Prevention of hip dysplasia in children and adults

The Impact of Maternal Age and Educational Level on Developmental Dysplasia of the Hip Diagnosis and Screening: A Descriptive Comparative Study

Background

Early and proper screening for developmental dysplasia of the hip (DDH) is very critical to prevent catastrophic complication on the developing hip joint. Many factors (either maternal or child-related) that hinder timely DDH screening have been previously investigated.

Methods

A cross-sectional descriptive study design was adopted. 175 babies presented for DDH screening coming with their mothers were investigated. Maternal age, age group, and educational level were recorded. In addition, multiple child-related variables such as age of screening, gender, positive family history, preterm delivery, and mode of delivery were recorded as well. Analysis for association between delayed vs. early screening was made against the maternal and the child-related variables.

Results

A total number of 175 children with their mothers were investigated. The mean maternal age was 27.9 years, about one third of the mothers had a graduate level of education (36.3%), while 41.1% had high school education, and 22.3% had middle school education. On the other hand, 40.0% of the investigated babies were first born and two thirds of our sample babies were females (66.9%). Of the included babies, 100 (57.1%) were screened at the appropriate 4-month age, while 75 (42.9%) missed the 4-month screening. Chi-square analysis showed that delayed DDH screening was associated with a lower maternal educational level (P ≤ 0.001), younger maternal age (P ≤ 0.001), and first born baby (P ≤ 0.001). Positive family history was protective against delayed DDH screening (P = 0.032).

Conclusion

The lower maternal educational level, younger maternal age group, and first born babies are risk factors for delayed DDH screening.

Can AI Automatically Assess Scan Quality of Hip Ultrasound?

Ultrasound images can reliably detect Developmental Dysplasia of the Hip (DDH) during early infancy. Accuracy of diagnosis depends on the scan quality, which is subjectively assessed by the sonographer during ultrasound examination. Such assessment is prone to errors and often results in poor-quality scans not being reported, risking misdiagnosis. In this paper, we propose an Artificial Intelligence (AI) technique for automatically determining scan quality. We trained a Convolutional Neural Network (CNN) to categorize 3D Ultrasound (3DUS) hip scans as ‘adequate’ or ‘inadequate’ for diagnosis. We evaluated the performance of this AI technique on two datasets—Dataset 1 (DS1) consisting of 2187 3DUS images in which each image was assessed by one reader for scan quality on a scale of 1 (lowest quality) to 5 (optimal quality) and Dataset 2 (DS2) consisting of 107 3DUS images evaluated semi-quantitatively by four readers using a 10-point scoring system. As a binary classifier (adequate/inadequate), the AI technique gave highly accurate predictions on both datasets (DS1 accuracy = 96% and DS2 accuracy = 91%) and showed high agreement with expert readings in terms of Intraclass Correlation Coefficient (ICC) and Cohen’s kappa coefficient (K). Using our AI-based approach as a screening tool during ultrasound scanning or postprocessing would ensure high scan quality and lead to more reliable ultrasound hip examination in infants.

Artificial Intelligence to Automatically Assess Scan Quality in Hip Ultrasound

PURPOSE

Since it is fast, inexpensive and increasingly portable, ultrasound can be used for early detection of Developmental Dysplasia of the Hip (DDH) in infants at point-of-care. However, accurate interpretation\is highly dependent on scan quality. Poor-quality images lead to misdiagnosis, but inexperienced users may not even recognize the deficiencies in the images. Currently, users assess scan quality subjectively, based on image landmarks which are prone to human errors. Instead, we propose using Artificial Intelligence (AI) to automatically assess scan quality.

METHODS

We trained separate Convolutional Neural Network (CNN) models to detect presence of each of four commonly used ultrasound landmarks in each hip image: straight horizontal iliac wing, labrum, os ischium and midportion of the femoral head. We used 100 3D ultrasound (3DUS) images for training and validated the technique on a set of 107 3DUS images also scored for landmarks by three non-expert readers and one expert radiologist.

