The Diagnosis of Developmental Dysplasia of the Hip From Hip Ultrasonography Images With Deep Learning Methods

Abstract

Background: Hip ultrasonography is very important in the early diagnosis of developmental dysplasia of the hip. The application of deep learning-based medical image analysis to computer-aided diagnosis has the potential to provide decision-making support to clinicians and improve the accuracy and efficiency of various diagnostic and treatment processes. This has encouraged new research and development efforts in computer-aided diagnosis. The aim of this study was to evaluate hip sonograms using computer-assisted deep-learning methods. Methods: The study included 376 sonograms evaluated as normal according to the Graf method, 541 images with dysplasia and 365 images with incorrect probe position. To classify the developmental hip dysplasia ultrasound images, transfer learning was applied with pretrained VGG-16, ResNet-101, MobileNetV2 and GoogLeNet networks. The performances of the networks were evaluated with the performance parameters of accuracy, sensitivity, specificity, precision, F1 score, and AUC (area under the ROC curve).

The diagnosis of femoroacetabular impingement can be made on pelvis radiographs using deep learning methods

“…Previous studies in the literature have also reported that better results are obtained with the VGG-16 model. [7,14,24] Femoroacetabular impingement is recognized as one of the important causes of secondary hip osteoarthritis. It should be kept in mind in the differential diagnosis of diseases such as femoral head avascular necrosis and rheumatic diseases causing pain originating from the hip joint, which can be seen in young individuals.…”

“…Previous studies in the literature have also reported that better results are obtained with the VGG-16 model. [ 7 , 14 , 24 ]…”

“…Most of them have reported the successful use of these methods, particularly in the fields of radiology, pathology, cardiology, dermatology, and ophthalmology. [9][10][11][12][13][14][15][16][17] In a recent study, the differentiation of normal and pathological hips in hip ultrasonography images was successfully made with deep learning methods, and ultrasonography images that were not suitable for evaluation were also successfully identified. [14] In another study, it was shown that sacroiliitis could be diagnosed with pre-trained VGG-16, ResNet-101 and Inception-v3 networks models on pelvic radiographs.…”

“…[9][10][11][12][13][14][15][16][17] In a recent study, the differentiation of normal and pathological hips in hip ultrasonography images was successfully made with deep learning methods, and ultrasonography images that were not suitable for evaluation were also successfully identified. [14] In another study, it was shown that sacroiliitis could be diagnosed with pre-trained VGG-16, ResNet-101 and Inception-v3 networks models on pelvic radiographs. [16] Radiography is the preferred imaging method as the first step for patients presenting with musculoskeletal complaints due to its accessibility, low cost, and ease of interpretation.…”

Use of deep learning techniques for early detection of hip dysplasia in infants through pelvic radiographs

“…La aplicación de algoritmos de aprendizaje profundo ya ha demostrado su eficacia en aumentar la pre-cisión diagnóstica y en reducir la subjetividad que naturalmente acompaña a la interpretación humana de las imágenes médicas. En estudios específicos, como se reporta en la literatura (Atalar et al, 2023), se han comparado diversas arquitecturas de redes neuronales, incluyendo VGG-16, ResNet-101, Mo-bileNetV2 y GoogLeNet, logrando también resultados prometedores con modelos preentrenados como VGG-19. Estas arquitecturas son ampliamente reconocidas en el campo de la visión por computadora y han mostrado su potencial en el análisis de imágenes médicas.…”