagnoses. Progress in neuroimaging is another helpful tool of AI, as enhanced analysis of MRI and CT scans supports early detection of neurodegenerative diseases. By identifying biomarkers and progression patterns, AI aids in timely therapeutic interventions, potentially slowing disease progression. In molecular pathology, AI's ability to analyze complex genomic data helps uncover the genetic and molecular basis of neuropathological conditions, facilitating personalized treatment plans. AI-driven automation streamlines routine diagnostic tasks, allowing pathologists to focus on complex cases, especially in settings with limited resources. This review explores AI's integration into neuropathology, highlighting its current applications, benefits, challenges, and future directions.

Scaphoid fractures, particularly occult and non-displaced fractures, are difficult to detect using traditional X-ray methods because of their subtle appearance and variability in bone density. This study proposes a two-stage CNN approach to detect and classify scaphoid fractures using anterior-posterior (AP) and lateral (LA) X-ray views for more accurate diagnosis.

This study emphasizes the use of multi-view X-ray images (AP and LA views) to improve fracture detection and classification. The multi-view fusion module helps integrate information from both views to enhance detection accuracy, particularly for occult fractures that may not be visible in a single view. The proposed method includes two stages, which are stage 1: detect the scaphoid bone using Faster RCNN and a Feature Pyramid Network (FPN) for region proposal and small object detection. The detection accuracy for scaphoid localization is 100%, with Intersection over Union (IoU) scores of 0.8662 for AP views and 0.8478 for LA views. And stage 2: perform fracture classification using a ResNet backbone and FPN combined with a multi-view fusion module to combine features from both AP and LA views. This stage achieves a classification accuracy of 89.94%, recall of 87.33%, and precision of 90.36%.

The proposed model performs well in both scaphoid bone detection and fracture classification. The multi-view fusion approach significantly improves recall and accuracy in detecting fractures compared to single-view approaches. In scaphoid detection, both AP and LA views achieved 100% detection accuracy. In fracture detection, using multi-view fusion, the accuracy for AP views reached 87.16%, and for LA views, it reached 83.83%.

The multi-view fusion model effectively improves the detection of scaphoid fractures, particularly in cases of occult and non-displaced fractures. The model provides a reliable, automated approach to assist clinicians in detecting and diagnosing scaphoid fractures more efficiently.

Chest X-rays are the most commonly performed medical imaging exam, yet they are often misinterpreted by physicians. Here, we present an FDA-cleared, artificial intelligence (AI) system which uses a deep learning algorithm to assist physicians in the comprehensive detection and localization of abnormalities on chest X-rays. We trained and tested the AI system on a large dataset, assessed generalizability on publicly available data, and evaluated radiologist and non-radiologist physician accuracy when unaided and aided by the AI system. The AI system accurately detected chest X-ray abnormalities (AUC: 0.976, 95% bootstrap CI: 0.975, 0.976) and generalized to a publicly available dataset (AUC: 0.975, 95% bootstrap CI: 0.971, 0.978). Physicians showed significant improvements in detecting abnormalities on chest X-rays when aided by the AI system compared to when unaided (difference in AUC: 0.101, p < 0.001). Non-radiologist physicians detected abnormalities on chest X-ray exams as accurately as radiologists when aided by the AI system and were faster at evaluating chest X-rays when aided compared to unaided. Together, these results show that the AI system is accurate and reduces physician errors in chest X-ray evaluation, which highlights the potential of AI systems to improve access to fast, high-quality radiograph interpretation.

The importance of patient perspectives is increasingly appreciated in clinical practice and academia with formal engagement processes developing worldwide. Digital surgery encompasses intraoperative patient data (including surgical video) analysis and so requires public-patient involvement (PPI).

Engagement events were conducted based on NIHR and GRIPP2 LF guidelines. Following informative talks on digital surgery, invited patients and patient relatives were split into focus groups regarding 1) Research; 2) Data; 3) Industry Involvement; and 4) Artificial Intelligence in surgery. Scribed feedback was thematically analysed by two researchers independently. A pre and post event survey was sought voluntarily.

