Automatic deep learning-based assessment of spinopelvic coronal and sagittal alignment

Artificial intelligence in musculoskeletal imaging: realistic clinical applications in the next decade

This article will provide a perspective review of the most extensively investigated deep learning (DL) applications for musculoskeletal disease detection that have the best potential to translate into routine clinical practice over the next decade. Deep learning methods for detecting fractures, estimating pediatric bone age, calculating bone measurements such as lower extremity alignment and Cobb angle, and grading osteoarthritis on radiographs have been shown to have high diagnostic performance with many of these applications now commercially available for use in clinical practice. Many studies have also documented the feasibility of using DL methods for detecting joint pathology and characterizing bone tumors on magnetic resonance imaging (MRI). However, musculoskeletal disease detection on MRI is difficult as it requires multi-task, multi-class detection of complex abnormalities on multiple image slices with different tissue contrasts. The generalizability of DL methods for musculoskeletal disease detection on MRI is also challenging due to fluctuations in image quality caused by the wide variety of scanners and pulse sequences used in routine MRI protocols. The diagnostic performance of current DL methods for musculoskeletal disease detection must be further evaluated in well-designed prospective studies using large image datasets acquired at different institutions with different imaging parameters and imaging hardware before they can be fully implemented in clinical practice. Future studies must also investigate the true clinical benefits of current DL methods and determine whether they could enhance quality, reduce error rates, improve workflow, and decrease radiologist fatigue and burnout with all of this weighed against the costs.

Deep learning algorithm for fully automated measurement of sagittal balance in adult spinal deformity

AIM

Deep learning (DL) algorithms can be used for automated analysis of medical imaging. The aim of this study was to assess the accuracy of an innovative, fully automated DL algorithm for analysis of sagittal balance in adult spinal deformity (ASD).

MATERIAL AND METHODS

Sagittal balance (sacral slope, pelvic tilt, pelvic incidence, lumbar lordosis and sagittal vertical axis) was evaluated in 141 preoperative and postoperative radiographs of patients with ASD. The DL, landmark-based measurements, were compared with the ground truth values from validated manual measurements.

RESULTS

The DL algorithm showed an excellent consistency with the ground truth measurements. The intra-class correlation coefficient between the DL and ground truth measurements was 0.71-0.99 for preoperative and 0.72-0.96 for postoperative measurements. The DL detection rate was 91.5% and 84% for preoperative and postoperative images, respectively.

CONCLUSION

This is the first study evaluating a complete automated DL algorithm for analysis of sagittal balance with high accuracy for all evaluated parameters. The excellent accuracy in the challenging pathology of ASD with long construct instrumentation demonstrates the eligibility and possibility for implementation in clinical routine.

Scoliosis

Scoliosis is a three-dimensional spinal deformity that can occur at any age. It may be idiopathic or secondary in children, idiopathic and degenerative in adults. Management of patients with scoliosis is multidisciplinary, involving rheumatologists, radiologists, orthopaedic surgeons, and prosthetists. Imaging plays a central role in diagnosis, including the search for secondary causes, follow-up, and preoperative work-up if surgery is required. Evaluating scoliosis involves obtaining frontal and lateral full-spine radiographs in the standing position, with analysis of coronal and sagittal alignment. For adolescent idiopathic scoliosis, imaging follow-up is often required, accomplished using low-dose stereoradiography such as EOS imaging. For adult degenerative scoliosis, the crucial characteristic is rotatory subluxation, also well detected on radiographs. Magnetic resonance imaging is usually more informative than computed tomography for visualizing associated canal and foraminal stenoses. Radiologists must also have a thorough understanding of postoperative features and complications of scoliosis surgery because aspects can be misleading.

PreOperative Planning for Adult Spinal Deformity Goals: Level Selection and Alignment Goals

Adult Spinal Deformity (ASD) is a complex pathologic condition with significant impact on quality of life, including pain, loss of function, and fatigue. Achieving realignment goals is crucial for long-term results. Reliable preoperative planning strategies, including nomograms, measurement tools, and level selection, are key to maximizing the likelihood of achieving a good outcome following ASD corrective surgery. This review covers recent literature on such strategies, including review of the different targets for realignment and their association with outcomes (both patients-reported outcomes and complications), selection of upper and lower instrumented vertebrae, and the latest innovation in preoperative planning for deformity surgery.