Can artificial intelligence support or even replace physicians in measuring sagittal balance? A validation study on preoperative and postoperative full spine images of 170 patients

Spine surgeon versus AI algorithm full-length radiographic measurements: a validation study of complex adult spinal deformity patients

INTRODUCTION

Spinal measurements play an integral role in surgical planning for a variety of spine procedures. Full-length imaging eliminates distortions that can occur with stitched images. However, these images take radiologists significantly longer to read than conventional radiographs. Artificial intelligence (AI) image analysis software that can make such measurements quickly and reliably would be advantageous to surgeons, radiologists, and the entire health system.

MATERIALS AND METHODS

Institutional Review Board approval was obtained for this study. Preoperative full-length standing anterior-posterior and lateral radiographs of patients that were previously measured by fellowship-trained spine surgeons at our institution were obtained. The measurements included lumbar lordosis (LL), greatest coronal Cobb angle (GCC), pelvic incidence (PI), coronal balance (CB), and T1-pelvic angle (T1PA). Inter-rater intra-class correlation (ICC) values were calculated based on an overlapping sample of 10 patients measured by surgeons. Full-length standing radiographs of an additional 100 patients were provided for AI software training. The AI algorithm then measured the radiographs and ICC values were calculated.

RESULTS

ICC values for inter-rater reliability between surgeons were excellent and calculated to 0.97 for LL (95% CI 0.88-0.99), 0.78 (0.33-0.94) for GCC, 0.86 (0.55-0.96) for PI, 0.99 for CB (0.93-0.99), and 0.95 for T1PA (0.82-0.99). The algorithm computed the five selected parameters with ICC values between 0.70 and 0.94, indicating excellent reliability. Exemplary for the comparison of AI and surgeons, the ICC for LL was 0.88 (95% CI 0.83-0.92) and 0.93 for CB (0.90-0.95). GCC, PI, and T1PA could be determined with ICC values of 0.81 (0.69-0.87), 0.70 (0.60-0.78), and 0.94 (0.91-0.96) respectively.

CONCLUSIONS

The AI algorithm presented here demonstrates excellent reliability for most of the parameters and good reliability for PI, with ICC values corresponding to measurements conducted by experienced surgeons. In future, it may facilitate the analysis of large data sets and aid physicians in diagnostics, pre-operative planning, and post-operative quality control.

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.

Implementation of cloud computing in the German healthcare system

With the advent of artificial intelligence and Big Data - projects, the necessity for a transition from analog medicine to modern-day solutions such as cloud computing becomes unavoidable. Even though this need is now common knowledge, the process is not always easy to start. Legislative changes, for example at the level of the European Union, are helping the respective healthcare systems to take the necessary steps. This article provides an overview of how a German university hospital is dealing with European data protection laws on the integration of cloud computing into everyday clinical practice. By describing our model approach, we aim to identify opportunities and possible pitfalls to sustainably influence digitization in Germany.

Analysis of sagittal parameters for easier and more accurate determination of cervical spine alignment

Cross-sectional comparative study. This study aimed to analyze the role of cervical parameters, in terms of the perception process, when evaluating cervical sagittal balance on an X-ray image. Reports on the role of cervical parameters in the perception of cervical sagittal balance have not been made. The study included 4 board-certified neurosurgeons and 6 residents of a neurosurgical department. They were instructed to answer a total of 40 questions. The parameter that was the most helpful in deriving the answer was checked. The correct answer rate, dependency on the parameter, and correct answer contribution of the parameter were analyzed. Among the various parameters, 5 parameters [C2-7 angle (C2-7A), T1 slope minus cervical lordosis (T1s-CL), C2 slope (C2s), C7 slope (C7s), and C2-7 sagittal vertical axis) were selected. The simple parameter (C2s, C7s) has a higher dependency and correct answer contribution than the complex parameter (C2-7A, T1s-CL). The angular (C2-7A, T1s-CL, C2s, C7s) parameters have a higher dependency; however, both the length and angular parameters correct answer contribution were similar. The cervical parameters that have simpler properties were highly preferred and had a lower perception error.

Emerging Technologies within Spine Surgery

New innovations within spine surgery continue to propel the field forward. These technologies improve surgeons' understanding of their patients and allow them to optimize treatment planning both in the operating room and clinic. Additionally, changes in the implants and surgeon practice habits continue to evolve secondary to emerging biomaterials and device design. With ongoing advancements, patients can expect enhanced preoperative decision-making, improved patient outcomes, and better intraoperative execution. Additionally, these changes may decrease many of the most common complications following spine surgery in order to reduce morbidity, mortality, and the need for reoperation. This article reviews some of these technological advancements and how they are projected to impact the field. As the field continues to advance, it is vital that practitioners remain knowledgeable of these changes in order to provide the most effective treatment possible.

Automatic Calculation of Cervical Spine Parameters Using Deep Learning: Development and Validation on an External Dataset

STUDY DESIGN

Retrospective data analysis.

OBJECTIVES

This study aims to develop a deep learning model for the automatic calculation of some important spine parameters from lateral cervical radiographs.

METHODS

We collected two datasets from two different institutions. The first dataset of 1498 images was used to train and optimize the model to find the best hyperparameters while the second dataset of 79 images was used as an external validation set to evaluate the robustness and generalizability of our model. The performance of the model was assessed by calculating the median absolute errors between the model prediction and the ground truth for the following parameters: T1 slope, C7 slope, C2-C7 angle, C2-C6 angle, Sagittal Vertical Axis (SVA), C0-C2, Redlund-Johnell distance (RJD), the cranial tilting (CT) and the craniocervical angle (CCA).