36 participant perspectives were analysed. In general, patients were enthusiastic about having a voice in surgical research and sharing their journey, with most groups concluding that capturing this was most appropriate after treatment recovery. The use of patient data for surgical development (i.e. research and education) was endorsed unanimously for the purpose of future patient benefit when responsibly and transparently managed and the value of industry was acknowledged. From 30 pre/post surveys (all p > 0.05), participants afforded the greatest data (including video) ownership claim to the surgical team (52 %/48 %) versus patients (32 %/24 %) and the hospital (12 %/24 %). While most (73 %/80 %) agreed that AI should be applied in surgical care, most felt the surgeon most valuable (93 %/80 %) with participants disagreeing that AI should make diagnoses (57 %/64 %) or treat patients (70 %/70 %) without human input.

Patients capably represent stable views and expectations that can strengthen modern and evolving surgical development involving data privacy, ownership and management.

The aim of this study was to create artificial intelligence (AI) software with the purpose of providing a second opinion to physicians to support distal radius fracture (DRF) detection, and to compare the accuracy of fracture detection of physicians with and without software support.

The dataset consisted of 26,121 anonymized anterior-posterior (AP) and lateral standard view radiographs of the wrist, with and without DRF. The convolutional neural network (CNN) model was trained to detect the presence of a DRF by comparing the radiographs containing a fracture to the inconspicuous ones. A total of 11 physicians (six surgeons in training and five hand surgeons) assessed 200 pairs of randomly selected digital radiographs of the wrist (AP and lateral) for the presence of a DRF. The same images were first evaluated without, and then with, the support of the CNN model, and the diagnostic accuracy of the two methods was compared.

At the time of the study, the CNN model showed an area under the receiver operating curve of 0.97. AI assistance improved the physician's sensitivity (correct fracture detection) from 80% to 87%, and the specificity (correct fracture exclusion) from 91% to 95%. The overall error rate (combined false positive and false negative) was reduced from 14% without AI to 9% with AI.

The use of a CNN model as a second opinion can improve the diagnostic accuracy of DRF detection in the study setting.

Conventional radiography (CR) is primarily utilized for fracture diagnosis. Artificial intelligence (AI) for CR is a rapidly growing field aimed at enhancing efficiency and increasing diagnostic accuracy. However, the diagnostic performance of commercially available AI fracture detection solutions (CAAI-FDS) for CR in various anatomical regions, their synergy with human assessment, as well as the influence of industry funding on reported accuracy are unknown. Peer-reviewed diagnostic test accuracy (DTA) studies were identified through a systematic review on Pubmed and Embase. Diagnostic performance measures were extracted especially for different subgroups such as product, type of rater (stand-alone AI, human unaided, human aided), funding, and anatomical region. Pooled measures were obtained with a bivariate random effects model. The impact of rater was evaluated with comparative meta-analysis. Seventeen DTA studies of seven CAAI-FDS analyzing 38,978 x-rays with 8,150 fractures were included. Stand-alone AI studies (n = 15) evaluated five CAAI-FDS; four with good sensitivities (> 90%) and moderate specificities (80-90%) and one with very poor sensitivity (< 60%) and excellent specificity (> 95%). Pooled sensitivities were good to excellent, and specificities were moderate to good in all anatomical regions (n = 7) apart from ribs (n = 4; poor sensitivity / moderate specificity) and spine (n = 4; excellent sensitivity / poor specificity). Funded studies (n = 4) had higher sensitivity (+ 5%) and lower specificity (-4%) than non-funded studies (n = 11). Sensitivity did not differ significantly between stand-alone AI and human AI aided ratings (p = 0.316) but specificity was significantly higher the latter group (p < 0.001). Sensitivity was significant lower in human unaided compared to human AI aided respectively stand-alone AI ratings (both p ≤ 0.001); specificity was higher in human unaided ratings compared to stand-alone AI (p < 0.001) and showed no significant differences AI aided ratings (p = 0.316). The study demonstrates good diagnostic accuracy across most CAAI-FDS and anatomical regions, with the highest performance achieved when used in conjunction with human assessment. Diagnostic accuracy appears lower for spine and rib fractures. The impact of industry funding on reported performance is small.

