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

Abstract Purpose To restore rotation center exactly in total hip arthroplasty (THA) is technically challenging for patients with end-stage osteoarthritis due to developmental dysplasia of the hip (DDH). The technical difficulty is attributable to the complex acetabular changes. In this study, we investigated the pathomorphology of acetabulum and Harris fossa of Crowe types I to IV and discussed the method of restoring rotation center of the hip. Methods This study retrospectively reviewed 56 patients (59 hips) who underwent cementless THA due to end-stage osteoarthritis of DDH. The pathomorphology of acetabulum and Harris fossa was observed during operations. Using the preoperative and postoperative pelvic radiographs, the vertical and the horizontal distances of hip rotation center were measured in order to evaluate the effects of restoring rotation center of the hip. Results Adult DDH acetabulum could be classified into four basic pathological types which include the shallow cup shape, the dish shape, the shell shape, and the triangular shape. Adult DDH Harris fossa could be classified into four pathological types, including the crack shape, the closed shape, the triangle shape, and the shallow shape, in accordance with the osteophyte coverage. The vertical and horizontal distances of hip rotation center on the pelvic radiographs before and after operations were as follows: the preoperative vertical distance of hip rotation center was (39.96 ± 5.65) mm, and the postoperative one was (13.83 ± 2.66) mm; the preoperative horizontal distance of hip rotation center was (42.15 ± 6.42) mm, and the postoperative one was (28.12 ± 4.56) mm. Conclusions The acetabulum and Harris fossa can display different pathological types on account of different degrees of dislocation and osteophyte hyperplasia in the end-stage osteoarthritis of adult DDH. The hip rotation center can be accurately restored by locating the acetabular center with Harris fossa and acetabular notch as the marks.

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

The Bone & Joint Journal ◽

10.1302/0301-620x.102b11.bjj-2020-0712.r2 ◽

2020 ◽

Vol 102-B (11) ◽

pp. 1574-1581

Author(s):

Si-Cheng Zhang ◽

Jun Sun ◽

Chuan-Bin Liu ◽

Ji-Hong Fang ◽

Hong-Tao Xie ◽

...

Keyword(s):

Artificial Intelligence ◽

Deep Learning ◽

Receiver Operating Characteristic Curve ◽

Operating Characteristic ◽

Characteristic Curve ◽

Developmental Dysplasia ◽

Learning System ◽

Pelvic Radiographs ◽

Operating Characteristic Curve ◽

Dysplasia Of The Hip

Aims The diagnosis of developmental dysplasia of the hip (DDH) is challenging owing to extensive variation in paediatric pelvic anatomy. Artificial intelligence (AI) may represent an effective diagnostic tool for DDH. Here, we aimed to develop an anteroposterior pelvic radiograph deep learning system for diagnosing DDH in children and analyze the feasibility of its application. Methods In total, 10,219 anteroposterior pelvic radiographs were retrospectively collected from April 2014 to December 2018. Clinicians labelled each radiograph using a uniform standard method. Radiographs were grouped according to age and into ‘dislocation’ (dislocation and subluxation) and ‘non-dislocation’ (normal cases and those with dysplasia of the acetabulum) groups based on clinical diagnosis. The deep learning system was trained and optimized using 9,081 radiographs; 1,138 test radiographs were then used to compare the diagnoses made by deep learning system and clinicians. The accuracy of the deep learning system was determined using a receiver operating characteristic curve, and the consistency of acetabular index measurements was evaluated using Bland-Altman plots. Results In all, 1,138 patients (242 males; 896 females; mean age 1.5 years (SD 1.79; 0 to 10) were included in this study. The area under the receiver operating characteristic curve, sensitivity, and specificity of the deep learning system for diagnosing hip dislocation were 0.975, 276/289 (95.5%), and 1,978/1,987 (99.5%), respectively. Compared with clinical diagnoses, the Bland-Altman 95% limits of agreement for acetabular index, as determined by the deep learning system from the radiographs of non-dislocated and dislocated hips, were -3.27° - 2.94° and -7.36° - 5.36°, respectively (p < 0.001). Conclusion The deep learning system was highly consistent, more convenient, and more effective for diagnosing DDH compared with clinician-led diagnoses. Deep learning systems should be considered for analysis of anteroposterior pelvic radiographs when diagnosing DDH. The deep learning system will improve the current artificially complicated screening referral process. Cite this article: Bone Joint J 2020;102-B(11):1574–1581.

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Differences of Anteroposterior Pelvic Radiographs Between Supine Position and Standing Position in Patients with Developmental Dysplasia of the Hip

Orthopaedic Surgery ◽

10.1111/os.12574 ◽

2019 ◽

Vol 11 (6) ◽

pp. 1142-1148 ◽

Cited By ~ 1

Author(s):

Guo‐yue Yang ◽

Ya‐yue Li ◽

Dian‐zhong Luo ◽

Cheng Hui ◽

Kai Xiao ◽

...

