Auxiliary diagnosis of developmental dysplasia of the hip by automated detection of Sharp's angle on standardized anteroposterior pelvic radiographs

Enhancing YOLO5 for the Assessment of Irregular Pelvic Radiographs with Multimodal Information

Developmental dysplasia of the hip (DDH) is one of the most common orthopedic disorders in infants and young children. Accurate identification and localization of anatomical landmarks are prerequisites for the diagnosis of DDH. In recent years, various works have employed deep learning algorithms on radiography images for DDH diagnosis. However, none of these works have considered the incorporation of multimodal information. The pelvis exhibits distinct structures at different developmental stages, and there are also gender-based differences. In light of this, this study proposes a method to enhance the performance of deep learning models in diagnosing DDH by incorporating age and gender information into the channels. The study utilizes YOLO5 to construct a deep learning network for detecting hip joint landmarks. Moreover, a comprehensive dataset of 7750 pelvic X-ray images is established, covering ages from 4 months to 16 years and encompassing various conditions, such as deformities and post-operative cases, which authentically capture the temporal diversity and pathological complexities of DDH. Experimental results show that the YOLO5 model with integrated multimodal information achieves a mAP0.5-0.95 of 83.1% and a diagnostic accuracy of 86.7% in test dataset. The F1 scores for diagnosing cases of normal (NM), suspected dislocation (SD), mild dislocation (MD), and heavily dislocation (HD) are 90.9%, 79.8%, 63.5%, and 97.4%, respectively. Furthermore, experiments conducted on datasets of different sizes and networks of different sizes demonstrate the beneficial impact of multimodal information in improving the effectiveness of deep learning in diagnosing DDH.

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.

Development and clinical validation of deep learning for auto-diagnosis of supraspinatus tears

BACKGROUND

Accurately diagnosing supraspinatus tears based on magnetic resonance imaging (MRI) is challenging and time-combusting due to the experience level variability of the musculoskeletal radiologists and orthopedic surgeons. We developed a deep learning-based model for automatically diagnosing supraspinatus tears (STs) using shoulder MRI and validated its feasibility in clinical practice.

MATERIALS AND METHODS

A total of 701 shoulder MRI data (2804 images) were retrospectively collected for model training and internal test. An additional 69 shoulder MRIs (276 images) were collected from patients who underwent shoulder arthroplasty and constituted the surgery test set for clinical validation. Two advanced convolutional neural networks (CNN) based on Xception were trained and optimized to detect STs. The diagnostic performance of the CNN was evaluated according to its sensitivity, specificity, precision, accuracy, and F1 score. Subgroup analyses were performed to verify its robustness, and we also compared the CNN's performance with that of 4 radiologists and 4 orthopedic surgeons on the surgery and internal test sets.

RESULTS

Optimal diagnostic performance was achieved on the 2D model, from which F1-scores of 0.824 and 0.75, and areas under the ROC curves of 0.921 (95% confidence interval, 0.841-1.000) and 0.882 (0.817-0.947) were observed on the surgery and internal test sets. For the subgroup analysis, the 2D CNN model demonstrated a sensitivity of 0.33-1.000 and 0.625-1.000 for different degrees of tears on the surgery and internal test sets, and there was no significant performance difference between 1.5 and 3.0 T data. Compared with eight clinicians, the 2D CNN model exhibited better diagnostic performance than the junior clinicians and was equivalent to senior clinicians.

CONCLUSIONS

The proposed 2D CNN model realized the adequate and efficient automatic diagnoses of STs, which achieved a comparable performance of junior musculoskeletal radiologists and orthopedic surgeons. It might be conducive to assisting poor-experienced radiologists, especially in community scenarios lacking consulting experts.

Effects of different pelvic osteotomies on acetabular morphology in developmental dysplasia of hip in children

Developmental dysplasia of hip seriously affects the health of children, and pelvic osteotomy is an important part of surgical treatment. Improving the shape of the acetabulum, preventing or delaying the progression of osteoarthritis is the ultimate goal of pelvic osteotomies. Re-directional osteotomies, reshaping osteotomies and salvage osteotomies are the three most common types of pelvic osteotomy. The influence of different pelvic osteotomy on acetabular morphology is different, and the acetabular morphology after osteotomy is closely related to the prognosis of the patients. But there lacks comparison of acetabular morphology between different pelvic osteotomies, on the basis of retrospective analysis and measurable imaging indicators, this study predicted the acetabular shape after developmental dysplasia of the hip pelvic osteotomy in order to help clinicians make reasonable and correct decisions and improve the planning and performance of pelvic osteotomy.

