Is Radiation-Free Ultrasound Accurate for Quantitative Assessment of Spinal Deformity in Idiopathic Scoliosis (IS): A Detailed Analysis With EOS Radiography on 952 Patients

A review of robot-assisted ultrasound examination: Systems and technology

BACKGROUND

At present, the number and overall level of ultrasound (US) doctors cannot meet the medical needs, and the medical ultrasound robots will largely solve the shortage of medical resources.

METHODS

According to the degree of automation, the handheld, semi-automatic and automatic ultrasound examination robot systems are summarised. Ultrasound scanning path planning and robot control are the keys to ensure that the robot systems can obtain high-quality images. Therefore, the ultrasound scanning path planning and control methods are summarised. The research progress and future trends are discussed.

RESULTS

A variety of ultrasound robot systems have been applied to various medical works. With the continuous improvement of automation, the systems provide high-quality ultrasound images and image guidance for clinicians.

CONCLUSION

Although the development of medical ultrasound robot still faces challenges, with the continuous progress of robot technology and communication technology, medical ultrasound robot will have great development potential and broad application space.

Validity and accuracy of automatic cobb angle measurement on 3D spinal ultrasonographs for children with adolescent idiopathic scoliosis: SOSORT 2024 award winner

PURPOSE

Ultrasonography for scoliosis is a novel imaging method that does not expose children with adolescent idiopathic scoliosis (AIS) to radiation. A single ultrasound scan provides 3D spinal views directly. However, measuring ultrasonograph parameters is challenging, time-consuming, and requires considerable training. This study aimed to validate a machine learning method to measure the coronal curve angle on ultrasonographs automatically.

METHODS

A total of 144 3D spinal ultrasonographs were extracted to train and validate a machine learning model. Among the 144 images, 70 were used for training, and 74 consisted of 144 curves for testing. Automatic coronal curve angle measurements were validated by comparing them with manual measurements performed by an experienced rater. The inter-method intraclass correlation coefficient (ICC2,1), standard error of measurement (SEM), and percentage of measurements within clinical acceptance (≤ 5°) were analyzed.

RESULTS

The automatic method detected 125/144 manually measured curves. The averages of the 125 manual and automatic coronal curve angle measurements were 22.4 ± 8.0° and 22.9 ± 8.7°, respectively. Good reliability was achieved with ICC2,1 = 0.81 and SEM = 1.4°. A total of 75% (94/125) of the measurements were within clinical acceptance. The average measurement time per ultrasonograph was 36 ± 7 s. Additionally, the algorithm displayed the predicted centers of laminae to illustrate the measurement.

CONCLUSION

The automatic algorithm measured the coronal curve angle with moderate accuracy but good reliability. The algorithm's quick measurement time and interpretability can make ultrasound a more accessible imaging method for children with AIS. However, further improvements are needed to bring the method to clinical use.

Monitoring of Curve Progression in Patients with Adolescent Idiopathic Scoliosis Using 3-D Ultrasound

OBJECTIVE

The aim of the work described here was to determine whether 3-D ultrasound can provide results comparable to those of conventional X-ray examination in assessing curve progression in patients with adolescent idiopathic scoliosis (AIS).

METHODS

One hundred thirty-six participants with AIS (42 males and 94 females; age range: 10-18 y, mean age: 14.1 ± 1.9 y) with scoliosis of different severity (Cobb angle range: 10º- 85º, mean: of 24.3 ± 14.4º) were included. Each participant underwent biplanar low-dose X-ray EOS and 3-D ultrasound system scanning with the same posture on the same date. Participants underwent the second assessment at routine clinical follow-up. Manual measurements of scoliotic curvature on ultrasound coronal projection images and posterior-anterior radiographs were expressed as the ultrasound curve angle (UCA) and radiographic Cobb angle (RCA), respectively. RCA and UCA increments ≥5º represented a scoliosis progression detected by X-ray assessment and 3-D ultrasound assessment, respectively.

