BackgroundRadiographic evaluation for patients with scoliosis using Cobb method is the current gold standard, but radiography has radiation hazards. Several groups have recently demonstrated the feasibility of using 3D ultrasound for the evaluation of scoliosis. Ultrasound imaging is radiation-free, comparatively more accessible, and inexpensive. However, a reliable and valid 3D ultrasound system ready for clinical scoliosis assessment has not yet been reported. Scolioscan is a newly developed system targeted for scoliosis assessment in clinics by using coronal images of spine generated by a 3D ultrasound volume projection imaging method. The aim of this study is to test the reliability of spine deformity measurement of Scolioscan and its validity compared to the gold standard Cobb angle measurements from radiography in adolescent idiopathic scoliosis (AIS) patients.MethodsProspective study divided into two stages: 1) Investigation of intra- and inter- reliability between two operators for acquiring images using Scolioscan and among three raters for measuring spinal curves from those images; 2) Correlation between the Cobb angle obtained from radiography by a medical doctor and the spine curve angle obtained using Scolioscan (Scolioscan angle). The raters for ultrasound images and the doctors for evaluating radiographic images were mutually blinded. The two stages of tests involved 20 (80 % females, total of 26 angles, age of 16.4 ± 2.7 years, and Cobb angle of 27.6 ± 11.8°) and 49 (69 % female, 73 angles, 15.8 ± 2.7 years and 24.8 ± 9.7°) AIS patients, respectively. Intra-class correlation coefficients (ICC) and Bland-Altman plots and root-mean-square differences (RMS) were employed to determine correlations, which interpreted based on defined criteria.ResultsWe demonstrated a very good intra-rater and intra-operator reliability for Scolioscan angle measurement with ICC larger than 0.94 and 0.88, respectively. Very good inter-rater and inter-operator reliability was also demonstrated, with both ICC larger than 0.87. For the thoracic deformity measurement, the RMS were 2.5 and 3.3° in the intra- and inter-operator tests, and 1.5 and 3.6° in the intra- and inter-rater tests, respectively. The RMS differences were 3.1, 3.1, 1.6, 3.7° in the intra- and inter-operator and intra- and inter-rater tests, respectively, for the lumbar angle measurement. Moderate to strong correlations (R2 > 0.72) were observed between the Scolioscan angles and Cobb angles for both the thoracic and lumbar regions. It was noted that the Scolioscan angle slightly underestimated the spinal deformity in comparison with Cobb angle, and an overall regression equation y = 1.1797x (R2 = 0.76) could be used to translate the Scolioscan angle (x) to Cobb angle (y) for this group of patients. The RMS difference between Scolioscan angle and Cobb angle was 4.7 and 6.2°, with and without the correlation using the overall regression equation.ConclusionsWe showed that Scolioscan is reliable for measuring coronal deformity for patients with AIS and appears ...

To diagnose scoliosis, the standing radiograph with Cobb’s method is the gold standard for clinical practice. Recently, three-dimensional (3D) ultrasound imaging, which is radiation-free and inexpensive, has been demonstrated to be reliable for the assessment of scoliosis and validated by several groups. A portable 3D ultrasound system for scoliosis assessment is very much demanded, as it can further extend its potential applications for scoliosis screening, diagnosis, monitoring, treatment outcome measurement, and progress prediction. The aim of this study was to investigate the reliability of a newly developed portable 3D ultrasound imaging system, Scolioscan Air, for scoliosis assessment using coronal images it generated. The system was comprised of a handheld probe and tablet PC linking with a USB cable, and the probe further included a palm-sized ultrasound module together with a low-profile optical spatial sensor. A plastic phantom with three different angle structures built-in was used to evaluate the accuracy of measurement by positioning in 10 different orientations. Then, 19 volunteers with scoliosis (13F and 6M; Age: 13.6 ± 3.2 years) with different severity of scoliosis were assessed. Each subject underwent scanning by a commercially available 3D ultrasound imaging system, Scolioscan, and the portable 3D ultrasound imaging system, with the same posture on the same date. The spinal process angles (SPA) were measured in the coronal images formed by both systems and compared with each other. The angle phantom measurement showed the measured angles well agreed with the designed values, 59.7 ± 2.9 vs. 60 degrees, 40.8 ± 1.9 vs. 40 degrees, and 20.9 ± 2.1 vs. 20 degrees. For the subject tests, results demonstrated that there was a very good agreement between the angles obtained by the two systems, with a strong correlation (R2 = 0.78) for the 29 curves measured. The absolute difference between the two data sets was 2.9 ± 1.8 degrees. In addition, there was a small mean difference of 1.2 degrees, and the differences were symmetrically distributed around the mean difference according to the Bland–Altman test. Scolioscan Air was sufficiently comparable to Scolioscan in scoliosis assessment, overcoming the space limitation of Scolioscan and thus providing wider applications. Further studies involving a larger number of subjects are worthwhile to demonstrate its potential clinical values for the management of scoliosis.

