A novel computer-based method for measuring the acetabular angle on hip radiographs

Şahin, Seda; Akata, Emin; Şahin, Orçun; Tuncay, Cengiz; Özkan, Hüseyin

doi:10.1016/j.aott.2016.09.002

Cited by 9 publications

(4 citation statements)

References 25 publications

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“…Table 4 shows some of the related results in the literature. In Sahin et al [ 24 ], comparing means is not a valid accuracy metric, as it does not compare individual measurements to the corresponding gold standard. Liu et al [ 25 ] report a 94.68% F1 score, however, this is done for the heavy dislocation case based on their approach for measuring the AA index, and it is not clear what gold standard did they compare to given that they compared their results to a doctor with/without the aid of a computer.…”

Section: Resultsmentioning

confidence: 99%

“…After that, the ResNet50 model was used for classification. Sahin et al [ 24 ] constructed a model template image based on expert opinion and used comparisons with that template to find the best fitting (i.e., diagnosis). However, this type of method depends on the quality, scale, rotation, and shape of the pelvic image being similar to the template.…”

Section: Introductionmentioning

confidence: 99%

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Detection of developmental dysplasia of the hip in X-ray images using deep transfer learning

Fraiwan

Al-Kofahi

Ibnian

et al. 2022

BMC Med Inform Decis Mak

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Background Developmental dysplasia of the hip (DDH) is a relatively common disorder in newborns, with a reported prevalence of 1–5 per 1000 births. It can lead to developmental abnormalities in terms of mechanical difficulties and a displacement of the joint (i.e., subluxation or dysplasia). An early diagnosis in the first few months from birth can drastically improve healing, render surgical intervention unnecessary and reduce bracing time. A pelvic X-ray inspection represents the gold standard for DDH diagnosis. Recent advances in deep learning artificial intelligence have enabled the use of many image-based medical decision-making applications. The present study employs deep transfer learning in detecting DDH in pelvic X-ray images without the need for explicit measurements. Methods Pelvic anteroposterior X-ray images from 354 subjects (120 DDH and 234 normal) were collected locally at two hospitals in northern Jordan. A system that accepts these images as input and classifies them as DDH or normal was developed using thirteen deep transfer learning models. Various performance metrics were evaluated in addition to the overfitting/underfitting behavior and the training times. Results The highest mean DDH detection accuracy was 96.3% achieved using the DarkNet53 model, although other models achieved comparable results. A common theme across all the models was the extremely high sensitivity (i.e., recall) value at the expense of specificity. The F1 score, precision, recall and specificity for DarkNet53 were 95%, 90.6%, 100% and 94.3%, respectively. Conclusions Our automated method appears to be a highly accurate DDH screening and diagnosis method. Moreover, the performance evaluation shows that it is possible to further improve the system by expanding the dataset to include more X-ray images.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection of developmental dysplasia of the hip in X-ray images using deep transfer learning

Fraiwan

Al-Kofahi

Ibnian

et al. 2022

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

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“…Hip radiography is a convenient diagnostic tool since it is available in most hospitals and clinics. However, the diagnostic accuracy of hip radiography for DDH varies according to the interpreter’s experience 6 . DDH is a relatively rare disease; in fact, a Japanese study revealed that 32% of orthopedic doctors were yet to encounter a patient with DDH in their career as at the time of the study 1 .…”

Section: Introductionmentioning

confidence: 99%

Diagnostic accuracy of a deep learning model using YOLOv5 for detecting developmental dysplasia of the hip on radiography images

Den

Ito

Kokaze

2023

Sci Rep

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Developmental dysplasia of the hip (DDH) is a cluster of hip development disorders and one of the most common hip diseases in infants. Hip radiography is a convenient diagnostic tool for DDH, but its diagnostic accuracy is dependent on the interpreter’s level of experience. The aim of this study was to develop a deep learning model for detecting DDH. Patients younger than 12 months who underwent hip radiography between June 2009 and November 2021 were selected. Using their radiography images, transfer learning was performed to develop a deep learning model using the “You Only Look Once” v5 (YOLOv5) and single shot multi-box detector (SSD). A total of 305 anteroposterior hip radiography images (205 normal and 100 DDH hip images) were collected. Of these, 30 normal and 17 DDH hip images were used as the test dataset. The sensitivity and the specificity of our best YOLOv5 model (YOLOv5l) were 0.94 (95% confidence interval [CI] 0.73–1.00) and 0.96 (95% CI 0.89–0.99), respectively. This model also outperformed the SSD model. This is the first study to establish a model for detecting DDH using YOLOv5. Our deep learning model provides good diagnostic performance for DDH. We believe our model is a useful diagnostic assistant tool.

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“…The anteroposterior (AP) view, frog-leg lateral view, or von Rosen view can be used for the diagnosis of DDH; however, the hip AP view is usually sufficient for screening DDH ( 5 ). The diagnostic performance of conventional radiography for the detection of DDH in infants can vary according to several factors such as the patient's age, technical adequacy, and reader's experience ( 6 ). In particular, the interpreter's experience can significantly affect the diagnostic accuracy of conventional radiography.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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A novel computer-based method for measuring the acetabular angle on hip radiographs

Cited by 9 publications

References 25 publications

Detection of developmental dysplasia of the hip in X-ray images using deep transfer learning

Detection of developmental dysplasia of the hip in X-ray images using deep transfer learning

Diagnostic accuracy of a deep learning model using YOLOv5 for detecting developmental dysplasia of the hip on radiography images

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

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