RESULTS

We got AI ≥ 85% accuracy for all four landmarks (ilium = 0.89, labrum = 0.94, os ischium = 0.85, femoral head = 0.98) as a binary classifier between adequate and inadequate scan quality. Our technique also showed excellent agreement with manual assessment in terms of Intraclass Correlation Coefficient (ICC) and Cohen's kappa coefficient (K) for ilium (ICC = 0.81, K = 0.56), os ischium (ICC = 0.89, K = 0.63) and femoral head (ICC = 0.83, K = 0.66), and moderate to good agreement for labrum (ICC = 0.65, K = 0.33).

CONCLUSION

This new technique could ensure high scan quality and facilitate more widespread use of ultrasound in population screening of DDH.

Current Evidence about Developmental Dysplasia of the Hip in Pregnancy

In adults, developmental dysplasia of the hip (DDH) represents a spectrum of disorders. It is commonly found in women in routine orthopedic practice. Hip dysplasia is a leading precursor of joint laxity; when untreated, it can contribute to chronic modifications, such as thickening of the pulvinar and ligamentum teres (which can also elongate), hypertrophy of the transverse acetabular ligament, and osteoarthritis. DDH is presumed to be associated with alterations in pelvic morphology that may affect vaginal birth by the reduction in the transverse diameter of the pelvic inlet or outlet. Here, we provide an overview of the current knowledge of pregnancy-associated DDH. We primarily focused on how a surgical DDH treatment might influence the pelvic shape and size and the effects on the mechanism of birth. We presented the female pelvis from the standpoint of bone and ligament morphology relative to a pelvic osteotomy. Then, we described whether the pregnancy was impacted by previous surgical DDH treatments, performed from infancy to adulthood. In conclusion, hip dysplasia is not associated with high-risk complications during pregnancy or with increased difficulty in vaginal delivery.

Impact of scan quality on AI assessment of hip dysplasia ultrasound

AIMS

Early diagnosis of developmental dysplasia of the hip (DDH) using ultrasound (US) is safe, effective and inexpensive, but requires high-quality scans. The effect of scan quality on diagnostic accuracy is not well understood, especially as artificial intelligence (AI) begins to automate such diagnosis. In this paper, we developed a 10-point scoring system for reporting DDH US scan quality, evaluated its inter-rater agreement and examined its effect on automated assessment by an AI system-MEDO-Hip.

METHODS

Scoring was based on iliac wing straightness and angulation; visibility of labrum, os ischium and femoral head; motion; and other artifacts. Four readers from novice to expert separately scored the quality of 107 scans with this 10-point scale and with holistic grading on a scale of 1-5. MEDO-Hip interpreted the same scans, providing a diagnostic category or identifying the scan as uninterpretable.

RESULTS

Inter-rater agreement for the 10-point scale was significantly higher than holistic scoring ICC 0.68 vs 0.93, p < 0.05. Inter-rater agreement on the categorisation of individual features, by Cohen's kappa, was highest for os ischium (0.67 ± 0.06), femoral head (0.65 ± 0.07) and iliac wing (0.49 ± 0.12) indices, and lower for the presence of labrum (0.21 ± 0.19). MEDO-Hip interpreted all images of a quality > 7 and flagged 13/107 as uninterpretable. These were low-quality images (3 ± 1.2 vs. 7 ± 1.8 in others, p < 0.05), with poor visualization of the os ischium and noticeable motion. AI accuracy in cases with quality scores <  = 7 was 57% vs. 89% on other cases, p < 0.01.

CONCLUSION

This study validates that our scoring system reliably characterises scan quality, and identifies cases likely to be misinterpreted by AI. This could lead to more accurate use of AI in DDH diagnosis by flagging low-quality scans likely to provide poor diagnosis up front.

