Although physical and ultrasound (US)-based screening for congenital deformities of the hip (developmental dysplasia of the hip, or DDH) is routinely performed in most countries, one of the most commonly performed manoeuvres done under ultrasound observation - dynamic assessment - has been shown to be relatively unreliable and is associated with significant misdiagnosis rates, on the order of 29%.

Our overall research objective is to develop a quantitative method of assessing hip instability, which we hope will standardise diagnosis across different raters and health-centres, and may perhaps improve reliability of diagnosis. To quantify dynamic assessment, we propose to use the variability in femoral head coverage (FHC) measurements within multiple US scans collected during a dynamic assessment. In every US scan, we use our recently-developed automatic FHC measuring tool which leverages phase symmetry features to approximate vertical cortex of ilium and a random forest classifier to identify approximate location of the femoral head. Having estimated FHC in each scan, we estimate the change in FHC across all the US scans during a dynamic assessment and compare this change with variability of FHC found in previous studies.

Our findings - in a dynamic assessment on an infant done by an orthopaedic surgeon, the femoral centre moved by up to 19% of its diameter during distraction, from 55% FHC to 74% FHC. This variability is similar to the variability of FHC in static US scans reported in previous studies, so the variability in FHC readings we found are not indicative of any subluxation or dislocation of the infant's femoral head. Our clinician's qualitative assessment concluded the hip to be normal and not indicative of instability. This suggests that our technique likely has sufficient resolution and repeatability to quantify differences in laxity between stable and unstable hips, although this presumption will have to be confirmed in a subsequent study with additional subjects. The long-term significance of this approach to evaluating dynamic assessments may lie in increasing early diagnostic sensitivity in order to prevent dysplasia remaining undetected prior to manifesting itself in early adulthood joint disease.

Ultrasound (US) is the standard imaging modality used to screen for developmental dysplasia of the hip in infants. Currently, radiologists or orthopaedic surgeons review scan images and judge them to be adequate or inadequate for interpretation. If considered adequate, diagnostic dysplasia metrics are determined; however, there is no standardised method for this process. There is significant inter-observer variability in this manual process which can affect misdiagnosis rates. To eliminate this subjectivity, we developed an automatic method to identify adequate US images and extract dysplasia metrics. The purpose of this study was to validate the efficacy of this automatic method by comparing results with observer-determined dysplasia metrics on a set of US images.

A total of 693 US images from scans of 35 infants were analysed. Trained clinicians at a single institution labeled each image as adequate or inadequate, and subsequently measured alpha and beta angles on adequate images to diagnose dysplasia. We trained our image classifier on random sets of 415 images and used it to assess the remaining 278 images. Alpha and beta angles were automatically estimated on all adequate images. We compared the manual and automatic methods for discrepancies in adequacy determination, metric variability and incidences of missed early diagnosis or over-treatment.

There was excellent agreement between the automatic and manual methods in image adequacy classification (Kappa coefficient = 0.912). On each adequate US image, alpha and beta angle measurements were compared, producing mixed levels of agreement between methods. Mean discrepancies of 1.78°±4.72° and 8.91°±6.437° were seen for alpha and beta angles, respectively. Standard deviations of the angle measures across multiple images from a single patient scan were significantly reduced by the automatic method for both alpha (p<0.05) and beta (p<0.01) angles. Additionally, the automatic method classified three hips (two patients) as Graf type II and two hips (two patients) as type III, while the manual method classified them as type I and II, respectively. Both cases flagged as type III patients by the automatic system subsequently failed Pavlik harness treatment and were booked for surgery.

The automatic method produced excellent agreement with radiologists in scan adequacy classification and significantly reduced measurement variability. Good agreement between methods was found in Graf classification. In instances of disagreement, subsequent clinical findings seemed to support the classification of the automatic method. This proposed method presents an alternative automatic, near-real-time analysis for US images that may potentially significantly improve dysplasia metric reliability and reduce missed early diagnoses without increasing over-treatment.

Ultrasound (US) imaging is recommended for early detection of Developmental Dysplasia of the Hip (DDH) to guide decisions about possible surgical treatment. However, a number of studies have raised concerns over the efficacy of US in early diagnosis. The main limitation of US-based diagnosis is sub-standard reliability of the primary dysplasia metric measurements: namely, the alpha and beta angles. In this study, we have proposed a novel and automatic method to extract dysplasia metrics from 2D US, which we hope will improve the overall reliability of US-based DDH measurements by removing error due to subjective measurements. We hypothesise that improvements in reliability of dysplasia metric measurements will reduce the chances of missed early-diagnosis, and therefore reduce the need for later complex surgical treatments.

We evaluated performance of the algorithm on 4 infants diagnosed with US scans for DDH. The typical runtime of our algorithm is less than 1 second for an US image. We found a 6° bias between manual and automatic measurements, with automatic measurements tending to be lower in value; the standard deviation in the discrepancy values was also relatively high at 7°. This suggests that there is considerable variability in the angle estimation process, which is typically done manually, which supports our contention that further work needs to be done to establish an accurate and repeatable measurement technique. Further, we found agreements in the Graf-classification types in six out of seven sessions. For the one patient where there was a discrepancy in classification, later US sessions suggest the manual technique possibly missed the opportunity for early detection, in contrast to the automatic method which classified the patient as having evidence of dysplasia. Thus, such an automatic method may improve the reliability of current US-based DDH diagnosis techniques. The primary limitation of this study is that we have done strictly an intra-image discrepancy analysis and have not compared the results with what could be considered a ‘gold standard’ reference. In future work, we plan to assess these indices on 3D images of the hip and assess the accuracy of proposed 2D and 3D-based automatic index calculation techniques against a 3D reference model.