The infants who have mature hip on ultrasonography but have risk factors of developmental dysplasia of the hip are required radiographic examination

Does cranial bone ossification differ in children with developmental dysplasia of the hip?

OBJECTIVES

This study aims to compare cranial bone ossification between patients with developmental dysplasia of the hip (DDH) and healthy individuals.

PATIENTS AND METHODS

Between September 2021 and April 2022, a total of 60 healthy female individuals (median age: 24.5 months; range, 18 to 36 months) and 56 female DDH patients (median age: 23 months; range, 18 to 35 months) were included. Age, head circumference, weight, height, and patency of the anterior fontanel were measured in groups. Percentiles were classified as very low, low, normal, high and very high. All patients were female and those with abnormal thyroid function test, vitamin D, calcium, phosphate and alkaline phosphatase values were not included in the study. For those diagnosed with DDH, they were included in the group regardless of the type of treatment.

RESULTS

No statistically significant difference was found between the groups in terms of age and weight (p>0.05). The very low and very high head circumferences were more frequent, and the normal head circumferences were less frequent in the DDH group (p<0.05). There was no significant difference between groups in terms of fontanel closure (p>0.05). In open fontanels, no significant difference was found in both groups in terms of age (p>0.05).

CONCLUSION

Our study results showed no significant difference between the fontanel ossifications of children with and without DDH; however, we found that the ossification of the skull bones of children with DDH was different compared to healthy children.

Segmentation of measurable images from standard plane of Graf hip ultrasonograms based on Mask Region-Based Convolutional Neural Network

OBJECTIVES

This study proposed a Mask Region-Based Convolutional Neural Network (R-CNN)-based automatic segmentation to accurately detect the measurable standard plane of Graf hip ultrasonography images via segmentation of the labrum, lower limb of ilium, and the iliac wing.

PATIENTS AND METHODS

The study examined the hip ultrasonograms of 675 infants (205 males, 470 females; mean age: 7±2.8 weeks; range, 3 to 20 weeks) recorded between January 2011 and January 2018. The standard plane newborn hip ultrasound images were classified according to Graf's method by an experienced ultrasonographer. The hips were grouped as type 1, type 2a, type 2b, and type 2c-D. Two hundred seventy-five ultrasonograms were utilized as training data, 30 were validation data, and 370 were test data. The three anatomical regions were simultaneously segmented by Mask-R CNN in the test data and defective ultrasonograms. Automatic instance-based segmentation results were compared with the manual segmentation results of an experienced orthopedic expert. Success rates were calculated using Dice and mean average precision (mAP) metrics.

RESULTS

Of these, 447 Graf type 1, 175 type 2a or 2b, 53 type 2c and D ultrasonograms were utilized. Average success rates with respect to hip types in the whole data were 96.95 and 96.96% according to Dice and mAP methods, respectively. Average success rates with respect to anatomical regions were 97.20 and 97.35% according to Dice and mAP methods, respectively. The highest average success rates were for type 1 hips, with 98.46 and 98.73%, and the iliac wing, with 98.25 and 98.86%, according to Dice and mAP methods, respectively.

CONCLUSION

Mask R-CNN is a robust instance-based method in the segmentation of Graf hip ultrasonograms to delineate the standard plane. The proposed method revealed high success in each type of hip for each anatomic region.

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

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).

RESULTS

The accuracy, sensitivity, specificity, precision, F1 score, and AUC results obtained by testing the VGG-16, ResNet-101, MobileNetV2, and GoogLeNet models showed performance >80%. With the pretrained VGG-19 model, 93%, 93.5%, 96.7%, 92.3%, 92.6%, and 0.99 accuracy, sensitivity, specificity, precision, F1 score, and AUC results were obtained, respectively.

CONCLUSION

In this study, in addition to the ultrasonography images of dysplastic and healthy hips, images were also included of probe malpositioning, and these images were able to be successfully evaluated with deep learning methods. On the sonograms, which provided criteria appropriate for evaluation, successful differentiation could be made of healthy hips and dysplastic hips.

LEVEL OF EVIDENCE

Level-IV; diagnostic studies.