Clinical application of artificial intelligence-assisted diagnosis using anteroposterior pelvic radiographs in children with developmental dysplasia of the hip

Artificial intelligence in musculoskeletal imaging: realistic clinical applications in the next decade

This article will provide a perspective review of the most extensively investigated deep learning (DL) applications for musculoskeletal disease detection that have the best potential to translate into routine clinical practice over the next decade. Deep learning methods for detecting fractures, estimating pediatric bone age, calculating bone measurements such as lower extremity alignment and Cobb angle, and grading osteoarthritis on radiographs have been shown to have high diagnostic performance with many of these applications now commercially available for use in clinical practice. Many studies have also documented the feasibility of using DL methods for detecting joint pathology and characterizing bone tumors on magnetic resonance imaging (MRI). However, musculoskeletal disease detection on MRI is difficult as it requires multi-task, multi-class detection of complex abnormalities on multiple image slices with different tissue contrasts. The generalizability of DL methods for musculoskeletal disease detection on MRI is also challenging due to fluctuations in image quality caused by the wide variety of scanners and pulse sequences used in routine MRI protocols. The diagnostic performance of current DL methods for musculoskeletal disease detection must be further evaluated in well-designed prospective studies using large image datasets acquired at different institutions with different imaging parameters and imaging hardware before they can be fully implemented in clinical practice. Future studies must also investigate the true clinical benefits of current DL methods and determine whether they could enhance quality, reduce error rates, improve workflow, and decrease radiologist fatigue and burnout with all of this weighed against the costs.

Comprehensive review of deep learning in orthopaedics: Applications, challenges, trustworthiness, and fusion

Deep learning (DL) in orthopaedics has gained significant attention in recent years. Previous studies have shown that DL can be applied to a wide variety of orthopaedic tasks, including fracture detection, bone tumour diagnosis, implant recognition, and evaluation of osteoarthritis severity. The utilisation of DL is expected to increase, owing to its ability to present accurate diagnoses more efficiently than traditional methods in many scenarios. This reduces the time and cost of diagnosis for patients and orthopaedic surgeons. To our knowledge, no exclusive study has comprehensively reviewed all aspects of DL currently used in orthopaedic practice. This review addresses this knowledge gap using articles from Science Direct, Scopus, IEEE Xplore, and Web of Science between 2017 and 2023. The authors begin with the motivation for using DL in orthopaedics, including its ability to enhance diagnosis and treatment planning. The review then covers various applications of DL in orthopaedics, including fracture detection, detection of supraspinatus tears using MRI, osteoarthritis, prediction of types of arthroplasty implants, bone age assessment, and detection of joint-specific soft tissue disease. We also examine the challenges for implementing DL in orthopaedics, including the scarcity of data to train DL and the lack of interpretability, as well as possible solutions to these common pitfalls. Our work highlights the requirements to achieve trustworthiness in the outcomes generated by DL, including the need for accuracy, explainability, and fairness in the DL models. We pay particular attention to fusion techniques as one of the ways to increase trustworthiness, which have also been used to address the common multimodality in orthopaedics. Finally, we have reviewed the approval requirements set forth by the US Food and Drug Administration to enable the use of DL applications. As such, we aim to have this review function as a guide for researchers to develop a reliable DL application for orthopaedic tasks from scratch for use in the market.

The diagnostic value of artificial intelligence-assisted imaging for developmental dysplasia of the hip: a systematic review and meta-analysis

OBJECTIVE

To clarify the efficacy of artificial intelligence (AI)-assisted imaging in the diagnosis of developmental dysplasia of the hip (DDH) through a meta-analysis.

METHODS

Relevant literature on AI for early DDH diagnosis was searched in PubMed, Web of Science, Embase, and The Cochrane Library databases until April 4, 2024. The Quality Assessment of Diagnostic Accuracy Studies tool was used to assess the quality of included studies. Revman5.4 and StataSE-64 software were used to calculate the combined sensitivity, specificity, AUC value, and DOC value of AI-assisted imaging for DDH diagnosis.

RESULTS

The meta-analysis included 13 studies (6 prospective and 7 retrospective) with 28 AI models and a total of 10,673 samples. The summary sensitivity, specificity, AUC value, and DOC value were 99.0% (95% CI: 97.0-100.0%), 94.0% (95% CI: 89.0-96.0%), 99.0% (95% CI: 98.0-100.0%), and 1342 (95% CI: 469-3842), respectively.

CONCLUSION

AI-assisted imaging demonstrates high diagnostic efficacy for DDH detection, improving the accuracy of early DDH imaging examination. More prospective studies are needed to further confirm the value of AI-assisted imaging for early DDH diagnosis.