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.

Relationship between labral length and symptoms in patients with acetabular dysplasia before rotational acetabular osteotomy

The aim of this study was to investigate the relationship between acetabular labral length and symptoms in patients with acetabular dysplasia. In a retrospective medical record review, 218 patients with acetabular dysplasia who had undergone rotational acetabular osteotomy were identified. After implementing the inclusion and exclusion criteria, 53 patients were analyzed for preoperative symptoms measured by the Japanese Orthopaedic Association Hip Disease Evaluation Questionnaire (JHEQ), acetabular bone morphology parameters by anteroposterior pelvic radiographs and labral parameters by radial magnetic resonance imaging. Spearman's correlation coefficients were calculated among JHEQ scores, bone morphologic parameters and labral parameters. Multiple linear regression models to determine the predictive variables of JHEQ score and labral length were obtained. There was no correlation between bone morphologic parameters and JHEQ scores. Labral length measured anteriorly correlated with JHEQ pain {r [95% confidence interval (CI)] = -0.335 (-0.555, -0.071), P = 0.014}, movement subscale [r (95% CI) = -0.398 (-0.603, -0.143), P = 0.003], mental subscale [r (95% CI) = -0.436 (-0.632, -0.188), P = 0.001] and total JHEQ score [r (95% CI) = -0.451 (-0.642, -0.204), P = 0.001]. The multiple linear regression results showed that anterior labral length was independently associated with JHEQ subscales in some models. Meanwhile, age, acetabular head index and total JHEQ score were independently associated with anterior labral length in all models. Labral length, notably in anterosuperior area, in patients with symptomatic acetabular dysplasia was related to patient's symptom. Labral length may be an important objective image finding that can be used to assess the severity of cumulative hip instability.

Fully automated measurement on coronal alignment of lower limbs using deep convolutional neural networks on radiographic images

Background

A deep convolutional neural network (DCNN) system is proposed to measure the lower limb parameters of the mechanical lateral distal femur angle (mLDFA), medial proximal tibial angle (MPTA), lateral distal tibial angle (LDTA), joint line convergence angle (JLCA), and mechanical axis of the lower limbs.

Methods

Standing X-rays of 1000 patients’ lower limbs were examined for the DCNN and assigned to training, validation, and test sets. A coarse-to-fine network was employed to locate 20 key landmarks on both limbs that first recognised the regions of hip, knee, and ankle, and subsequently outputted the key points in each sub-region from a full-length X-ray. Finally, information from these key landmark locations was used to calculate the above five parameters.

Results

The DCNN system showed high consistency (intraclass correlation coefficient > 0.91) for all five lower limb parameters. Additionally, the mean absolute error (MAE) and root mean squared error (RMSE) of all angle predictions were lower than 3° for both the left and right limbs. The MAE of the mechanical axis of the lower limbs was 1.124 mm and 1.416 mm and the RMSE was 1.032 mm and 1.321 mm, for the right and left limbs, respectively. The measurement time of the DCNN system was 1.8 ± 1.3 s, which was significantly shorter than that of experienced radiologists (616.8 ± 48.2 s, t = -180.4, P < 0.001).

Conclusions

The proposed DCNN system can automatically measure mLDFA, MPTA, LDTA, JLCA, and the mechanical axis of the lower limbs, thus helping physicians manage lower limb alignment accurately and efficiently.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12891-022-05818-4.

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.

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.

An application for classifying perceptions on my health bank in Taiwan using convolutional neural networks and web-based computerized adaptive testing: A development and usability study

BACKGROUND

The classification of a respondent's opinions online into positive and negative classes using a minimal number of questions is gradually changing and helps turn techniques into practices. A survey incorporating convolutional neural networks (CNNs) into web-based computerized adaptive testing (CAT) was used to collect perceptions on My Health Bank (MHB) from users in Taiwan. This study designed an online module to accurately and efficiently turn a respondent's perceptions into positive and negative classes using CNNs and web-based CAT.

METHODS

In all, 640 patients, family members, and caregivers with ages ranging from 20 to 70 years who were registered MHB users were invited to complete a 3-domain, 26-item, 5-category questionnaire asking about their perceptions on MHB (PMHB26) in 2019. The CNN algorithm and k-means clustering were used for dividing respondents into 2 classes of unsatisfied and satisfied classes and building a PMHB26 predictive model to estimate parameters. Exploratory factor analysis, the Rasch model, and descriptive statistics were used to examine the demographic characteristics and PMHB26 factors that were suitable for use in CNNs and Rasch multidimensional CAT (MCAT). An application was then designed to classify MHB perceptions.