RESULTS

The sensitivity and specificity of UCA measurement in detecting scoliosis progression were 0.93 and 0.90, respectively. The negative likelihood ratio of the diagnostic test for scoliosis progression by the 3-D ultrasound imaging system was 0.08.

CONCLUSION

The 3-D ultrasound imaging method is a valid technique for detecting coronal curve progression as compared with conventional radiography in follow-up of AIS. Substituting conventional radiography with 3-D ultrasound is effective in reducing the radiation dose to which AIS patients are exposed during their follow-up examinations.

Spine Deformity Assessment for Scoliosis Diagnostics Utilizing Image Processing Techniques: A Systematic Review

Spinal deformity refers to a range of disorders that are defined by anomalous curvature of the spine and may be classified as scoliosis, hypo/hyperlordosis, or hypo/hyperkyphosis. Among these, scoliosis stands out as the most common type of spinal deformity in human beings, and it can be distinguished by abnormal lateral spine curvature accompanied by axial rotation. Accurate identification of spinal deformity is crucial for a person’s diagnosis, and numerous assessment methods have been developed by researchers. Therefore, the present study aims to systematically review the recent works on spinal deformity assessment for scoliosis diagnosis utilizing image processing techniques. To gather relevant studies, a search strategy was conducted on three electronic databases (Scopus, ScienceDirect, and PubMed) between 2012 and 2022 using specific keywords and focusing on scoliosis cases. A total of 17 papers fully satisfied the established criteria and were extensively evaluated. Despite variations in methodological designs across the studies, all reviewed articles obtained quality ratings higher than satisfactory. Various diagnostic approaches have been employed, including artificial intelligence mechanisms, image processing, and scoliosis diagnosis systems. These approaches have the potential to save time and, more significantly, can reduce the incidence of human error. While all assessment methods have potential in scoliosis diagnosis, they possess several limitations that can be ameliorated in forthcoming studies. Therefore, the findings of this study may serve as guidelines for the development of a more accurate spinal deformity assessment method that can aid medical personnel in the real diagnosis of scoliosis.

Assessment of thoracic spinal curvatures in static postures using spatially tracked 3D ultrasound volumes: a proof-of-concept study

The assessment of spinal posture is a difficult endeavour given the lack of identifiable bony landmarks for placement of skin markers. Moreover, potentially significant soft tissue artefacts along the spine further affect the accuracy of marker-based approaches. The objective of this proof-of-concept study was to develop an experimental framework to assess spinal postures by using three-dimensional (3D) ultrasound (US) imaging. A phantom spine model immersed in water was scanned using 3D US in a neutral and two curved postures mimicking a forward flexion in the sagittal plane while the US probe was localised by three electromagnetic tracking sensors attached to the probe head. The obtained anatomical 'coarse' registrations were further refined using an automatic registration algorithm and validated by an experienced sonographer. Spinal landmarks were selected in the US images and validated against magnetic resonance imaging data of the same phantom through image registration. Their position was then related to the location of the tracking sensors identified in the acquired US volumes, enabling the localisation of landmarks in the global coordinate system of the tracking device. Results of this study show that localised 3D US enables US-based anatomical reconstructions comparable to clinical standards and the identification of spinal landmarks in different postures of the spine. The accuracy in sensor identification was 0.49 mm on average while the intra- and inter-observer reliability in sensor identification was strongly correlated with a maximum deviation of 0.8 mm. Mapping of landmarks had a small relative distance error of 0.21 mm (SD = ± 0.16) on average. This study implies that localised 3D US holds the potential for the assessment of full spinal posture by accurately and non-invasively localising vertebrae in space.

