Abstract
Hip dysplasia (HD) is a frequent cause of hip pain in skeletally mature patients and may lead to osteoarthritis (OA). An accurate and early diagnosis may postpone, reduce or even prevent the onset of OA and ultimately hip arthroplasty at a young age. The overall aim of this study was to assess the reliability of an algorithm, designed to read pelvic anterior-posterior (AP) radiographs and to estimate the agreement between the algorithm and human readers for measuring (i) lateral center edge angle of Wiberg (LCEA) and (ii) Acetabular index angle (AIA). The algorithm was based on deep-learning models developed using a modified U-net architecture and ResNet 34. The newly developed algorithm was found to be highly reliable when identifying the anatomical landmarks used for measuring LCEA and AIA in pelvic radiographs, thus offering highly consistent measurement outputs. The study showed that manual identification of the same landmarks made by five specialist readers were subject to variance and the level of agreement between the algorithm and human readers was consequently poor with mean measured differences from 0.37 to 9.56° for right LCEA measurements. The algorithm displayed the highest agreement with the senior orthopedic surgeon. With further development, the algorithm may be a good alternative to humans when screening for HD.
Introduction
Joint deformity as seen in the presence of hip dysplasia is a common cause of hip pain in young skeletally mature patients and may lead to osteoarthritis (OA) [1]. Traditionally, first line modality when diagnosing dysplasia of the hip is radiographs, where measurements taken from a standardized anterior-posterior (AP) pelvic radiograph are used to evaluate the anatomical configuration of the pelvis. The lateral center edge angle of Wiberg (LCEA) describes femoral head coverage by the acetabulum and the acetabular index angle (AIA) quantifies the inclination of the acetabular roof [2].
An accurate and early diagnosis may postpone, reduce or even prevent the onset of OA and ultimately hip arthroplasty at a young age [3]. It was previously reported, though, that patients with non-specific hip pain may be left with symptoms for years before the correct diagnosis of hip dysplasia is made, perhaps because the anatomical deformities indicative of hip dysplasia are not routinely reported in all departments of radiology. They found that the correct diagnosis of hip dysplasia was delayed with up to several years (range: 0–204 months) and more than three confrontations (range: 0–11) with the healthcare system [4,5]. The anatomical deformities associated with hip dysplasia may be diagnosed earlier by using artificial intelligence models and algorithms, possibly as a screening tool, which may also have the potential to improve reader variability and workflow.
The process of developing and testing an algorithm for measuring LCEA and AIA has recently been published and results indicated that an automatic measurement model is feasible [6]. Moreover, Fraiwan and colleagues showed the potential of deep transfer learning for detecting developmental dysplasia of the hip in pelvic radiographs of infants [7]. It has also been suggested that AI is useful for detection and classification of hip dysplasia using ultrasound images [8]. Clinical tests of algorithms for measuring hip parameters such as LCEA and AIA in skeletally mature patients are to the best of the authors’ knowledge limited.
The overall purpose of this study was, in a clinical setting, to assess the performance of an algorithm designed to read pelvic AP radiographs of skeletally mature patients. We aimed to assess reliability of the algorithm and agreement between the algorithm and, respectively, orthopedic surgeons, radiologists and a reporting radiographer for measuring LCEA and AIA.
Methods
In this retrospective study, we used an algorithm trained to identify several specific segments related to hip dysplasia. The algorithm was applied to 78 pelvic radiographs that were consecutively collected from one center. Moreover, two orthopedic surgeons, two radiologists and one reporting radiographer evaluated all images in regard to LCEA, AIA, and the width of both obturator foramen. The study was approved by the Danish National Committee on Health Research Ethics (Project-ID: 2103745) and registered with the regional health authorities (project-ID: 21/22036). The analyses were carried out in concordance with current Guidelines for Reporting Reliability and Agreement Studies [9,10].
Results
The algorithm was not able to read seven of the 78 included images. Therefore, 71 radiographs were read by the algorithm, resulting in a sample with an average age of 50.1 years [range; 18 to 91] consisting of 36 females and 35 males for agreement analyses. All 78 radiographs were reported by the human readers and included in repeatability and reproducibility estimates.
The algorithm proved highly consistent when double reading all measurements, displaying variances between first and second read that were identical or within the range of machine precision (Table 1). Values for all parameters showed a tendency to be higher when measured by humans, particularly the LCEA measurements. The LCEA (right hip) for humans ranged from 25.8 to 35.0° versus 25.4° when measured by the algorithm. Corresponding values for AIA (right hip) ranged from 4.1 to 6.7° for humans versus 4.7° when measured by the algorithm (Table 2). Scatterplots visually depict human measurements over algorithm measurements (Figure 4).
Discussion
To the best of our knowledge, this is the first study presenting an algorithm that assesses hip dysplasia in adults in a clinical setting. Radiographic evaluation of the pelvis is commonly the first-line approach in patients suspicious of hip dysplasia, where a set of measurements are used to describe anatomy of the pelvis. Several of those measurements are, however, associated with reader variability [11].
Open access
This research is open access.
Diagnostics is an international, peer-reviewed, open access journal on medical diagnosis published semimonthly online by MDPI. The British Neuro-Oncology Society (BNOS), the International Society for Infectious Diseases in Obstetrics and Gynaecology (ISIDOG) and the Swiss Union of Laboratory Medicine (SULM) are affiliated with Diagnostics.
