Artificial intelligence for adult spinal deformity: current state and future directions

Beyond the Bony Fragment: A Review of Limbus Vertebra

Vertebral limbus is a condition characterized by the intraspongious herniation of a portion of the nucleus pulposus. It is often asymptomatic, but it can sometimes cause nonspecific symptoms such as local pain and muscle spasm, or, in rare cases, radiculopathies, which is why it can be confused with vertebral fractures, spondyloarthropathies, infectious or tumoral processes. Early recognition of this pathology is preferable for a correct diagnosis and adequate treatment, the latter ranging from conservative approaches (such as personalized exercise programs and physical therapy) to surgical interventions reserved for severe cases with nerve compression.

Innovations in Spine Surgery: A Narrative Review of Current Integrative Technologies

Neurosurgical technologies have become increasingly more adaptive, featuring real-time and patient-specific guidance in preoperative, intraoperative, and postoperative settings. This review offers insight into how these integrative innovations compare with conventional approaches in spine surgery, focusing on machine learning (ML), artificial intelligence, augmented reality and virtual reality, and spinal navigation systems. Data on technology applications, diagnostic and procedural accuracy, intraoperative times, radiation exposures, postoperative outcomes, and costs were extracted and compared with conventional methods to assess their advantages and limitations. Preoperatively, augmented reality and virtual reality have applications in surgical training and planning that are more immersive, case specific, and risk-free and have been shown to enhance accuracy and reduce complications. ML algorithms have demonstrated high accuracy in predicting surgical candidacy (up to 92.1%) and tailoring personalized treatments based on patient-specific variables. Intraoperatively, advantages include more accurate pedicle screw insertion (96%-99% with ML), enhanced visualization, reduced radiation exposure (49 μSv with O-arm navigation vs. 556 μSv with fluoroscopy), increased efficiency, and potential for fewer intraoperative complications compared with conventional approaches. Postoperatively, certain ML and artificial intelligence models have outperformed conventional methods in predicting all postoperative complications of >6000 patients as well as predicting variables contributing to in-hospital and 90-day mortality. However, applying these technologies comes with limitations, such as longer operative times (up to 35.6% longer) with navigation, dependency on datasets, costs, accessibility, steep learning curve, and inherent software malfunctions. As these technologies advance, continuing to assess their efficacy and limitations will be crucial to their successful integration within spine surgery.

Digitalization in orthopaedics: a narrative review

Advances in technology and digital tools like the Internet of Things (IoT), artificial intelligence (AI), and sensors are shaping the field of orthopaedic surgery on all levels, from patient care to research and facilitation of logistic processes. Especially the COVID-19 pandemic, with the associated contact restrictions was an accelerator for the development and introduction of telemedical applications and digital alternatives to classical in-person patient care. Digital applications already used in orthopaedic surgery include telemedical support, online video consultations, monitoring of patients using wearables, smart devices, surgical navigation, robotic-assisted surgery, and applications of artificial intelligence in forms of medical image processing, three-dimensional (3D)-modelling, and simulations. In addition to that immersive technologies like virtual, augmented, and mixed reality are increasingly used in training but also rehabilitative and surgical settings. Digital advances can therefore increase the accessibility, efficiency and capabilities of orthopaedic services and facilitate more data-driven, personalized patient care, strengthening the self-responsibility of patients and supporting interdisciplinary healthcare providers to offer for the optimal care for their patients.

Development and Validation of an Online Calculator to Predict Proximal Junctional Kyphosis After Adult Spinal Deformity Surgery Using Machine Learning

OBJECTIVE

Although adult spinal deformity (ASD) surgery aims to restore and maintain alignment, proximal junctional kyphosis (PJK) may occur. While existing scoring systems predict PJK, they predominantly offer a generalized 3-tier risk classification, limiting their utility for nuanced treatment decisions. This study seeks to establish a personalized risk calculator for PJK, aiming to enhance treatment planning precision.

METHODS

Patient data for ASD were sourced from the Korean spinal deformity database. PJK was defined a proximal junctional angle (PJA) of ≥ 20° at the final follow-up, or an increase in PJA of ≥ 10° compared to the preoperative values. Multivariable analysis was performed to identify independent variables. Subsequently, 5 machine learning models were created to predict individualized PJK risk post-ASD surgery. The most efficacious model was deployed as an online and interactive calculator.

RESULTS

From a pool of 201 patients, 49 (24.4%) exhibited PJK during the follow-up period. Through multivariable analysis, postoperative PJA, body mass index, and deformity type emerged as independent predictors for PJK. When testing machine learning models using study results and previously reported variables as hyperparameters, the random forest model exhibited the highest accuracy, reaching 83%, with an area under the receiver operating characteristics curve of 0.76. This model has been launched as a freely accessible tool at: (https://snuspine.shinyapps.io/PJKafterASD/).