RESULTS

Regarding the angles, we found median errors of 1.66° (SD 2.46°), 1.56° (1.95°), 2.46° (SD 2.55), 1.85° (SD 3.93°), 1.25° (SD 1.83°), .29° (SD .31°) and .67° (SD .77°) for T1 slope, C7 slope, C2-C7, C2-C6, C0-C2, CT, and CCA respectively. As concerns the distances, we found median errors of .55 mm (SD .47 mm) and .47 mm (.62 mm) for SVA and RJD respectively.

CONCLUSIONS

In this work, we developed a model that was able to accurately predict cervical spine parameters from lateral cervical radiographs. In particular, the performances on the external validation set demonstrate the robustness and the high degree of generalizability of our model on images acquired in a different institution.

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.

Proximal Junctional Failure in Adult Spinal Deformity Surgery: An In-depth Review

Adult spinal deformity (ASD) surgery aims to correct abnormal spinal curvature in adults, leading to improved functionality and reduced pain. However, this surgery is associated with various complications, one of which is proximal junctional failure (PJF). PJF can have a significant impact on a patient's quality of life, necessitating a comprehensive understanding of its causes and the development of effective management strategies. This review aims to provide an in-depth understanding of PJF in ASD surgery. PJF is a complex complication resulting from a multitude of factors including patient characteristics, surgical techniques, and postoperative management. Age, osteoporosis, overcorrection of sagittal alignment, and poor bone quality are identified as significant risk factors. The clinical implications of PJF are substantial, often requiring revision surgery and causing a considerable decrease in patients' quality of life. Prevention strategies include careful preoperative planning, appropriate patient selection, and optimization of surgical techniques. Treatment often necessitates a multifaceted approach, including surgical intervention and the management of underlying risk factors. Predictive modeling is an emerging field that may offer a promising avenue for the risk stratification of patients and individualized preventive strategies. A thorough understanding of PJF's pathogenesis, risk factors, and clinical implications is essential for surgeons involved in ASD surgery. Current preventive measures and treatment strategies aim to mitigate the risk and manage the complications of PJF, but the complication cannot be entirely prevented. Future research should focus on the development of more effective preventive and treatment strategies, and predictive models could be valuable in this pursuit.

Novel AI-Based Algorithm for the Automated Computation of Coronal Parameters in Adolescent Idiopathic Scoliosis Patients: A Validation Study on 100 Preoperative Full Spine X-Rays

STUDY DESIGN

Retrospective, mono-centric cohort research study.

OBJECTIVES

The purpose of this study is to validate a novel artificial intelligence (AI)-based algorithm against human-generated ground truth for radiographic parameters of adolescent idiopathic scoliosis (AIS).

METHODS

An AI-algorithm was developed that is capable of detecting anatomical structures of interest (clavicles, cervical, thoracic, lumbar spine and sacrum) and calculate essential radiographic parameters in AP spine X-rays fully automatically. The evaluated parameters included T1-tilt, clavicle angle (CA), coronal balance (CB), lumbar modifier, and Cobb angles in the proximal thoracic (C-PT), thoracic, and thoracolumbar regions. Measurements from 2 experienced physicians on 100 preoperative AP full spine X-rays of AIS patients were used as ground truth and to evaluate inter-rater and intra-rater reliability. The agreement between human raters and AI was compared by means of single measure Intra-class Correlation Coefficients (ICC; absolute agreement; >.75 rated as excellent), mean error and additional statistical metrics.

RESULTS

The comparison between human raters resulted in excellent ICC values for intra- (range: .97-1) and inter-rater (.85-.99) reliability. The algorithm was able to determine all parameters in 100% of images with excellent ICC values (.78-.98). Consistently with the human raters, ICC values were typically smallest for C-PT (eg, rater 1A vs AI: .78, mean error: 4.7°) and largest for CB (.96, -.5 mm) as well as CA (.98, .2°).

CONCLUSIONS

The AI-algorithm shows excellent reliability and agreement with human raters for coronal parameters in preoperative full spine images. The reliability and speed offered by the AI-algorithm could contribute to the efficient analysis of large datasets (eg, registry studies) and measurements in clinical practice.

Application of artificial intelligence to imaging interpretations in the musculoskeletal area: Where are we? Where are we going?

The interest of researchers, clinicians and radiologists, in artificial intelligence (AI) continues to grow. Deep learning is a subset of machine learning, in which the computer algorithm itself can determine the optimal imaging features to answer a clinical question. Convolutional neural networks are the most common architecture for performing deep learning on medical images. The various musculoskeletal applications of deep learning are the detection of abnormalities on X-rays or cross-sectional images (CT, MRI), for example the detection of fractures, meniscal tears, anterior cruciate ligament tears, degenerative lesions of the spine, bone metastases, classification of e.g., dural sac stenosis, degeneration of intervertebral discs, assessment of skeletal age, and segmentation, for example of cartilage. Software developments are already impacting the daily practice of orthopedic imaging by automatically detecting fractures on radiographs. Improving image acquisition protocols, improving the quality of low-dose CT images, reducing acquisition times in MRI, or improving MR image resolution is possible through deep learning. Deep learning offers an automated way to offload time-consuming manual processes and improve practitioner performance. This article reviews the current state of AI in musculoskeletal imaging.