Keyword(s):

Supine Position ◽

Standing Position ◽

Developmental Dysplasia ◽

Pelvic Radiographs ◽

Dysplasia Of The Hip

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4-year follow-up of ultrasound-based diagnosis and non-surgical treatment of developmental dysplasia of the hip in mongolia: a prospective cohort study

Ultraschall in der Medizin - European Journal of Ultrasound ◽

10.1055/s-0036-1587724 ◽

2016 ◽

Vol 37 (S 01) ◽

Cited By ~ 1

Author(s):

T Baumann ◽

B Munkhuu ◽

B Chuluunbaatar ◽

R Schmid ◽

S Essig

Keyword(s):

Cohort Study ◽

Surgical Treatment ◽

Prospective Cohort Study ◽

Prospective Cohort ◽

Developmental Dysplasia ◽

Dysplasia Of The Hip ◽

Non Surgical Treatment

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Faculty Opinions recommendation of Avascular necrosis following closed reduction for treatment of developmental dysplasia of the hip: a systematic review.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.729524230.793540307 ◽

2017 ◽

Author(s):

Edilson Forlin

Keyword(s):

Systematic Review ◽

Avascular Necrosis ◽

Closed Reduction ◽

Developmental Dysplasia ◽

Dysplasia Of The Hip

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Screening of Developmental Dysplasia of the Hip in Neonates with Breech Presentation

مجلة دراسات الطفولة ◽

10.21608/jsc.2015.60708 ◽

2015 ◽

Vol 18 (10.2015) ◽

pp. 0-0

Keyword(s):

Breech Presentation ◽

Developmental Dysplasia ◽

Dysplasia Of The Hip

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Prediction of the requirement of open reduction for developmental dysplasia of the hip

Journal of International Medical Research ◽

10.1177/0300060517717357 ◽

2017 ◽

Vol 46 (1) ◽

pp. 54-61 ◽

Cited By ~ 8

Author(s):

Nabil Alassaf

Keyword(s):

Open Reduction ◽

Area Under The Curve ◽

Internal Validity ◽

Developmental Dysplasia ◽

Estimation Model ◽

Cross Sectional ◽

Noninvasive Treatment ◽

Dysplasia Of The Hip ◽

Logistic Regression Equation ◽

Objective Tool

Objective Closed reduction (CR) is a noninvasive treatment for developmental dysplasia of the hip (DDH), and this treatment is confirmed intraoperatively. This study aimed to develop a preoperative estimation model of the probability of requiring open reduction (OR) for DDH. Methods The study design was cross-sectional by screening all patients younger than 2 years who had attempted CR between October 2012 and July 2016 by a single surgeon. Potential diagnostic determinants were sex, age, side, bilaterality, International Hip Dysplasia Institute (IHDI) grade, and acetabular index (AI). An intraoperative arthrogram was the reference standard. A logistic regression equation was built from a reduced model. Bootstrapping was performed for internal validity. Results A total of 164 hips in 104 patients who met the inclusion criteria were analysed. The prevalence of CR was 72.2%. Independent factors for OR were older age, higher IHDI grade, and lower AI. The probability of OR = 1/[1 + exp − (−2.753 + 0.112 × age (months) + 1.965 × IHDI grade III (0 or 1) + 3.515 × IHDI grade IV (0 or 1) − 0.058 × AI (degrees)]. The area under the curve was 0.79. Conclusion This equation is an objective tool that can be used to estimate the requirement for OR.

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Accuracy of New Deep Learning Model-Based Segmentation and Key-Point Multi-Detection Method for Ultrasonographic Developmental Dysplasia of the Hip (DDH) Screening

Diagnostics ◽

10.3390/diagnostics11071174 ◽

2021 ◽

Vol 11 (7) ◽

pp. 1174

Author(s):

Si-Wook Lee ◽

Hee-Uk Ye ◽

Kyung-Jae Lee ◽

Woo-Young Jang ◽

Jong-Ha Lee ◽

...

Keyword(s):

Deep Learning ◽

Intraclass Correlation ◽

Alpha Angle ◽

Learning Model ◽

Developmental Dysplasia ◽

Human Observer ◽

Intraobserver Variability ◽

Cad System ◽

Deep Learning Model ◽

Dysplasia Of The Hip

Hip joint ultrasonographic (US) imaging is the golden standard for developmental dysplasia of the hip (DDH) screening. However, the effectiveness of this technique is subject to interoperator and intraobserver variability. Thus, a multi-detection deep learning artificial intelligence (AI)-based computer-aided diagnosis (CAD) system was developed and evaluated. The deep learning model used a two-stage training process to segment the four key anatomical structures and extract their respective key points. In addition, the check angle of the ilium body balancing level was set to evaluate the system’s cognitive ability. Hence, only images with visible key anatomical points and a check angle within ±5° were used in the analysis. Of the original 921 images, 320 (34.7%) were deemed appropriate for screening by both the system and human observer. Moderate agreement (80.9%) was seen in the check angles of the appropriate group (Cohen’s κ = 0.525). Similarly, there was excellent agreement in the intraclass correlation coefficient (ICC) value between the measurers of the alpha angle (ICC = 0.764) and a good agreement in beta angle (ICC = 0.743). The developed system performed similarly to experienced medical experts; thus, it could further aid the effectiveness and speed of DDH diagnosis.

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