Bimodal machine learning model for unstable hips in infants: integration of radiographic images with automatically-generated clinical measurements

Bimodal convolutional neural networks (CNNs) are frequently combined with patient information or several medical images to enhance the diagnostic performance. However, the technologies that integrate automatically generated clinical measurements within the images are scarce. Hence, we developed a bimodal model that produced automatic algorithm for clinical measurement (aaCM) from radiographic images and integrated the model with CNNs. In this multicenter research project, the diagnostic performance of the model was investigated with 813 radiographic hip images of infants at risk of developmental dysplasia of the hips (232 and 581 images of unstable and stable hips, respectively), with the ground truth defined by provocative examinations. The results indicated that the accuracy of aaCM was equal or higher than that of specialists, and the bimodal model showed better diagnostic performance than LightGBM, XGBoost, SVM, and single CNN models. aaCM can provide expert's knowledge in a high level, and our proposed bimodal model has better performance than the state-of-art models.

Does the Information Quality of ChatGPT Meet the Requirements of Orthopedics and Trauma Surgery?

BACKGROUND

The integration of artificial intelligence (AI) in medicine, particularly through AI-based language models like ChatGPT, offers a promising avenue for enhancing patient education and healthcare delivery. This study aims to evaluate the quality of medical information provided by Chat Generative Pre-trained Transformer (ChatGPT) regarding common orthopedic and trauma surgical procedures, assess its limitations, and explore its potential as a supplementary source for patient education.

METHODS

Using the GPT-3.5-Turbo version of ChatGPT, simulated patient information was generated for 20 orthopedic and trauma surgical procedures. The study utilized standardized information forms as a reference for evaluating ChatGPT's responses. The accuracy and quality of the provided information were assessed using a modified DISCERN instrument, and a global medical assessment was conducted to categorize the information's usefulness and reliability.

RESULTS

ChatGPT mentioned an average of 47% of relevant keywords across procedures, with a variance in the mention rate between 30.5% and 68.6%. The average modified DISCERN (mDISCERN) score was 2.4 out of 5, indicating a moderate to low quality of information. None of the ChatGPT-generated fact sheets were rated as "very useful," with 45% deemed "somewhat useful," 35% "not useful," and 20% classified as "dangerous." A positive correlation was found between higher mDISCERN scores and better physician ratings, suggesting that information quality directly impacts perceived utility.

CONCLUSION

While AI-based language models like ChatGPT hold significant promise for medical education and patient care, the current quality of information provided in the field of orthopedics and trauma surgery is suboptimal. Further development and refinement of AI sources and algorithms are necessary to improve the accuracy and reliability of medical information. This study underscores the need for ongoing research and development in AI applications in healthcare, emphasizing the critical role of accurate, high-quality information in patient education and informed consent processes.

Deep learning-based screening tool for rotator cuff tears on shoulder radiography

BACKGROUND

Early diagnosis of rotator cuff tears is essential for appropriate and timely treatment. Although radiography is the most used technique in clinical practice, it is difficult to accurately rule out rotator cuff tears as an initial imaging diagnostic modality. Deep learning-based artificial intelligence has recently been applied in medicine, especially diagnostic imaging. This study aimed to develop a deep learning algorithm as a screening tool for rotator cuff tears based on radiography.

METHODS

We used 2803 shoulder radiographs of the true anteroposterior view to develop the deep learning algorithm. Radiographs were labeled 0 and 1 as intact or low-grade partial-thickness rotator cuff tears and high-grade partial or full-thickness rotator cuff tears, respectively. The diagnosis of rotator cuff tears was determined based on arthroscopic findings. The diagnostic performance of the deep learning algorithm was assessed by calculating the area under the curve (AUC), sensitivity, negative predictive value (NPV), and negative likelihood ratio (LR-) of test datasets with a cutoff value of expected high sensitivity determination based on validation datasets. Furthermore, the diagnostic performance for each rotator cuff tear size was evaluated.

RESULTS

The AUC, sensitivity, NPV, and LR- with expected high sensitivity determination were 0.82, 84/92 (91.3%), 102/110 (92.7%), and 0.16, respectively. The sensitivity, NPV, and LR- for full-thickness rotator cuff tears were 69/73 (94.5%), 102/106 (96.2%), and 0.10, respectively, while the diagnostic performance for partial-thickness rotator cuff tears was low at 15/19 (78.9%), NPV of 102/106 (96.2%) and LR- of 0.39.

CONCLUSIONS

Our algorithm had a high diagnostic performance for full-thickness rotator cuff tears. The deep learning algorithm based on shoulder radiography helps screen rotator cuff tears by setting an appropriate cutoff value.

LEVEL OF EVIDENCE

Level III: Diagnostic Study.

Automatic and human level Graf's type identification for detecting developmental dysplasia of the hip

BACKGROUND

Developmental dysplasia of the hip (DDH) is a common congenital disorder that may lead to hip dislocation and requires surgical intervention if left untreated. Ultrasonography is the preferred method for DDH screening; however, the lack of experienced operators impedes its application in universal neonatal screening.