Double Diapering Ineffectiveness in Avoiding Adduction and Extension in Newborns Hips

Hip flexion and abduction is fundamental for developmental dysplasia of the hip (DDH) treatment. At present, double diaper treatment has been inappropriately adopted when DDH is suspected. The aim of this study was to verify whether double diapers influence a newborn’s hip position. Here, we studied 50 children (23 female; 27 male; average age 62.33 ± 20.50 days; average birth weight 3230 ± 447 g) with type I hips according to Graf. At the same time of the ultrasound (US) examination, the following hip positions were measured using a manual protractor: (1) spontaneous position, supine on the outpatient bed without a diaper; (2) spontaneous position, with a double diaper; and (3) squatting position on the caretakers’ side. Statistical analysis was performed with a t-test to compare between (1) the spontaneous position without a diaper and with double diapers; (2) the spontaneous position with double diapers as well as the squatting position on the caretakers’ side with a diaper. The comparison between the hip position without diaper and with double diapers was statistically not significant for all measurements, i.e., right hip flexion (p < 0.33), left hip flexion (p < 0.34), and right and left hip abduction (p < 0.87). The comparison between the hip position with double diapers and on the caretakers’ side was statistically significant for all measurements, i.e., right hip flexion (p < 0.001), left hip flexion (p < 0.001) and right and left hip abduction (p < 0.001). We found that the use of double diapers did not affect hip position, while the position formed on the caretaker’s side shows favorable influence.

3-D Ultrasound Imaging Reliability of Measuring Dysplasia Metrics in Infants

Developmental dysplasia of the hip is a hip abnormality that ranges from mild acetabular dysplasia to irreducible femoral head dislocations. While 2-D B-mode ultrasound (US)-based dysplasia metrics or disease metrics are currently used clinically to diagnose developmental dysplasia of the hip, such estimates suffer from high inter-exam variability. In this work, we propose and evaluate 3-D US-derived dysplasia metrics that are automatically computed and demonstrate that these automatically derived dysplasia metrics are considerably more reproducible. The key features of our automatic method are (i) a random forest-based learning technique to remove regions across the coronal axis that do not contain bone structures necessary for dysplasia-metric extraction, thereby reducing outliers; (ii) a bone segmentation method that uses rotation-invariant and intensity-invariant filters, thus remaining robust to signal dropout and varying bone morphology; (iii) a novel slice-based learning and 3-D reconstruction strategy to estimate a probability map of the hypoechoic femoral head in the US volume; and (iv) formulae for calculating the 3-D US-derived dysplasia metrics. We validate our proposed method on real clinical data acquired from 40 infant hip examinations. Results show a considerable (around 70%) reduction in variability in two key 3-D US-derived dysplasia metrics compared with their 2-D counterparts.

Late Presentation of Developmental Hip Dislocation: A Case Report

CASE

We describe a patient who was diagnosed with developmental hip dislocation at 21 months of age despite having had normal ultrasonography findings at 5 weeks of age.

CONCLUSION

This case report provides evidence that late developmental hip dislocation can occur despite normal clinical and sonographic findings early in life, and that it is difficult to know the cause of developmental hip dislocation when it presents late.

Developmental dysplasia of the hip: A computational biomechanical model of the path of least energy for closed reduction

This study utilized a computational biomechanical model and applied the least energy path principle to investigate two pathways for closed reduction of high grade infantile hip dislocation. The principle of least energy when applied to moving the femoral head from an initial to a final position considers all possible paths that connect them and identifies the path of least resistance. Clinical reports of severe hip dysplasia have concluded that reduction of the femoral head into the acetabulum may occur by a direct pathway over the posterior rim of the acetabulum when using the Pavlik harness, or by an indirect pathway with reduction through the acetabular notch when using the modified Hoffman-Daimler method. This computational study also compared the energy requirements for both pathways. The anatomical and muscular aspects of the model were derived using a combination of MRI and OpenSim data. Results of this study indicate that the path of least energy closely approximates the indirect pathway of the modified Hoffman-Daimler method. The direct pathway over the posterior rim of the acetabulum required more energy for reduction. This biomechanical analysis confirms the clinical observations of the two pathways for closed reduction of severe hip dysplasia. The path of least energy closely approximated the modified Hoffman-Daimler method. Further study of the modified Hoffman-Daimler method for reduction of severe hip dysplasia may be warranted based on this computational biomechanical analysis. © 2016 The Authors. Journal of Orthopaedic Research Published by Wiley Periodicals, Inc. on behalf of Orthopaedic Research Society. J Orthop Res 35:1799-1805, 2017.