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Adolescent idiopathic scoliosis (AIS) is a three-dimensional curvature of spine. Children with AIS and low bone quality have higher chance to get curve progression leading to bigger spinal curvature. In addition, bone quality affects acoustic impedance of bone, thus influencing the reflection coefficient of ultrasound signal from the soft tissue–bone interface. This study aimed to estimate the bone quality of AIS patients based on the reflection coefficients to determine the correlation of the bone quality with curve severity. A simple bone model was used to develop an equation to calculate the reflection coefficient value. Experiments were conducted on five different phantoms. Acrylic was used to design a vertebral shape to study the effect of surface roughness and inclination, including: smooth flat surface (SFS), smooth curved surface (SCS), rough curved surface (RCS), and the rough curved inclined surface (RCIS). A clinical study with 37 AIS patients were recruited. The estimated reflection coefficient values of plate phantoms agreed well with the predicted values and the maximum error was 6.7%. The reflection coefficients measured from the acrylic-water interface for the SFS, SCS, RCS, RCIS (3° and 5°) were 0.37, 0.33, 0.28, (0.23 and 0.12), respectively. The surface roughness and inclination increased the reflection loss. From the clinical data, the average reflection coefficients for children with AIS were 0.11 and 0.07 for the mild curve group and the moderate curve group, respectively. A moderate linear correlation was found between the reflection coefficients and curve severity ([Formula: see text] = 0.3). Patients with lower bone quality have observed to have larger spinal curvature.

A recent innovation in scoliosis monitoring is the use of ultrasonography, which provides true 3D information in one scan and does not emit ionizing radiation. Measuring the severity of scoliosis on ultrasonographs requires identifying lamina pairs on the most tilted vertebrae, which is difficult and time-consuming. To expedite and automate measurement steps, this paper detailed an automatic convolutional neural network-based algorithm for identifying the laminae on 3D ultrasonographs. The predicted laminae were manually paired to measure the lateral spinal curvature on the coronal view, called the Cobb angle. In total, 130 spinal ultrasonographs of adolescents with idiopathic scoliosis recruited from a scoliosis clinic were selected, with 70 for training and 60 for testing. Data augmentation increased the effective training set size to 140 ultrasonographs. Semi-automatic Cobb measurements were compared to manual measurements on the same ultrasonographs. The semi-automatic measurements demonstrated good inter-method reliability (ICC

Ultrasound spine imaging technique has been applied to the assessment of spine deformity. However, manual measurements of scoliotic angles on ultrasound images are time-consuming and heavily rely on raters experience. The objectives of this study are to construct a fully automatic framework based on Faster R-CNN for detecting vertebral lamina and to measure the fitting spinal curves from the detected lamina pairs. The framework consisted of two closely linked modules: 1) the lamina detector for identifying and locating each lamina pairs on ultrasound coronal images, and 2) the spinal curvature estimator for calculating the scoliotic angles based on the chain of detected lamina. Two hundred ultrasound images obtained from AIS patients were identified and used for the training and evaluation of the proposed method. The experimental results showed the 0.76 AP on the test set, and the Mean Absolute Difference (MAD) between automatic and manual measurement which was within the clinical acceptance error. Meanwhile the correlation between automatic measurement and Cobb angle from radiographs was 0.79. The results revealed that our proposed technique could provide accurate and reliable automatic curvature measurements on ultrasound spine images for spine deformities., Accepted by IUS2021

Adolescent idiopathic scoliosis is a 3D lateral spinal curvature coupled with axial vertebral rotation (AVR). Measuring AVR during clinic is important because it affects treatment options and predicts the risk of scoliosis progression. However, manual measurements are time consuming and have high inter-rater and intra-rater errors. This study aimed to develop a machine learning algorithm based on convolutional neural networks (CNNs) to automatically calculate AVR on posteroanterior radiographs using three different segmentations including spinal column, individual vertebra, and pedicles. Separate labeling and training processes were performed on each of the developed segmentation algorithms. The final machine learning software was tested on 221 vertebrae from 17 spinal radiographs. An experienced rater with over 25 years of experience measured the 221 vertebral rotations manually. By comparing the manual and the fully automatic measurements, 81% (178/221) of the automatic measurements were within the clinical acceptance error (±5°). The mean absolute difference and the standard deviation between the manual and automatic measurements was 4.3° ± 5.7°. Based on the Bland-Altman plot, the manual and automatic measurements had a strong correlation and no bias. The error did not relate to the severity of the rotation. This method is fully automatic, and the result is comparable to others.