Joint Spine Segmentation and Noise Removal From Ultrasound Volume Projection Images With Selective Feature Sharing

Volume Projection Imaging from ultrasound data is a promising technique to visualize spine features and diagnose Adolescent Idiopathic Scoliosis. In this paper, we present a novel multi-task framework to reduce the scan noise in volume projection images and to segment different spine features simultaneously, which provides an appealing alternative for intelligent scoliosis assessment in clinical applications. Our proposed framework consists of two streams: i) A noise removal stream based on generative adversarial networks, which aims to achieve effective scan noise removal in a weakly-supervised manner, i.e., without paired noisy-clean samples for learning; ii) A spine segmentation stream, which aims to predict accurate bone masks. To establish the interaction between these two tasks, we propose a selective feature-sharing strategy to transfer only the beneficial features, while filtering out the useless or harmful information. We evaluate our proposed framework on both scan noise removal and spine segmentation tasks. The experimental results demonstrate that our proposed method achieves promising performance on both tasks, which provides an appealing approach to facilitating clinical diagnosis.

Semi-automatic ultrasound curve angle measurement for adolescent idiopathic scoliosis

PURPOSE

Using X-ray to evaluate adolescent idiopathic scoliosis (AIS) conditions is the clinical gold standard, with potential radiation hazards. 3D ultrasound has demonstrated its validity and reliability of estimating X-ray Cobb angle (XCA) using spinous process angle (SPA), which can be automatically measured. While angle measurement with ultrasound using spine transverse process-related landmarks (UCA) shows better agreed with XCA, its automatic measurement is challenging and not available yet. This research aimed to analyze and measure scoliotic angles through a novel semi-automatic UCA method.

METHODS

100 AIS subjects (age: 15.0 ± 1.9 years, gender: 19 M and 81 F, Cobb: 25.5 ± 9.6°) underwent both 3D ultrasound and X-ray scanning on the same day. Scoliotic angles with XCA and UCA methods were measured manually; and transverse process-related features were identified/drawn for the semi-automatic UCA method. The semi-automatic method measured the spinal curvature with pairs of thoracic transverse processes and lumbar lumps in respective regions.

RESULTS

The new semi-automatic UCA method showed excellent correlations with manual XCA (R² = 0.815: thoracic angles R² = 0.857, lumbar angles R² = 0.787); and excellent correlations with manual UCA (R² = 0.866: thoracic angles R² = 0.921, lumbar angles R² = 0.780). The Bland-Altman plot also showed a good agreement against manual UCA/XCA. The MADs of semi-automatic UCA against XCA were less than 5°, which is clinically insignificant.

CONCLUSION

The semi-automatic UCA method had demonstrated the possibilities of estimating manual XCA and UCA. Further advancement in image processing to detect the vertebral landmarks in ultrasound images could help building a fully automated measurement method.

LEVEL OF EVIDENCE

Level III.

Convolutional Neural Network to Segment Laminae on 3D Ultrasound Spinal Images to Assist Cobb Angle Measurement

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_3,1 = 0.87) and performed better on thoracic (ICC_3,1 = 0.91) than lumbar curves (ICC_3,1 = 0.81). The mean absolute difference and standard deviation between semi-automatic and manual was 3.6° ± 3.0°. In conclusion, the semi-automatic method to measure the Cobb angle on ultrasonographs is feasible and accurate. This is the first algorithm that automates steps of Cobb angle measurement on ultrasonographs.

Using Ultrasound to Screen for Scoliosis to Reduce Unnecessary Radiographic Radiation: A Prospective Diagnostic Accuracy Study on 442 Schoolchildren