METHODS

We developed a deep neural network tool to automatically register the five keypoints that mark important anatomical structures of the hip and provide a reference for measuring alpha and beta angles following Graf's guidelines, which is an ultrasound classification system for DDH in infants. Two-dimensional (2D) ultrasonography images were obtained from 986 neonates aged 0-6 months. A total of 2406 images from 921 patients were labeled with ground truth keypoints by senior orthopedists.

RESULTS

Our model demonstrated precise keypoint localization. The mean absolute error was approximately 1 mm, and the derived alpha angle measurement had a correlation coefficient of R = 0.89 between the model and ground truth. The model achieved an area under the receiver operating characteristic curve of 0.937 and 0.974 for classifying alpha <60° (abnormal hip) and <50° (dysplastic hip), respectively. On average, the experts agreed with 96% of the inferenced images, and the model could generalize its prediction on newly collected images with a correlation coefficient higher than 0.85.

CONCLUSIONS

Precise localization and highly correlated performance metrics suggest that the model can be an efficient tool for assisting DDH diagnosis in clinical settings.

Automated decision support for Hallux Valgus treatment options using anteroposterior foot radiographs

BACKGROUND

Assessment of the potential utility of deep learning with subsequent image analysis to automate the measurement of hallux valgus and intermetatarsal angles from radiographs to serve as a preoperative aid in establishing hallux valgus severity for clinical decision-making.

AIM

To investigate the accuracy of automated measurements of angles of hallux valgus from radiographs for further integration with the preoperative planning process.

METHODS

The data comprises 265 consecutive digital anteroposterior weightbearing foot radiographs. 181 radiographs were utilized for training (161) and validating (20) a U-Net neural network to achieve a mean Sørensen–Dice index > 97% on bone segmentation. 84 test radiographs were used for manual (computer assisted) and automated measurements of hallux valgus severity determined by hallux valgus (HVA) and intermetatarsal angles (IMA). The reliability of manual and computer-based measurements was calculated using the interclass correlation coefficient (ICC) and standard error of measurement (SEM). Inter- and intraobserver reliability coefficients were also compared. An operative treatment recommendation was then applied to compare results between automated and manual angle measurements.

RESULTS

Very high reliability was achieved for HVA and IMA between the manual measurements of three independent clinicians. For HVA, the ICC between manual measurements was 0.96-0.99. For IMA, ICC was 0.78-0.95. Comparing manual against automated computer measurement, the reliability was high as well. For HVA, absolute agreement ICC and consistency ICC were 0.97, and SEM was 0.32. For IMA, absolute agreement ICC was 0.75, consistency ICC was 0.89, and SEM was 0.21. Additionally, a strong correlation (0.80) was observed between our approach and traditional clinical adjudication for preoperative planning of hallux valgus, according to an operative treatment algorithm proposed by EFORT.

CONCLUSION

The proposed automated, artificial intelligence assisted determination of hallux valgus angles based on deep learning holds great potential as an accurate and efficient tool, with comparable accuracy to manual measurements by expert clinicians. Our approach can be effectively implemented in clinical practice to determine the angles of hallux valgus from radiographs, classify the deformity severity, streamline preoperative decision-making prior to corrective surgery.

Assessing Acetabular Index Angle in Infants: A Deep Learning-Based Novel Approach

Developmental dysplasia of the hip (DDH) is a disorder characterized by abnormal hip development that frequently manifests in infancy and early childhood. Preventing DDH from occurring relies on a timely and accurate diagnosis, which requires careful assessment by medical specialists during early X-ray scans. However, this process can be challenging for medical personnel to achieve without proper training. To address this challenge, we propose a computational framework to detect DDH in pelvic X-ray imaging of infants that utilizes a pipelined deep learning-based technique consisting of two stages: instance segmentation and keypoint detection models to measure acetabular index angle and assess DDH affliction in the presented case. The main aim of this process is to provide an objective and unified approach to DDH diagnosis. The model achieved an average pixel error of 2.862 ± 2.392 and an error range of 2.402 ± 1.963° for the acetabular angle measurement relative to the ground truth annotation. Ultimately, the deep-learning model will be integrated into the fully developed mobile application to make it easily accessible for medical specialists to test and evaluate. This will reduce the burden on medical specialists while providing an accurate and explainable DDH diagnosis for infants, thereby increasing their chances of successful treatment and recovery.

A novel approach for screening standard anteroposterior pelvic radiographs in children

Anteroposterior pelvic radiography is the first-line imaging modality for diagnosing developmental dysplasia of the hip (DDH). Nonstandard radiographs with pelvic malposition make the correct diagnosis of DDH challenging. However, as the only method available for screening standard pelvic radiographs, traditional manual assessment is relatively laborious and potentially erroneous. We retrospectively collected 3,247 pelvic radiographs. There were 2,887 radiographs randomly selected to train and optimize the AI model. Then 362 radiographs were used to test the model's diagnostic performance. Its diagnostic accuracy was assessed using receiver operating characteristic (ROC) curves and measurement consistency using Bland-Altman plots. In 362 radiographs, the AI model's area under ROC curves, accuracy, sensitivity, and specificity for quality assessment was 0.993, 99.4% (360/362), 98.6% (138/140), and 100.0% (222/222), respectively. Compared with clinicians, the 95% limits of agreement (Bland-Altman analysis) for pelvic tilt index (PTI) and pelvic rotation index (PRI), as determined by the model, were -0.052-0.072 and -0.088-0.055, respectively.