CONCLUSION

An online calculator, founded on the random forest model, has been developed to gauge the risk of PJK following ASD surgery. This may be a useful clinical tool for surgeons, allowing them to better predict PJK probabilities and refine subsequent therapeutic strategies.

Artificial Intelligence to Preoperatively Predict Proximal Junction Kyphosis Following Adult Spinal Deformity Surgery: Soft Tissue Imaging May Be Necessary for Accurate Models

STUDY DESIGN

Retrospective cohort.

OBJECTIVE

In a cohort of patients undergoing adult spinal deformity (ASD) surgery, we used artificial intelligence to compare three models of preoperatively predicting radiographic proximal junction kyphosis (PJK) using: (1) traditional demographics and radiographic measurements, (2) raw preoperative scoliosis radiographs, and (3) raw preoperative thoracic magnetic resonance imaging (MRI).

SUMMARY OF BACKGROUND DATA

Despite many proposed risk factors, PJK following ASD surgery remains difficult to predict.

MATERIALS AND METHODS

A single-institution, retrospective cohort study was undertaken for patients undergoing ASD surgery from 2009 to 2021. PJK was defined as a sagittal Cobb angle of upper-instrumented vertebra (UIV) and UIV+2>10° and a postoperative change in UIV/UIV+2>10°. For model 1, a support vector machine was used to predict PJK within 2 years postoperatively using clinical and traditional sagittal/coronal radiographic variables and intended levels of instrumentation. Next, for model 2, a convolutional neural network (CNN) was trained on raw preoperative lateral and posterior-anterior scoliosis radiographs. Finally, for model 3, a CNN was trained on raw preoperative thoracic T1 MRIs.

RESULTS

A total of 191 patients underwent ASD surgery with at least 2-year follow-up and 89 (46.6%) developed radiographic PJK within 2 years. Model 1: Using clinical variables and traditional radiographic measurements, the model achieved a sensitivity: 57.2% and a specificity: 56.3%. Model 2: a CNN with raw scoliosis x-rays predicted PJK with a sensitivity: 68.2% and specificity: 58.3%. Model 3: a CNN with raw thoracic MRIs predicted PJK with average sensitivity: 73.1% and specificity: 79.5%. Finally, an attention map outlined the imaging features used by model 3 elucidated that soft tissue features predominated all true positive PJK predictions.

CONCLUSIONS

The use of raw MRIs in an artificial intelligence model improved the accuracy of PJK prediction compared with raw scoliosis radiographs and traditional clinical/radiographic measurements. The improved predictive accuracy using MRI may indicate that PJK is best predicted by soft tissue degeneration and muscle atrophy.

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.

Advances in artificial intelligence, robotics, augmented and virtual reality in neurosurgery

Neurosurgical practitioners undergo extensive and prolonged training to acquire diverse technical proficiencies, while neurosurgical procedures necessitate a substantial amount of pre-, post-, and intraoperative clinical data acquisition, making decisions, attention, and convalescence. The past decade witnessed an appreciable escalation in the significance of artificial intelligence (AI) in neurosurgery. AI holds significant potential in neurosurgery as it supplements the abilities of neurosurgeons to offer optimal interventional and non-interventional care to patients by improving prognostic and diagnostic outcomes in clinical therapy and assisting neurosurgeons in making decisions while surgical interventions to enhance patient outcomes. Other technologies including augmented reality, robotics, and virtual reality can assist and promote neurosurgical methods as well. Moreover, they play a significant role in generating, processing, as well as storing experimental and clinical data. Also, the usage of these technologies in neurosurgery is able to curtail the number of costs linked with surgical care and extend high-quality health care to a wider populace. This narrative review aims to integrate the results of articles that elucidate the role of the aforementioned technologies in neurosurgery.

Revolutionizing Spinal Care: Current Applications and Future Directions of Artificial Intelligence and Machine Learning

Artificial intelligence (AI) and machine learning (ML) are rapidly becoming integral components of modern healthcare, offering new avenues for diagnosis, treatment, and outcome prediction. This review explores their current applications and potential future in the field of spinal care. From enhancing imaging techniques to predicting patient outcomes, AI and ML are revolutionizing the way we approach spinal diseases. AI and ML have significantly improved spinal imaging by augmenting detection and classification capabilities, thereby boosting diagnostic accuracy. Predictive models have also been developed to guide treatment plans and foresee patient outcomes, driving a shift towards more personalized care. Looking towards the future, we envision AI and ML further ingraining themselves in spinal care with the development of algorithms capable of deciphering complex spinal pathologies to aid decision making. Despite the promise these technologies hold, their integration into clinical practice is not without challenges. Data quality, integration hurdles, data security, and ethical considerations are some of the key areas that need to be addressed for their successful and responsible implementation. In conclusion, AI and ML represent potent tools for transforming spinal care. Thoughtful and balanced integration of these technologies, guided by ethical considerations, can lead to significant advancements, ushering in an era of more personalized, effective, and efficient healthcare.