To evaluate the feasibility of acquiring vertebral height from chest low-dose computed tomography (LDCT) images using an artificial intelligence (AI) system based on 3D U-Net vertebral segmentation technology and the correlation and features of vertebral morphology with sex and age of the Chinese population. Patients who underwent chest LDCT between September 2020 and April 2023 were enrolled. The Altman and Pearson's correlation analyses were used to compare the correlation and consistency between the AI software and manual measurement of vertebral height. The anterior height (Ha), middle height (Hm), posterior height (Hp), and vertebral height ratios (VHRs) (Ha/Hp and Hm/Hp) were measured from T1 to L2 using an AI system. The VHR is the ratio of Ha to Hp or the ratio of Hm to Hp of the vertebrae, which can reflect the shape of the anterior wedge and biconcave vertebrae. Changes in these parameters, particularly the VHR, were analysed at different vertebral levels in different age and sex groups. The results of the AI methods were highly consistent and correlated with manual measurements. The Pearson's correlation coefficients were 0.855, 0.919, and 0.846, respectively. The trend of VHRs showed troughs at T7 and T11 and a peak at T9; however, Hm/Hp showed slight fluctuations. Regarding the VHR, significant sex differences were found at L1 and L2 in all age bands. This innovative study focuses on vertebral morphology for opportunistic analysis in the mainland Chinese population and the distribution tendency of vertebral morphology with ageing using a chest LDCT aided by an AI system based on 3D U-Net vertebral segmentation technology. The AI system demonstrates the potential to automatically perform opportunistic vertebral morphology analyses using LDCT scans obtained during lung cancer screening. We advocate the use of age-, sex-, and vertebral level-specific criteria for the morphometric evaluation of vertebral osteoporotic fractures for a more accurate diagnosis of vertebral fractures and spinal pathologies.

The implementation of artificial intelligence (AI) in health care is gaining popularity. Many publications describe powerful AI-enabled algorithms. Yet there's only scarce evidence for measurable value in terms of patient outcomes, clinical decision-making or socio-economic impact. Our aim was to investigate the significance of AI in the emergency treatment of wrist trauma patients.

Two groups of physicians were confronted with twenty realistic cases of wrist trauma patients and had to find the correct diagnosis and provide a treatment recommendation. One group was assisted by an AI-enabled application which detects and localizes distal radius fractures (DRF) with near-to-perfect precision while the other group had no help. Primary outcome measurement was diagnostic accuracy. Secondary outcome measurements were required time, number of added CT scans and senior consultations, correctness of the treatment, subjective and objective stress levels.

The AI-supported group was able to make a diagnosis without support (no additional CT, no senior consultation) in significantly more of the cases than the control group (75% vs. 52%, p = 0.003). The AI-enhanced group detected DRF with superior sensitivity (1.00 vs. 0.96, p = 0.06) and specificity (0.99 vs. 0.93, p = 0.17), used significantly less additional CT scans to reach the correct diagnosis (14% vs. 28%, p = 0.02) and was subjectively significantly less stressed (p = 0.05).

The results indicate that physicians can diagnose wrist trauma more accurately and faster when aided by an AI-tool that lessens the need for extra diagnostic procedures. The AI-tool also seems to lower physicians' stress levels while examining cases. We anticipate that these benefits will be amplified in larger studies as skepticism towards the new technology diminishes.

This study evaluated the performance of the YOLOv5 deep learning model in detecting different mandibular fracture types in panoramic images.

The dataset of panoramic radiographs with mandibular fractures was divided into training, validation, and testing sets, with 60%, 20%, and 20% of the images, respectively. An equal number of control images without fractures were also distributed among the datasets. The YOLOv5 algorithm was trained to detect six mandibular fracture types based on the anatomical location including symphysis, body, angle, ramus, condylar neck, and condylar head. Performance metrics of accuracy, precision, sensitivity (recall), specificity, dice coefficient (F1 score), and area under the curve (AUC) were calculated for each class.

A total of 498 panoramic images containing 673 fractures were collected. The accuracy was highest in detecting body (96.21%) and symphysis (95.87%), and was lowest in angle (90.51%) fractures. The highest and lowest precision values were observed in detecting symphysis (95.45%) and condylar head (63.16%) fractures, respectively. The sensitivity was highest in the body (96.67%) fractures and was lowest in the condylar head (80.00%) and condylar neck (81.25%) fractures. The highest specificity was noted in symphysis (98.96%), body (96.08%), and ramus (96.04%) fractures, respectively. The dice coefficient and AUC were highest in detecting body fractures (0.921 and 0.942, respectively), and were lowest in detecting condylar head fractures (0.706 and 0.812, respectively).