CONCLUSIONS

The artificial intelligence-assisted method was more efficient and highly consistent with clinical experts. This method can be used for real-time validation of the quality of pelvic radiographs in current picture archiving and communications systems (PACS).

WHAT IS KNOWN

• Nonstandard pediatric radiographs with pelvic malposition make the correct diagnosis of developmental dysplasia of the hip (DDH) challenging. • Traditional manual assessment remains the only method available for screening standard pediatric pelvic radiographs, which is relatively laborious and potentially erroneous.

WHAT IS NEW

• This study proposed an artificial intelligence-assisted model to assess the quality of pediatric pelvic radiographs accurately and efficiently. • We recommend the integration of the model into current picture archiving and communications systems (PACS) for real-time screening of standard pediatric pelvic radiographs.

Predicting the Risk of Total Hip Replacement by Using A Deep Learning Algorithm on Plain Pelvic Radiographs: Diagnostic Study

BACKGROUND

Total hip replacement (THR) is considered the gold standard of treatment for refractory degenerative hip disorders. Identifying patients who should receive THR in the short term is important. Some conservative treatments, such as intra-articular injection administered a few months before THR, may result in higher odds of arthroplasty infection. Delayed THR after functional deterioration may result in poorer outcomes and longer waiting times for those who have been flagged as needing THR. Deep learning (DL) in medical imaging applications has recently obtained significant breakthroughs. However, the use of DL in practical wayfinding, such as short-term THR prediction, is still lacking.

OBJECTIVE

In this study, we will propose a DL-based assistant system for patients with pelvic radiographs to identify the need for THR within 3 months.

METHODS

We developed a convolutional neural network-based DL algorithm to analyze pelvic radiographs, predict the hip region of interest (ROI), and determine whether or not THR is required. The data set was collected from August 2008 to December 2017. The images included 3013 surgical hip ROIs that had undergone THR and 1630 nonsurgical hip ROIs. The images were split, using split-sample validation, into training (n=3903, 80%), validation (n=476, 10%), and testing (n=475, 10%) sets to evaluate the algorithm performance.

RESULTS

The algorithm, called SurgHipNet, yielded an area under the receiver operating characteristic curve of 0.994 (95% CI 0.990-0.998). The accuracy, sensitivity, specificity, and F1-score of the model were 0.977, 0.920, 0932, and 0.944, respectively.

CONCLUSIONS

The proposed approach has demonstrated that SurgHipNet shows the ability and potential to provide efficient support in clinical decision-making; it can assist physicians in promptly determining the optimal timing for THR.

Deep Learning Approach for Diagnosing Early Osteonecrosis of the Femoral Head Based on Magnetic Resonance Imaging

BACKGROUND

The diagnosis of early osteonecrosis of the femoral head (ONFH) based on magnetic resonance imaging (MRI) is challenging due to variability in the surgeon's experience level. This study developed an MRI-based deep learning system to detect early ONFH and evaluated its feasibility in the clinic.

METHODS

We retrospectively evaluated clinical MRIs of the hips that were performed in our institution from January 2019 to June 2022 and collected all MRIs diagnosed with early ONFH. An advanced convolutional neural network (CNN) was trained and optimized; then, the diagnostic performance of the CNN was evaluated according to its accuracy, sensitivity, and specificity. We also further compared the CNN's performance with that of orthopaedic surgeons.

RESULTS

Overall, 11,061 images were retrospectively included in the present study and were divided into three datasets with ratio 7:2:1. The area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity of the CNN model for identifying early ONFH were 0.98, 98.4, 97.6, and 98.6%, respectively. In our review panel, the averaged accuracy, sensitivity, and specificity for identifying ONFH were 91.7, 87.0, and 94.1% for attending orthopaedic surgeons; 87.1, 84.0, and 89.3% for resident orthopaedic surgeons; and 97.1, 96.0, and 97.9% for deputy chief orthopaedic surgeons, respectively.

CONCLUSION

The deep learning system showed a comparable performance to that of deputy chief orthopaedic surgeons in identifying early ONFH. The success of deep learning diagnosis of ONFH might be conducive to assisting less-experienced surgeons, especially in large-scale medical imaging screening and community scenarios lacking consulting experts.