Artificial intelligence and treatment algorithms in spine surgery

Artificial intelligence (AI) is a set of theories and techniques in which machines are used to simulate human intelligence with complex computer programs. The various machine learning (ML) methods are a subtype of AI. They originate from computer science and use algorithms established from analyzing a database to accomplish certain tasks. Among these methods are decision trees or random forests, support vector machines along with artificial neural networks. Convolutive neural networks were inspired from the visual cortex; they process combinations of information used in image or voice recognition. Deep learning (DL) groups together a set of ML methods and is useful for modeling complex relationships with a high degree of abstraction by using multiple layers of artificial neurons. ML techniques have a growing role in spine surgery. The main applications are the segmentation of intraoperative images for surgical navigation or robotics used for pedicle screw placement, the interpretation of images of intervertebral discs or full spine radiographs, which can be automated using ML algorithms. ML techniques can also be used as aids for surgical decision-making in complex fields, such as preoperative evaluation of adult spinal deformity. ML algorithms "learn" from large clinical databases. They make it possible to establish the intraoperative risk level and make a prognosis on how the postoperative functional scores will change over time as a function of the patient profile. These applications open a new path relative to standard statistical analyses. They make it possible to explore more complex relationships with multiple indirect interactions. In the future, AI algorithms could have a greater role in clinical research, evaluating clinical and surgical practices, and conducting health economics analyses.

The future of artificial intelligence in neurosurgery: A narrative review

Background:

Artificial intelligence (AI) and machine learning (ML) algorithms are on the tremendous rise for being incorporated into the field of neurosurgery. AI and ML algorithms are different from other technological advances as giving the capability for the computer to learn, reason, and problem-solving skills that a human inherits. This review summarizes the current use of AI in neurosurgery, the challenges that need to be addressed, and what the future holds.

Methods:

A literature review was carried out with a focus on the use of AI in the field of neurosurgery and its future implication in neurosurgical research.

Results:

The online literature on the use of AI in the field of neurosurgery shows the diversity of topics in terms of its current and future implications. The main areas that are being studied are diagnostic, outcomes, and treatment models.

Conclusion:

Wonders of AI in the field of medicine and neurosurgery hold true, yet there are a lot of challenges that need to be addressed before its implications can be seen in the field of neurosurgery from patient privacy, to access to high-quality data and overreliance on surgeons on AI. The future of AI in neurosurgery is pointed toward a patient-centric approach, managing clinical tasks, and helping in diagnosing and preoperative assessment of the patients.

Adult Spinal Deformity: A Comprehensive Review of Current Advances and Future Directions

Owing to rapidly changing global demographics, adult spinal deformity (ASD) now accounts for a significant proportion of the Global Burden of Disease. Sagittal imbalance caused by age-related degenerative changes leads to back pain, neurological deficits, and deformity, which negatively affect the health-related quality of life (HRQoL) of patients. Along with the recognized regional, global, and sagittal spinopelvic parameters, poor paraspinal muscle quality has recently been acknowledged as a key determinant of the clinical outcomes of ASD. Although the Scoliosis Research Society-Schwab ASD classification system incorporates the radiological factors related to HRQoL, it cannot accurately predict the mechanical complications. With the rapid advances in surgical techniques, many surgical options for ASD have been developed, ranging from minimally invasive surgery to osteotomies. Therefore, structured patient-specific management is important in surgical decision-making, selecting the proper surgical technique, and to prevent serious complications in patients with ASD. Moreover, utilizing the latest technologies such as robotic-assisted surgery and machine learning, should help in minimizing the surgical risks and complications in the future.

An Evolution Gaining Momentum—The Growing Role of Artificial Intelligence in the Diagnosis and Treatment of Spinal Diseases

In recent years, applications using artificial intelligence have been gaining importance in the diagnosis and treatment of spinal diseases. In our review, we describe the basic features of artificial intelligence which are currently applied in the field of spine diagnosis and treatment, and we provide an orientation of the recent technical developments and their applications. Furthermore, we point out the possible limitations and challenges in dealing with such technological advances. Despite the momentary limitations in practical application, artificial intelligence is gaining ground in the field of spine treatment. As an applying physician, it is therefore necessary to engage with it in order to benefit from those advances in the interest of the patient and to prevent these applications being misused by non-medical partners.