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Purpose Adolescent idiopathic scoliosis (AIS) patients are exposed to 9–10 times more radiation and a fivefold increased lifetime cancer risk. Radiation-free imaging alternatives are needed. Ultrasound imaging of spinal curvature was shown to be accurate, however, systematically underestimating the Cobb angle. The purpose of this study is to create and cross-validate an equation that calculates the expected Cobb angle using ultrasound spinal measurements of AIS patients. Methods Seventy AIS patients with upright radiography and spinal ultrasound were split randomly in a 4:1 ratio to the equation creation (n = 54) or validation (n = 16) group. Ultrasound angles based on the spinous processes shadows were measured automatically by the ultrasound system (Scolioscan, Telefield, Hong Kong). For thoracic and lumbar curves separately, the equation: expected Cobb angle = regression coefficient × ultrasound angle, was created and subsequently cross-validated in the validation group. Results Linear regression analysis between ultrasound angles and radiographic Cobb angles (thoracic: R2 = 0.968, lumbar: R2 = 0.923, p < 0.001) in the creation group resulted in the equations: thoracic Cobb angle = 1.43 × ultrasound angle and lumbar Cobb angle = 1.23 × ultrasound angle. With these equations, expected Cobb angles in the validation group were calculated and showed an excellent correlation with the radiographic Cobb angles (thoracic: R2 = 0.959, lumbar: R2 = 0.936, p < 0.001). The mean absolute differences were 6.5°–7.3°. Bland–Altman plots showed good accuracy and no proportional bias. Conclusion The equations from ultrasound measurements to Cobb angles were valid and accurate. This supports the implementation of ultrasound imaging, possibly leading to less frequent radiography and reducing ionizing radiation in AIS patients.

Radiographic Cobb's angle is the gold standard for evaluation of spinal curvature, however X-ray is ionizing. In contrast, ultrasound is non-ionizing and inexpensive, thus more accessible. However, no study has reported the reliability and accuracy of ultrasound on sagittal curvature analysis. Ultrasound and X-ray scanning were conducted on 16 sets of spine phantoms with different deformities. Intrarater and inter-rater reliability, correlations, mean absolute differences (MAD) and linear regression of Ultrasound spinous process angles (USSPA), X-ray spinous process angles (XSPA) and X-ray Cobb's angles (XCA) together with the intra-operator reliability of USSPA were investigated. In addition, USSPA and XCA of five AIS subjects were scanned using the ultrasound system. In the phantom study, excellent intra-rater and inter-rater reproducibility for the three angles and excellent intraoperator reproducibility for USSPA were demonstrated. Good to moderate correlations were obtained between lumbar XCA and XSPA and between lumbar XCA and USSPA, whereas excellent correlations were observed between the other angles. All three angles indicated positive linear relationships, with MAD of 5.8°, 3.0° and 6.0° for XSPA against XCA, USSPA against XSPA and USSPA against XCA, respectively. The results of the preliminary study demonstrated a high intrareliability for the ultrasound measurements. The measured difference between the USSPA and XCA methods was 6.3° ± 5.4°. It should be noted that ultrasound and X-ray measurements were based on different structures of the vertebrae. The results showed that ultrasound is feasible for measuring sagittal curvature and has the potential for monitoring the curve progression and evaluating sagittal spinal profiles.

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Scoliosis is a widespread medical condition where the spine becomes severely deformed and bends over time. It mostly affects young adults and may have a permanent impact on them. A periodic assessment, using a suitable modality, is necessary for its early detection. Conventionally, the usually employed modalities include X-ray and MRI, which employ ionising radiation and are expensive. Hence, a non-radiating 3D ultrasound imaging technique has been developed as a safe and economic alternative. However, ultrasound produces low-contrast images that are full of speckle noise, and skilled intervention is necessary for their processing. Given the prevalent occurrence of scoliosis and the limitations of scalability of human expert interventions, an automatic, fast, and low-computation assessment technique is being developed for mass scoliosis diagnosis. In this paper, a novel hybridized light-weight convolutional neural network architecture is presented for automatic lateral bony feature identification, which can help to develop a fully-fledged automatic scoliosis detection system. The proposed architecture, Light-convolution Dense Selection U-Net (LDS U-Net), can accurately segment ultrasound spine lateral bony features, from noisy images, thanks to its capabilities of smartly selecting only the useful information and extracting rich deep layer features from the input image. The proposed model is tested using a dataset of 109 spine ultrasound images. The segmentation result of the proposed network is compared with basic U-Net, Attention U-Net, and MultiResUNet using various popular segmentation indices. The results show that LDS U-Net provides a better segmentation performance compared to the other models. Additionally, LDS U-Net requires a smaller number of parameters and less memory, making it suitable for a large-batch screening process of scoliosis without a high computational requirement.