INFLUENCE OF AGE ON PARAMETERS FOR FEMOROACETABULAR IMPINGEMENT AND HIP DYSPLASIA IN X-RAYS

Femoral Head Coverage Assessment in Healthy Children Younger than 6 Years

Introduction

Developmental dysplasia of the hip (DDH) is one of the commonest hip joint pathologies in children; to treat it properly, hip surgeons should know the normal femoral head (FH) coverage by the acetabulum. This paper aims to assess the femoral head coverage in healthy children younger than 6 years.

Methods

270 hip joint CT scans were selected, and digital pelvic models were created according to these scans. FH coverage by the five acetabular regions was assessed according to patient's age and sex.

Results

Normal reference values of FH coverage by different acetabular regions were obtained. It was found that the growth process of different acetabular regions occurs nonlinearly with the periods of acceleration. Anterior and superior-anterior acetabular regions grow more intensively in boys up to 3 years old and between 4 and 5 years old both in boys and girls; superior-posterior, posterior-superior, and posterior-inferior acetabular regions grow more intensively in boys and girls up to 3 years old and between 4 and 5 years old (p ≤ 0.005). The following sex differences in FH coverage by the acetabulum were found: more superior-anterior FH coverage was found in boys and posterior FH coverage in girls (p ≤ 0.005).

A prospective cohort study on cam morphology and its role in progression of osteoarthritis

Background

Cam morphology contributes to the development of hip osteoarthritis (OA) but is less studied in the general population. This study describes its associations with clinical and imaging features of hip OA.

Methods

Anteroposterior hip radiographs of 1019 participants from the Tasmanian Older Adult Cohort (TASOAC) were scored at baseline for α angle (cam morphology) in both hips. Using the Altman's atlas, radiographic hip OA (ROA) was assessed at baseline. Hip pain and right hip structural changes were assessed on a subset of 245 magnetic resonance images (MRI) at 5 years. Joint registry data for total hip replacement (THR) was acquired 14 years from baseline.

Results

Of 1906 images, cam morphology was assessed in 1016 right and 890 left hips. Cross‐sectionally, cam morphology modestly associated with age (prevalence ratio [PR]: 1.02 P = .03) and body mass index (BMI) (PR: 1.03‐1.07, P = .03) and strongly related to male gender (PR: 2.96, P < .001). Radiographically, cam morphology was prevalent in those with decreased joint space (PR: 1.30 P = .03) and osteophytes (PR: 1.47, P = .03). Longitudinally, participants with right cam and high BMI had more hip pain (PR: 17.9, P = .02). At the end of 5 years of follow‐up these participants were also more likely to have structural changes such as bone marrow lesions (BMLs) (PR: 1.90 P = .04), cartilage defects (PR: 1.26, P = .04) and effusion‐synovitis at multiple sites (PR: 1.25 P = .02). Cam morphology at baseline in either hip predicted up to threefold risk of THR (PR: 3.19, P = .003) at the end of 14 years.

Conclusion

At baseline, cam morphology was linked with age, higher weight, male gender, early signs of radiographic OA such as joint space narrowing (JSN) and osteophytes (OST). At follow‐up, cam predicted development of hip BMLs, hip effusion‐synovitis, cartilage damage and THR. These findings suggest that cam morphology plays a significant role in early OA and can be a precursor or contribute to hip OA in later life.

Feasibility of automatic measurements of hip joints based on pelvic radiography and a deep learning algorithm

PURPOSE

To develop and evaluate an automatic measurement model for hip joints based on anteroposterior (AP) pelvic radiography and a deep learning algorithm.

METHODS

A total of 1260 AP pelvic radiographs were included. 1060 radiographs were randomly sampled for training and validation and 200 radiographs were used as the test set. Landmarks for four commonly used parameters, such as the center-edge (CE) angle of Wiberg, Tönnis angle, sharp angle, and femoral head extrusion index (FHEI), were identified and labeled. An encoder-decoder convolutional neural network was developed to output a multi-channel heat map. Measurements were obtained through landmarks on the test set. Right and left hips were analyzed respectively. The mean of each parameter obtained by three radiologists was used as the reference standard. The Percentage of Correct Key points (PCK), intraclass correlation coefficient (ICC), Pearson correlation coefficient (r), root mean square error (RMSE), mean absolute error (MAE), and Bland-Altman plots were used to determine the performance of deep learning algorithm.

RESULTS

PCK of the model at 3 mm distance threshold range was from 87 % to 100 %. The CE angle, Tönnis angle, Sharp angle and FHEI of the left hip generated by the model were 29.8°±6.1°, 5.6°±4.2°, 39.0°±3.5° and 19 %±5 %, respectively. The parameters of the right hip were 30.4°±6.1°, 7.1°±4.4°, 38.9°±3.7° and 18 %±5 %. There were good correlation and consistency of the four parameters between the model and the reference standard (ICC 0.83-0.93, r 0.83-0.93, RMSE 0.02-3.27, MAE 0.02-1.79).

CONCLUSIONS

The new developed model based on deep learning algorithm can accurately identify landmarks on AP pelvic radiography and automatically generate parameters of hip joint. It will provide convenience for clinical practice of measurement.