An AI-Based Image Quality Control Framework for Knee Radiographs

Image quality control (QC) is crucial for the accurate diagnosis of knee diseases using radiographs. However, the manual QC process is subjective, labor intensive, and time-consuming. In this study, we aimed to develop an artificial intelligence (AI) model to automate the QC procedure typically performed by clinicians. We proposed an AI-based fully automatic QC model for knee radiographs using high-resolution net (HR-Net) to identify predefined key points in images. We then performed geometric calculations to transform the identified key points into three QC criteria, namely, anteroposterior (AP)/lateral (LAT) overlap ratios and LAT flexion angle. The proposed model was trained and validated using 2212 knee plain radiographs from 1208 patients and an additional 1572 knee radiographs from 753 patients collected from six external centers for further external validation. For the internal validation cohort, the proposed AI model and clinicians showed high intraclass consistency coefficients (ICCs) for AP/LAT fibular head overlap and LAT knee flexion angle of 0.952, 0.895, and 0.993, respectively. For the external validation cohort, the ICCs were also high, with values of 0.934, 0.856, and 0.991, respectively. There were no significant differences between the AI model and clinicians in any of the three QC criteria, and the AI model required significantly less measurement time than clinicians. The experimental results demonstrated that the AI model performed comparably to clinicians and required less time. Therefore, the proposed AI-based model has great potential as a convenient tool for clinical practice by automating the QC procedure for knee radiographs.

Automatic detection of early osteonecrosis of the femoral head from various hip pathologies using deep convolutional neural network: a multi-centre study

PURPOSE

The aim of this study was to develop a deep convolutional neural network (DCNN) for detecting early osteonecrosis of the femoral head (ONFH) from various hip pathologies and evaluate the feasibility of its application.

METHODS

We retrospectively reviewed and annotated hip magnetic resonance imaging (MRI) of ONFH patients from four participated institutions and constructed a multi-centre dataset to develop the DCNN system. The diagnostic performance of the DCNN in the internal and external test datasets was calculated, including area under the receiver operating characteristic curve (AUROC), accuracy, precision, recall, and F1 score, and gradient-weighted class activation mapping (Grad-CAM) technique was used to visualize its decision-making process. In addition, a human-machine comparison trial was performed.

RESULTS

Overall, 11,730 hip MRI segments from 794 participants were used to develop and optimize the DCNN system. The AUROC, accuracy, and precision of the DCNN in internal test dataset were 0.97 (95% CI, 0.93-1.00), 96.6% (95% CI: 93.0-100%), and 97.6% (95% CI: 94.6-100%), and in external test dataset, they were 0.95 (95% CI, 0.91- 0.99), 95.2% (95% CI, 91.1-99.4%), and 95.7% (95% CI, 91.7-99.7%). Compared with attending orthopaedic surgeons, the DCNN showed superior diagnostic performance. The Grad-CAM demonstrated that the DCNN placed focus on the necrotic region.

CONCLUSION

Compared with clinician-led diagnoses, the developed DCNN system is more accurate in diagnosing early ONFH, avoiding empirical dependence and inter-reader variability. Our findings support the integration of deep learning systems into real clinical settings to assist orthopaedic surgeons in diagnosing early ONFH.

Can an artificial intelligence powered software reliably assess pelvic radiographs?

PURPOSE

Despite advances of three-dimensional imaging pelvic radiographs remain the cornerstone in the evaluation of the hip joint. However, large inter- and intra-rater variabilities were reported due to subjective landmark setting. Artificial intelligence (AI)-powered software applications could improve the reproducibility of pelvic radiograph evaluation by providing standardized measurements. The aim of this study was to evaluate the reliability and agreement of a newly developed AI algorithm for the evaluation of pelvic radiographs.

METHODS

Three-hundred pelvic radiographs from 280 patients with different degrees of acetabular coverage and osteoarthritis (Tönnis Grade 0 to 3) were evaluated. Reliability and agreement between manual measurements and the outputs of the AI software were assessed for the lateral-center-edge (LCE) angle, neck-shaft angle, sharp angle, acetabular index, as well as the femoral head extrusion index.

RESULTS

The AI software provided reliable results in 94.3% (283/300). The ICC values ranged between 0.73 for the Acetabular Index to 0.80 for the LCE Angle. Agreement between readers and AI outputs, given by the standard error of measurement (SEM), was good for hips with normal coverage (LCE-SEM: 3.4°) and no osteoarthritis (LCE-SEM: 3.3°) and worse for hips with undercoverage (LCE-SEM: 5.2°) or severe osteoarthritis (LCE-SEM: 5.1°).

CONCLUSION

AI-powered applications are a reliable alternative to manual evaluation of pelvic radiographs. While being accurate for patients with normal acetabular coverage and mild signs of osteoarthritis, it needs improvement in the evaluation of patients with hip dysplasia and severe osteoarthritis.

Clinical thought-based software for diagnosing developmental dysplasia of the hip on pediatric pelvic radiographs

Background

The common methods of radiographic diagnosis of developmental dysplasia of the hip (DDH) include measuring hip parameters and quantifying the degree of hip dislocation. However, clinical thought-based analysis of hip parameters may be a more effective way to achieve expert-like diagnoses of DDH. This study aims to develop a diagnostic strategy-based software for pediatric DDH and validate its clinical feasibility.