The trained algorithm achieved promising results in detecting most fracture types, particularly in body and symphysis regions indicating machine learning potential as a diagnostic aid for clinicians.

In spinal revision surgery, previous pedicle screws (PS) may need to be replaced with new implants. Failure to accurately identify the brand of PS-based instrumentation preoperatively may increase the risk of perioperative complications. This study aimed to develop and validate an optimal deep learning (DL) model to identify the brand of PS-based instrumentation on plain radiographs of spine (PRS) using anteroposterior (AP) and lateral images.

A total of 529 patients who received PS-based instrumentation from seven manufacturers were enrolled in this retrospective study. The postoperative PRS were gathered as ground truths. The training, validation, and testing datasets contained 338, 85, and 106 patients, respectively. YOLOv5 was used to crop out the screws' trajectory, and the EfficientNet-b0 model was used to develop single models (AP, Lateral, Merge, and Concatenated) based on the different PRS images. The ensemble models were different combinations of the single models. Primary outcomes were the models' performance in accuracy, sensitivity, precision, F1-score, kappa value, and area under the curve (AUC). Secondary outcomes were the relative performance of models versus human readers and external validation of the DL models.

The Lateral model had the most stable performance among single models. The discriminative performance was improved by the ensemble method. The AP + Lateral ensemble model had the most stable performance, with an accuracy of 0.9434, F1 score of 0.9388, and AUC of 0.9834. The performance of the ensemble models was comparable to that of experienced orthopedic surgeons and superior to that of inexperienced orthopedic surgeons. External validation revealed that the Lat + Concat ensemble model had the best accuracy (0.9412).

The DL models demonstrated stable performance in identifying the brand of PS-based instrumentation based on AP and/or lateral images of PRS, which may assist orthopedic spine surgeons in preoperative revision planning in clinical practice.

Femur fractures are a significant worldwide public health concern that affects patients as well as their families because of their high frequency, morbidity, and mortality. When employing computer-aided diagnostic (CAD) technologies, promising results have been shown in the efficiency and accuracy of fracture classification, particularly with the growing use of Deep Learning (DL) approaches. Nevertheless, the complexity is further increased by the need to collect enough input data to train these algorithms and the challenge of interpreting the findings. By improving on the results of the most recent deep learning-based Arbeitsgemeinschaft für Osteosynthesefragen and Orthopaedic Trauma Association (AO/OTA) system classification of femur fractures, this study intends to support physicians in making correct and timely decisions regarding patient care. A state-of-the-art architecture, YOLOv8, was used and refined while paying close attention to the interpretability of the model. Furthermore, data augmentation techniques were involved during preprocessing, increasing the dataset samples through image processing alterations. The fine-tuned YOLOv8 model achieved remarkable results, with 0.9 accuracy, 0.85 precision, 0.85 recall, and 0.85 F1-score, computed by averaging the values among all the individual classes for each metric. This study shows the proposed architecture's effectiveness in enhancing the AO/OTA system's classification of femur fractures, assisting physicians in making prompt and accurate diagnoses.

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.

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.

Fracture detection is one of the most commonly used and studied aspects of artificial intelligence (AI) in medicine. In this systematic review and meta-analysis, we aimed to summarize available literature and data regarding AI performance in fracture detection on plain radiographs and various factors affecting it.

We systematically reviewed studies evaluating AI algorithms in detecting bone fractures in plain radiographs, combined their performance using meta-analysis (a bivariate regression approach), and compared it with that of clinicians. We also analyzed the factors potentially affecting algorithm performance using meta-regression.

Our analysis included 100 studies. In 83 studies with confusion matrices, AI algorithms showed a sensitivity of 91.43% and a specificity of 92.12% (Area under the summary receiver operator curve = 0.968). After adjustment and false discovery rate correction, tibia/fibula (excluding ankle) fractures were associated with higher (7.0%, p=0.004) AI sensitivity, while more recent publications (5.5%, p=0.003) and Xception architecture (6.6%, p<0.001) were associated with higher specificity. Clinicians and AI showed similar specificity in fracture identification, although AI leaned to higher sensitivity (7.6%, p=0.07). Radiologists, on the other hand, were more specific than AI overall and in several subgroups, and more sensitive to hip fractures before FDR correction.