RESULTS

We found that 3 construct factors were extracted from PMHB26. The reliability of PMHB26 for each subscale beyond 0.94 was evident based on internal consistency and stability in the data. We further found the following: the accuracy of PMHB26 with CNN yields a higher accuracy rate (0.98) with an area under the curve of 0.98 (95% confidence interval, 0.97-0.99) based on the 391 returned questionnaires; and for the efficiency, approximately one-third of the items were not necessary to answer in reducing the respondents' burdens using Rasch MCAT.

CONCLUSIONS

The PMHB26 CNN model, combined with the Rasch online MCAT, is recommended for improving the accuracy and efficiency of classifying patients' perceptions of MHB utility. An application developed for helping respondents self-assess the MHB cocreation of value can be applied to other surveys in the future.

What are the significant factors affecting pain in patients with Hartofilakidis type Ι developmental dysplasia of the hip?

Objective

To explore the influencing factors of age at onset of pain and severe pain in patients with Hartofilakidis type I developmental dysplasia of the hip (DDH).

Methods

A retrospective study of 83 patients with DDH treated at our hospital from January 2017 to June 2021 was conducted. The age at onset of pain, patients’ demographic data, and radiographic parameters were collected. Multiple linear regression was used to determine the influencing factors of age at onset of pain. Cox regression analysis was used to determine the influencing factors of severe pain attacks.

Results

According to the results of multiple linear regression analysis, when the distance between the medial femoral head and the ilioischial line increased by one millimetre, the age at onset of pain decreased by 1.7 years (β = − 1.738, 95% CI − 1.914–[− 1.561], p < 0.001). When the sharp angle increases by one degree, the age at onset of pain decreases by 0.3 years (β = − 0.334, 95% CI − 0.496–[− 0.171], p < 0.001). According to the results of the Cox regression analysis, for each additional degree of the lateral centre-edge angle (LCEA), the probability of severe pain was reduced by 5% (Exp [β]: = 0.947, 95% CI 0.898–0.999, p = 0.044). For each additional millimetre in the distance between the medial femoral head and the ilioischial line, the likelihood of severe pain increased by 2.4 times (Exp [β]: 2.417, 95% CI 1.653–3.533, p < 0.001).

Conclusion

Larger distances between the medial femoral head and the ilioischial line and sharp angle can lead to an earlier age at onset of pain in patients with DDH. Small LCEA and excessive distance between the medial femoral head and the ilioischial line are risk factors for severe pain.

Assessment of hip displacement in children with cerebral palsy using machine learning approach

Manual measurements of migration percentage (MP) on pelvis radiographs for assessing hip displacement are subjective and time consuming. A deep learning approach using convolution neural networks (CNNs) to automatically measure the MP was proposed. The pre-trained Inception ResNet v2 was fine tuned to detect locations of the eight reference landmarks used for MP measurements. A second network, fine-tuned MobileNetV2, was trained on the regions of interest to obtain more precise landmarks' coordinates. The MP was calculated from the final estimated landmarks' locations. A total of 122 radiographs were divided into 57 for training, 10 for validation, and 55 for testing. The mean absolute difference (MAD) and intra-class correlation coefficient (ICC [2,1]) of the comparison for the MP on 110 measurements (left and right hips) were 4.5 [Formula: see text] 4.3% (95% CI, 3.7-5.3%) and 0.91, respectively. Sensitivity and specificity were 87.8% and 93.4% for the classification of hip displacement (MP-threshold of 30%), and 63.2% and 94.5% for the classification of surgery-needed hips (MP-threshold of 40%). The prediction results were returned within 5 s. The developed fine-tuned CNNs detected the landmarks and provided automatic MP measurements with high accuracy and excellent reliability, which can assist clinicians to diagnose hip displacement in children with CP.

Diagnostic Performance of a New Convolutional Neural Network Algorithm for Detecting Developmental Dysplasia of the Hip on Anteroposterior Radiographs

OBJECTIVE

To evaluate the diagnostic performance of a deep learning algorithm for the automated detection of developmental dysplasia of the hip (DDH) on anteroposterior (AP) radiographs.

MATERIALS AND METHODS

Of 2601 hip AP radiographs, 5076 cropped unilateral hip joint images were used to construct a dataset that was further divided into training (80%), validation (10%), or test sets (10%). Three radiologists were asked to label the hip images as normal or DDH. To investigate the diagnostic performance of the deep learning algorithm, we calculated the receiver operating characteristics (ROC), precision-recall curve (PRC) plots, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) and compared them with the performance of radiologists with different levels of experience.