Methods

In total, 543 anteroposterior pelvic radiographs were retrospectively collected from January 2017 to December 2021. Two independent clinicians measured four diagnostic indices to compare the diagnoses made by the software and conventional manual method. The diagnostic accuracy was evaluated using the receiver operator characteristic (ROC) curves and confusion matrix, and the consistency of parametric measurements was assessed using Bland-Altman plots.

Results

In 543 cases (1,086 hips), the area under the curve, accuracy, sensitivity, and specificity of the software for diagnosing DDH were 0.988-0.994, 99.08%-99.72%, 98.07%-100.00%, and 99.59%, respectively. Compared with the expert panel, the Bland-Altman 95% limits of agreement for the acetabular index, as determined by the software, were -2.09°-2.91° (junior orthopedist) and -1.98°-2.72° (intermediate orthopedist). As for the lateral center-edge angle, the 95% limits were -3.68°-5.28° (junior orthopedist) and -2.94°-4.59° (intermediate orthopedist).

Conclusions

The software can provide expert-like analysis of pelvic radiographs and obtain the radiographic diagnosis of pediatric DDH with great consistency and efficiency. Its initial success lays the groundwork for developing a full-intelligent comprehensive diagnostic system of DDH.

Comparison of Transfer Learning Models in Pelvic Tilt and Rotation Measurement in Pediatric Anteroposterior Pelvic Radiographs

The rotation and tilt of the pelvis during anteroposterior pelvic radiography can lead to misdiagnosis of developmental dysplasia of the hip (DDH) in children. At present, no method exists for accurately and conveniently measuring the precise rotation and tilt angles of pelvic on radiographs. The objective of this study was to develop several rotation and tilt measurement models using transfer learning and digital reconstructed radiographs (DRRs), and to compare their performances on pelvic radiographs. Based on the inclusion criteria, 30 of 92 children who underwent 3D hip CT scans at Xijing Hospital from 2015 to 2020 were included in the study. Using DRR techniques, radiographs were generated by rotating and tilting the pelvis in CT datasets at - 12 to 12° (projected every 3°) and were randomized to a 2:1:1 ratio of training dataset, validation dataset, and test dataset. Five pre-trained networks, including VGG16, Xception, VGG19, ResNet50 and InceptionV3 were used to develop pelvic rotation measurement models and tilt measurement models, and these models were trained with training dataset. The callback function was used during the training to slow down the learning rate when learning was stalled. Then, the validation set was used to optimize each model and compare their performances. At last, we tested the final performances of optimal rotation measurement model and optimal tilt measurement model on test dataset. The mean absolute error (MAE) was employed to assess the performance of the models. A total of 2430 pelvic DRRs were collected based on 30 CT datasets. Among 5 pre-trained transfer learning models, VGG16-Tilt achieved the best tilt prediction performance at the same BS and different LR. VGG16-Tilt model achieved its best performance on validation set at LR = 0.001 and BS = 4, and the final MAE on the test set was 0.5250°. In terms of rotation prediction, VGG16-Rotation also achieved the best performance, and it achieved its best performance on validation set at LR = 0.002 and BS = 8. The final MAE of VGG16-Rotation on the test set was 1.0731°. Pretrained transfer learning models worked well in predicting tilt and rotation angles of the pelvis on radiographs in children. Among them, VGG16-Tilt and VGG16-Rotation had the best effect in dealing with such problems despite their simple structures. These models deployed in devices can give orthopedic surgeons a powerful aid in DDH diagnosis.

Anteroposterior pelvic radiograph is not sufficient to confirm hip reduction after conservative treatment of developmental dysplasia of the hip

The purpose of this study was to investigate whether an anteroposterior pelvic radiograph alone is sufficient to confirm hip reduction after conservative treatment or whether MRI could be alternatively performed. A total of 133 children (145 hips) were enrolled. All children were examined by anteroposterior pelvic radiographs and MRI. Three experts interpreted anteroposterior pelvic radiographs and then verified these results on MRI. For patients with inconsistent results between anteroposterior pelvic radiographs and MRI, the continuity of Shenton's line and Calve's line was recorded, and the medial clear space of bilateral hips was measured for unilateral cases. There was complete agreement between the three experts in the interpretation of anteroposterior pelvic radiographs of 111 (76.55%) hips; there was disagreement in the remaining 34 hips, with two experts diagnosing satisfactory reduction in 13 hips and dislocation in 21 hips. Assuming that the judgment of two or more doctors on anteroposterior pelvic radiographs was taken as the final result, 17 hips (11.72%) were misjudged. There was no statistically significant difference between the actual in-position group and the actual dislocation group in terms of the continuity of Shenton's line ( P  = 0.62) and Calve's line ( P  = 0.10) and the medial clear space of bilateral hips ( P  = 0.08). In children less than 1 year of age with developmental dysplasia of the hip treated conservatively, the use of anteroposterior pelvic radiographs alone to judge hip reduction might result in misdiagnosis and missed diagnosis. MRI could be alternatively used to detect hip reduction after conservative treatment, especially when the doctor was not familiar with ultrasound in the presence of plaster.