Scoliosis screening is important for timely initiation of brace treatment to mitigate curve progression in skeletally immature children and adolescents. School scoliosis screening programs in Hong Kong follow the protocol of referring children screened positive with a scoliometer and Moiré topography for confirmatory standard radiography. Despite being highly sensitive (88%) in detecting those who require specialist referral, the screening program was found to have a false-positive rate >50%, which could lead to unnecessary X-ray radiation. Radiation-free ultrasound has been reported to be valid and reliable for quantitative assessment of curve severity in scoliosis patients. The aim of this prospective diagnostic accuracy study was to determine the accuracy of ultrasound in determining the threshold of referral that requires X-ray for children screened positive with the scoliometer and Moiré topography. Our study recruited 442 schoolchildren with a mean Cobb angle of 14.0 ± 6.6°. The sensitivity and specificity of ultrasound in predicting the correct referral status, confirmed by X-ray, were 92.3% and 51.6%, with positive and negative predictive values of 29.0% and 96.9%, respectively. Receiver operating characteristic curve analysis revealed area under the curve values of 0.735 for ultrasound alone and 0.832 for ultrasound in combination with measurement of angle of trunk rotation. The finding supports the accuracy of using ultrasound to determine referral status, which could result in a >50% reduction of unnecessary radiation for children undergoing scoliosis screening.

Investigation of the Phenomenon of Coronal-Sagittal Curvature Coupling on Curve Progression: An Exploratory Study using 3-D Ultrasound

A 3-D ultrasound system was determined to provide reliable and valid results for scoliosis assessment in the coronal and sagittal planes. The objective of this study was to investigate whether 3-D ultrasound can detect coronal-sagittal coupling and to study its potential effect on curve progression in patients with adolescence idiopathic scoliosis (AIS) as per the traditional Cobb angle classification. Radiographic and ultrasonic coronal and sagittal curvatures of 126 patients with AIS were evaluated. Thoracic kyphosis (TK) and lumbar lordosis (LL) with different coronal deformity were compared correspondingly based on either main thoracic or (thoraco)lumbar curve groups. The TK and LL of patients with single curves were also compared with study the curve effect on sagittal curvatures. A prospective cohort of 51 patients were followed for an average of 23 months for preliminary progression investigation. TKs in patients with larger main thoracic Cobb angles was significantly smaller than those with smaller main thoracic Cobb angles, judging by the results obtained from ultrasound and X-ray. The TKs of patients with only single right main thoracic curves were significantly smaller than those of patients with only single left (thoraco)lumbar curves. In addition, patients with progressive curves were observed to be relative hypokyphotic during early visits.

3D ultrasound imaging provides reliable angle measurement with validity comparable to X-ray in patients with adolescent idiopathic scoliosis

BACKGROUND & OBJECTIVE

The application of ultrasound imaging for spine evaluation could minimize radiation exposure for patients with adolescence idiopathic scoliosis (AIS). A customized three-dimensional (3D) ultrasound imaging system has been demonstrated to provide reliable and valid coronal curvature measurements. However, these measurements were using the spinous processes as anatomical reference, leading to a predictable underestimation of the traditionally used Cobb angles. An alternative 3D ultrasound image reconstruction method was applied to create coronal images with more lateral features for angle measurement. The objective of this study was to test the reliability and the validity of this angle, the ultrasound curve angle (UCA), and compare the UCA with the Cobb angles on X-ray images of patients with AIS.

MATERIALS AND METHODS

This study was divided into: 1) Investigation of intra- and inter-reliability between two raters for measuring the UCA and two operators for acquiring ultrasound images; 2) Investigation of the validity between the radiographic Cobb angle and the UCA. Fifty patients and 164 patients with AIS, were included in the two stages, respectively. Patients underwent bi-planar X-ray and 3D ultrasound scanning on the same day. The proposed UCA was used to measure the coronal curvature from the ultrasound coronal images, which were formed using a newly customized volume projection imaging (VPI) method. The intra-rater/operator and inter-rater and operator reliability of the UCA were tested by intra-class correlation coefficient (ICC) (3,1) and (2,1), respectively. The validity of UCA measurements as compared to radiographic Cobb angles was tested by inter-method ICC (2,1), mean absolute difference (MAD), standard error of measurement (SEM), Pearson correlation coefficient and Bland-Altman statistics. The level of significance was set as 0.05.