RESULTS

The area under the ROC plot generated by the deep learning algorithm and radiologists was 0.988 and 0.988-0.919, respectively. The area under the PRC plot generated by the deep learning algorithm and radiologists was 0.973 and 0.618-0.958, respectively. The sensitivity, specificity, PPV, and NPV of the proposed deep learning algorithm were 98.0, 98.1, 84.5, and 99.8%, respectively. There was no significant difference in the diagnosis of DDH by the algorithm and the radiologist with experience in pediatric radiology (p = 0.180). However, the proposed model showed higher sensitivity, specificity, and PPV, compared to the radiologist without experience in pediatric radiology (p < 0.001).

CONCLUSION

The proposed deep learning algorithm provided an accurate diagnosis of DDH on hip radiographs, which was comparable to the diagnosis by an experienced radiologist.

A brief history of artificial intelligence and robotic surgery in orthopedics & traumatology and future expectations

Recently, the rate of the production and renewal of information makes it almost impossible to be updated. It is quite difficult to process and interpret large amounts of data by human beings. Unlimited memory capacities, learning abilities, artificial intelligence (AI) applications, and robotic surgery techniques cause orthopedic surgeons to be concerned about losing their jobs. The idea of AI, which was first introduced in 1956, has evolved over time by revealing deep learning and evolutionary plexus that can mimic the human neuron cell. Image processing is the leading improvement in developed algorithms. Theoretically, these algorithms appear to be quite successful in interpreting medical images and orthopedic decision support systems for preoperative evaluation. Robotic surgeons have emerged as significant competitors in carrying out the taken decisions. The first robotic applications of orthopedic surgery started in 1992 with the ROBODOC system. Applications started with hip arthroplasty continued with knee arthroplasty. Publications indicate that problems such as blood loss and infection caused by the long operation time in the early stages have been overcome in time with the help of learning systems. Comparative studies conducted with humans indicate that robots are better than humans in providing limb lengthening, patient satisfaction, and cost. As in all new technologies, the developments in both AI applications and robotics surgery indicate that technology is in favor in terms of cost/benefit analyses. Although studies indicate that new technologies are more successful than humans, the replacement of technology with experience and long-term results with traditional methods will not be observed in the near future.

Artificial intelligence in paediatric radiology: Future opportunities

Artificial intelligence (AI) has received widespread and growing interest in healthcare, as a method to save time, cost and improve efficiencies. The high-performance statistics and diagnostic accuracies reported by using AI algorithms (with respect to predefined reference standards), particularly from image pattern recognition studies, have resulted in extensive applications proposed for clinical radiology, especially for enhanced image interpretation. Whilst certain sub-speciality areas in radiology, such as those relating to cancer screening, have received wide-spread attention in the media and scientific community, children's imaging has been hitherto neglected.In this article, we discuss a variety of possible 'use cases' in paediatric radiology from a patient pathway perspective where AI has either been implemented or shown early-stage feasibility, while also taking inspiration from the adult literature to propose potential areas for future development. We aim to demonstrate how a 'future, enhanced paediatric radiology service' could operate and to stimulate further discussion with avenues for research.

Computer-Aided System Application Value for Assessing Hip Development

Purpose

A computer-aided system was used to semiautomatically measure Tönnis angle, Sharp angle, and center-edge (CE) angle using contours of the hip bones to establish an auxiliary measurement model for developmental screening or diagnosis of hip joint disorders.

Methods

We retrospectively analyzed bilateral hip x-rays for 124 patients (41 men and 83 women aged 20-70 years) who presented at the Affiliated Zhongshan Hospital of Dalian University in 2017 and 2018. All images were imported into a computer-aided detection system. After manually outlining hip bone contours, Tönnis angle, Sharp angle, and CE angle marker lines were automatically extracted, and the angles were measured and recorded. An imaging physician also manually measured all angles and recorded hip development, and Pearson correlation coefficients were used to compare computer-aided system measurements with imaging physician measurements. Accuracy for different angles was calculated, and the area under the receiver operating characteristic (AUROC) curve was used to represent the diagnostic efficiency of the computer-aided system.

Results

For Tönnis angle, Sharp angle, and CE angle, correlation coefficients were 0.902, 0.887, and 0.902, respectively; the accuracies of the computer-aided detection system were 89.1, 93.1, and 82.3%; and the AUROC curve values were 0.940, 0.956, and 0.948.

Conclusion

The measurements of Tönnis angle, Sharp angle, and CE angle using the semiautomatic system were highly correlated with the measurements of the imaging physician and can be used to assess hip joint development with high accuracy and diagnostic efficiency.