Current and emerging artificial intelligence applications for pediatric musculoskeletal radiology

Artificial intelligence (AI) is playing an ever-increasing role in radiology (more so in the adult world than in pediatrics), to the extent that there are unfounded fears it will completely take over the role of the radiologist. In relation to musculoskeletal applications of AI in pediatric radiology, we are far from the time when AI will replace radiologists; even for the commonest application (bone age assessment), AI is more often employed in an AI-assist mode rather than an AI-replace or AI-extend mode. AI for bone age assessment has been in clinical use for more than a decade and is the area in which most research has been conducted. Most other potential indications in children (such as appendicular and vertebral fracture detection) remain largely in the research domain. This article reviews the areas in which AI is most prominent in relation to the pediatric musculoskeletal system, briefly summarizing the current literature and highlighting areas for future research. Pediatric radiologists are encouraged to participate as members of the research teams conducting pediatric radiology artificial intelligence research.

Artificial Intelligence in Modern Orthopaedics: Current and Future Applications

➢

With increasing computing power, artificial intelligence (AI) has gained traction in all aspects of health care delivery. Orthopaedics is no exception because the influence of AI technology has become intricately linked with its advancement as evidenced by increasing interest and research.

➢

This review is written for the orthopaedic surgeon to develop a better understanding of the main clinical applications and potential benefits of AI within their day-to-day practice.

➢

A brief and easy-to-understand foundation for what AI is and the different terminology used within the literature is first provided, followed by a summary of the newest research on AI applications demonstrating increased accuracy and convenience in risk stratification, clinical decision-making support, and robotically assisted surgery.

Feasibility study of hallux valgus measurement with a deep convolutional neural network based on landmark detection

OBJECTIVE

To develop a deep learning algorithm based on automatic detection of landmarks that can be used to automatically calculate forefoot imaging parameters from radiographs and test its performance.

MATERIALS AND METHODS

A total of 1023 weight-bearing dorsoplantar (DP) radiographs were included. A total of 776 radiographs were used for training and verification of the model, and 247 radiographs were used for testing the performance of the model. The radiologists manually marked 18 landmarks on each image. By training our model to automatically label these landmarks, 4 imaging parameters commonly used for the diagnosis of hallux valgus could be measured, including the first-second intermetatarsal angle (IMA), hallux valgus angle (HVA), hallux interphalangeal angle (HIA), and distal metatarsal articular angle (DMAA). The reference standard was determined by the radiologists' measurements. The percentage of correct key points (PCK), intragroup correlation coefficient (ICC), Pearson correlation coefficient (r), root mean square error (RMSE), and mean absolute error (MAE) between the predicted value of the model and the reference standard were calculated. The Bland-Altman plot shows the mean difference and 95% LoA.

RESULTS

The PCK was 84-99% at the 3-mm threshold. The correlation between the observed and predicted values of the four angles was high (ICC: 0.89-0.96, r: 0.81-0.97, RMSE: 3.76-6.77, MAE: 3.22-5.52). However, there was a systematic error between the model predicted value and the reference standard (the mean difference ranged from - 3.00 to - 5.08°, and the standard deviation ranged from 2.25 to 4.47°).

CONCLUSION

Our model can accurately identify landmarks, but there is a certain amount of error in the angle measurement, which needs further improvement.

Multi-landmark environment analysis with reinforcement learning for pelvic abnormality detection and quantification

Morphological abnormalities of the femoroacetabular (hip) joint are among the most common human musculoskeletal disorders and often develop asymptomatically at early easily treatable stages. In this paper, we propose an automated framework for landmark-based detection and quantification of hip abnormalities from magnetic resonance (MR) images. The framework relies on a novel idea of multi-landmark environment analysis with reinforcement learning. In particular, we merge the concepts of the graphical lasso and Morris sensitivity analysis with deep neural networks to quantitatively estimate the contribution of individual landmark and landmark subgroup locations to the other landmark locations. Convolutional neural networks for image segmentation are utilized to propose the initial landmark locations, and landmark detection is then formulated as a reinforcement learning (RL) problem, where each landmark-agent can adjust its position by observing the local MR image neighborhood and the locations of the most-contributive landmarks. The framework was validated on T1-, T2- and proton density-weighted MR images of 260 patients with the aim to measure the lateral center-edge angle (LCEA), femoral neck-shaft angle (NSA), and the anterior and posterior acetabular sector angles (AASA and PASA) of the hip, and derive the quantitative abnormality metrics from these angles. The framework was successfully tested using the UNet and feature pyramid network (FPN) segmentation architectures for landmark proposal generation, and the deep Q-network (DeepQN), deep deterministic policy gradient (DDPG), twin delayed deep deterministic policy gradient (TD3), and actor-critic policy gradient (A2C) RL networks for landmark position optimization. The resulting overall landmark detection error of 1.5 mm and angle measurement error of 1.4° indicates a superior performance in comparison to existing methods. Moreover, the automatically estimated abnormality labels were in 95% agreement with those generated by an expert radiologist.