Currently available AI aids could result in a significant improvement in care where radiologists are not readily available. Moreover, identifying factors affecting algorithm performance could guide AI development teams in their process of optimizing their products.

We created an infrastructure for no code machine learning (NML) platform for non-programming physicians to create NML model. We tested the platform by creating an NML model for classifying radiographs for the presence and absence of clavicle fractures.

Our IRB-approved retrospective study included 4135 clavicle radiographs from 2039 patients (mean age 52 ± 20 years, F:M 1022:1017) from 13 hospitals. Each patient had two-view clavicle radiographs with axial and anterior-posterior projections. The positive radiographs had either displaced or non-displaced clavicle fractures. We configured the NML platform to automatically retrieve the eligible exams using the series' unique identification from the hospital virtual network archive via web access to DICOM Objects. The platform trained a model until the validation loss plateaus. Once the testing was complete, the platform provided the receiver operating characteristics curve and confusion matrix for estimating sensitivity, specificity, and accuracy.

The NML platform successfully retrieved 3917 radiographs (3917/4135, 94.7 %) and parsed them for creating a ML classifier with 2151 radiographs in the training, 100 radiographs for validation, and 1666 radiographs in testing datasets (772 radiographs with clavicle fracture, 894 without clavicle fracture). The network identified clavicle fracture with 90 % sensitivity, 87 % specificity, and 88 % accuracy with AUC of 0.95 (confidence interval 0.94-0.96).

A NML platform can help physicians create and test machine learning models from multicenter imaging datasets such as the one in our study for classifying radiographs based on the presence of clavicle fracture.

In the coming years, artificial intelligence (AI) and machine learning will likely give rise to profound changes in the field of emergency medicine, and medicine more broadly. This article discusses these anticipated changes in terms of 3 overlapping yet distinct stages of AI development. It reviews some fundamental concepts in AI and explores their relation to clinical practice, with a focus on emergency medicine. In addition, it describes some of the applications of AI in disease diagnosis, prognosis, and treatment, as well as some of the practical issues that they raise, the barriers to their implementation, and some of the legal and regulatory challenges they create.

The reaserch of artificial intelligence (AI) model for predicting spinal refracture is limited to bone mineral density, X-ray and some conventional laboratory indicators, which has its own limitations. Besides, it lacks specific indicators related to osteoporosis and imaging factors that can better reflect bone quality, such as computed tomography (CT).

To construct a novel predicting model based on bone turn-over markers and CT to identify patients who were more inclined to suffer spine refracture.

CT images and clinical information of 383 patients (training set = 240 cases of osteoporotic vertebral compression fractures (OVCF), validation set = 63, test set = 80) were retrospectively collected from January 2015 to October 2022 at three medical centers. The U-net model was adopted to automatically segment ROI. Three-dimensional (3D) cropping of all spine regions was used to achieve the final ROI regions including 3D_Full and 3D_RoiOnly. We used the Densenet 121-3D model to model the cropped region and simultaneously build a T-NIPT prediction model. Diagnostics of deep learning models were assessed by constructing ROC curves. We generated calibration curves to assess the calibration performance. Additionally, decision curve analysis (DCA) was used to assess the clinical utility of the predictive models.

The performance of the test model is comparable to its performance on the training set (dice coefficients of 0.798, an mIOU of 0.755, an SA of 0.767, and an OS of 0.017). Univariable and multivariable analysis indicate that T_P1NT was an independent risk factor for refracture. The performance of predicting refractures in different ROI regions showed that 3D_Full model exhibits the highest calibration performance, with a Hosmer-Lemeshow goodness-of-fit (HL) test statistic exceeding 0.05. The analysis of the training and test sets showed that the 3D_Full model, which integrates clinical and deep learning results, demonstrated superior performance with significant improvement (p-value < 0.05) compared to using clinical features independently or using only 3D_RoiOnly.

T_P1NT was an independent risk factor of refracture. Our 3D-FULL model showed better performance in predicting high-risk population of spine refracture than other models and junior doctors do. This model can be applicable to real-world translation due to its automatic segmentation and detection.