RESULTS

Excellent intra-rater and intra-operator (ICC (3,1)≥0.973) and excellent inter-rater and inter-operator reliability (ICC (2,1)≥0.925) for UCA measurement, with overall MAD and SEM no more than 3.5° and 1.7° were demonstrated for both main thoracic and (thoraco)lumbar curvatures. Very good correlations were observed between UCA and Cobb angle for main thoracic (R 2 =0.893) and (thoraco)lumbar (R 2 =0.884) curves. The mean (SD) measurements in terms of radiographic Cobb and UCA were 27.2 ​± ​11.6° and 26.3 ​± ​11.4° for main thoracic curves; and 26.2 ​± ​11.4° and 24.8 ​± ​9.7° for (thoraco)lumbar curve respectively. One hundred sixty-four subjects (33 male and 131 female subjects; 11-18 years of age, mean of 15.1 ​± ​1.9 years) were included for the validity session. Excellent inter-method variations (ICC (2,K) ≥0.933) with overall MAD and SEM no more than 3.0° and 1.5° were demonstrated for both main thoracic and (thoraco)lumbar curvatures. In addition, Bland-Altman plots demonstrated an acceptable agreement between ultrasound and radiographic Cobb measurements.

CONCLUSION

In this study, very good correlations and agreement were demonstrated between the ultrasound and X-ray measurements of the scoliotic curvature. Judging from the promising results of this study, patients with AIS with different severity of curves can be evaluated and monitored by ultrasound imaging, reducing the usage of radiation during follow-ups. This method could also be used for scoliosis screening.The Translational potential of this article: Ultrasound curve angle (UCA) obtained from 3D ultrasound imaging system can provide reliable and valid evaluation on coronal curvature for patients with AIS, without the need of radiation.

Validation of Scolioscan Air-Portable Radiation-Free Three-Dimensional Ultrasound Imaging Assessment System for Scoliosis

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.

Artificial Intelligence-enabled, Real-time Intraoperative Ultrasound Imaging of Neural Structures Within the Psoas: Validation in a Porcine Spine Model

STUDY DESIGN

Experimental in-vivo animal study.

OBJECTIVE

The aim of this study was to evaluate an Artificial Intelligence (AI)-enabled ultrasound imaging system's ability to detect, segment, classify, and display neural and other structures during trans-psoas spine surgery.

SUMMARY OF BACKGROUND DATA

Current methodologies for intraoperatively localizing and visualizing neural structures within the psoas are limited and can impact the safety of lateral lumbar interbody fusion (LLIF). Ultrasound technology, enhanced with AI-derived neural detection algorithms, could prove useful for this task.

METHODS

The study was conducted using an in vivo porcine model (50 subjects). Image processing and machine learning algorithms were developed to detect neural and other anatomic structures within and adjacent to the psoas muscle while using an ultrasound imaging system during lateral lumbar spine surgery (SonoVision,™ Tissue Differentiation Intelligence, USA). The imaging system's ability to detect and classify the anatomic structures was assessed with subsequent tissue dissection. Dice coefficients were calculated to quantify the performance of the image segmentation.

RESULTS

The AI-trained ultrasound system detected, segmented, classified, and displayed nerve, psoas muscle, and vertebral body surface with high sensitivity and specificity. The mean Dice coefficient score for each tissue type was >80%, indicating that the detected region and ground truth were >80% similar to each other. The mean specificity of nerve detection was 92%; for bone and muscle, it was >95%. The accuracy of nerve detection was >95%.

CONCLUSION

This study demonstrates that a combination of AI-derived image processing and machine learning algorithms can be developed to enable real-time ultrasonic detection, segmentation, classification, and display of critical anatomic structures, including neural tissue, during spine surgery. AI-enhanced ultrasound imaging can provide a visual map of important anatomy in and adjacent to the psoas, thereby providing the surgeon with critical information intended to increase the safety of LLIF surgery.Level of Evidence: N/A.

Cross-validation of ultrasound imaging in adolescent idiopathic scoliosis

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.