A Deep-Learning Aided Diagnostic System in Assessing Developmental Dysplasia of the Hip on Pediatric Pelvic Radiographs

Background

Developmental dysplasia of the hip (DDH) is a common orthopedic disease in children. In clinical surgery, it is essential to quickly and accurately locate the exact position of the lesion, and there are still some controversies relating to DDH status. We adopt artificial intelligence (AI) to solve the above problems.

Methods

In this paper, automatic DDH measurements and classifications were achieved using a three-stage pipeline. In the first stage, we used Mask-RCNN to detect the local features of the image and segment the bony pelvis, including the ilium, pubis, ischium, and femoral heads. For the second stage, local image patches focused on semantically related areas for DDH landmarks were extracted by high-resolution network (HRNet). In the third stage, some radiographic results are obtained. In the above process, we used 1,265 patient x-ray samples as the training set and 133 samples from two other medical institutions as the verification set. The results of AI were compared with three orthopedic surgeons for reliability and time consumption.

Results

AI-aided diagnostic system's Tönnis and International Hip Dysplasia Institute (IHDI) classification accuracies for both hips ranged from 0.86 to 0.95. The measurements of numerical indices showed that there was no statistically significant difference between surgeons and AI. Tönnis and IHDI indicators were similar across the AI system, intermediate surgeon, and junior surgeon. Among some objective interpretation indicators, such as acetabular index and CE angle, there were good stability and consistency among the four observers. Intraclass consistency of acetabular index and CE angle among surgeons was 0.79-0.98, while AI was 1.00. The measurement time required by AI was significantly less than that of the doctors.

Conclusion

The AI-aided diagnosis system can quickly and automatically measure important parameters and improve the quality of clinical diagnosis and screening referral process with a convenient and efficient way.

Deep Learning for Orthopedic Disease Based on Medical Image Analysis: Present and Future

Since its development, deep learning has been quickly incorporated into the field of medicine and has had a profound impact. Since 2017, many studies applying deep learning-based diagnostics in the field of orthopedics have demonstrated outstanding performance. However, most published papers have focused on disease detection or classification, leaving some unsatisfactory reports in areas such as segmentation and prediction. This review introduces research published in the field of orthopedics classified according to disease from the perspective of orthopedic surgeons, and areas of future research are discussed. This paper provides orthopedic surgeons with an overall understanding of artificial intelligence-based image analysis and the information that medical data should be treated with low prejudice, providing developers and researchers with insight into the real-world context in which clinicians are embracing medical artificial intelligence.

Application of deep-learning-based artificial intelligence in acetabular index measurement

OBJECTIVE

To construct an artificial intelligence system to measure acetabular index and evaluate its accuracy in clinical application.

METHODS

A total of 10,219 standard anteroposterior pelvic radiographs were collected retrospectively from April 2014 to December 2018 in our hospital. Of these, 9,219 radiographs were randomly selected to train and verify the system. The remaining 1,000 radiographs were used to compare the system's and the clinicians' measurement results. All plain pelvic films were labeled by an expert committee through PACS system based on a uniform standard to measure acetabular index. Subsequently, eight other clinicians independently measured the acetabular index from 200 randomly selected radiographs from the test radiographs. Bland-Altman test was used for consistency analysis between the system and clinician measurements.

RESULTS

The test set included 1,000 cases (2,000 hips). Compared with the expert committee measurement, the 95% limits of agreement (95% LOA) of the system was -4.02° to 3.45° (bias = -0.27°, P < 0.05). The acetabular index measured by the system within all age groups, including normal and abnormal groups, also showed good credibility according to the Bland-Altman principle. Comparison of the measurement evaluations by the system and eight clinicians vs. that of, the expert committee, the 95% LOA of the clinician with the smallest measurement error was -2.76° to 2.56° (bias = -0.10°, P = 0.126). The 95% LOA of the system was -0.93° to 2.86° (bias = -0.03°, P = 0.647). The 95% LOA of the clinician with the largest measurement error was -3.41° to 4.25° (bias = 0.42°, P < 0.05). The measurement error of the system was only greater than that of a senior clinician.

CONCLUSION

The newly constructed artificial intelligence system could quickly and accurately measure the acetabular index of standard anteroposterior pelvic radiographs. There is good data consistency between the system in measuring standard anteroposterior pelvic radiographs. The accuracy of the system is closer to that of senior clinicians.