Intelligence-Based Spine Care Model: A New Era of Research and Clinical Decision-Making

SPINE20 Recommendations 2024 -Spinal Disability: Social Inclusion as a Key to Prevention and Management

Spine disorders are the leading cause of disability worldwide. To promote social inclusion, it is essential to ensure that people can participate in their societies by improving their ability, opportunities, and dignity, through access to high-quality, evidence-based, and affordable spine services for all.To achieve this goal, SPINE20 recommends six actions.- SPINE20 recommends that G20 countries deliver evidence-based education to the community health workers and primary care clinicians to promote best practice for spine health, especially in underserved communities.- SPINE20 recommends that G20 countries deliver evidence-based, high-quality, cost-effective spine care interventions that are accessible, affordable and beneficial to patients.- SPINE20 recommends that G20 countries invest in Health Policy and System Research (HPSR) to generate evidence to develop and implement policies aimed at integrating rehabilitation in primary care to improve spine health.- SPINE20 recommends that G20 countries support ongoing research initiatives on digital technologies including artificial intelligence, regulate digital technologies, and promote evidence-based, ethical digital solutions in all aspects of spine care, to enrich patient care with high value and quality.- SPINE20 recommends that G20 countries prioritize social inclusion by promoting equitable access to comprehensive spine care through collaborations with healthcare providers, policymakers, and community organizations.- SPINE20 recommends that G20 countries prioritize spine health to improve the well-being and productivity of their populations. Government health systems are expected to create a healthier, more productive, and equitable society for all through collaborative efforts and sustained investment in evidence-based care and promotion of spine health.

Current Applications and Future Implications of Artificial Intelligence in Spine Surgery and Research: A Narrative Review and Commentary

STUDY DESIGN

Narrative review.

OBJECTIVES

Artificial intelligence (AI) is being increasingly applied to the domain of spine surgery. We present a review of AI in spine surgery, including its use across all stages of the perioperative process and applications for research. We also provide commentary regarding future ethical considerations of AI use and how it may affect surgeon-industry relations.

METHODS

We conducted a comprehensive literature review of peer-reviewed articles that examined applications of AI during the pre-, intra-, or postoperative spine surgery process. We also discussed the relationship among AI, spine industry partners, and surgeons.

RESULTS

Preoperatively, AI has been mainly applied to image analysis, patient diagnosis and stratification, decision-making. Intraoperatively, AI has been used to aid image guidance and navigation. Postoperatively, AI has been used for outcomes prediction and analysis. AI can enable curation and analysis of huge datasets that can enhance research efforts. Large amounts of data are being accrued by industry sources for use by their AI platforms, though the inner workings of these datasets or algorithms are not well known.

CONCLUSIONS

AI has found numerous uses in the pre-, intra-, or postoperative spine surgery process, and the applications of AI continue to grow. The clinical applications and benefits of AI will continue to be more fully realized, but so will certain ethical considerations. Making industry-sponsored databases open source, or at least somehow available to the public, will help alleviate potential biases and obscurities between surgeons and industry and will benefit patient care.

Virtual Clinical Trials - Implications of Computer Simulations and Artificial Intelligence for Musculoskeletal Research

ABSTRACT

In silico clinical trials, particularly when augmented with artificial intelligence methods, represent an innovative approach with much to offer, particularly in the musculoskeletal field. They are a cost-effective, efficient, and ethical means of evaluating treatments and interventions by supplementing and/or augmenting traditional randomized controlled trials (RCTs). While they are not a panacea and should not replace traditional RCTs, their integration into the research process promises to accelerate medical advancements and improve patient outcomes. To accomplish this, a multidisciplinary approach is needed, and collaboration is instrumental. With advances in computing and analytical prowess, and by adhering to the tenets of team science, realization of such a novel integrative approach toward clinical trials may not be far from providing far-reaching contributions to medical research. As such, by harnessing the power of in silico clinical trials, investigators can potentially unlock new possibilities in treatment and intervention for ultimately improving patient care and outcomes.

Evaluating Skill-Mix Models of Care: A Rapid Scoping Review of Measures and Outcomes

OBJECTIVE

To synthesize the literature on measures and outcomes for skill-mix models of care.

BACKGROUND

To address the human health resource crisis, changes to skill mix within models of care are being implemented emphasizing the need to synthesize evaluation methods for skill-mix models in the future.

METHODS

A scoping review of the literature using a rigorous search strategy and selection process was completed to identify articles that examined skill-mix models in an effort to identify related concepts.

RESULTS

Ten studies examined skill-mix models. Areas of measurement in assessing the impact of skill-mix models included patient outcomes, patient satisfaction, nurse satisfaction, cost, and nurse perceptions of role changes, model effectiveness, and quality of care. Studies examining nurse satisfaction, patient satisfaction, and/or cost generally reported improvements upon skill-mix model implementation. Studies examining patient outcomes related to skill mix were inconsistent.

CONCLUSIONS

Factors for consideration upon implementation of a skill-mix change include education of role clarity, the number of unregulated staff who require supervision, and professional practice support.

A Bibliometric Analysis of Artificial Intelligence Applications in Spine Care

BACKGROUND

 With the rapid development of science and technology, artificial intelligence (AI) has been widely used in the diagnosis and prognosis of various spine diseases. It has been proved that AI has a broad prospect in accurate diagnosis and treatment of spine disorders.

METHODS

 On May 7, 2022, the Web of Science (WOS) Core Collection database was used to identify the documents on the application of AI in the field of spine care. HistCite and VOSviewer were used for citation analysis and visualization mapping.

RESULTS

 A total of 693 documents were included in the final analysis. The most prolific authors were Karhade A.V. and Schwab J.H. United States was the most productive country. The leading journal was Spine. The most frequently used keyword was spinal. The most prolific institution was Northwestern University in Illinois, USA. Network visualization map showed that United States was the largest network of international cooperation. The keyword "machine learning" had the strongest total link strengths (TLS) and largest number of occurrences. The latest trends suggest that AI for the diagnosis of spine diseases may receive widespread attention in the future.

CONCLUSIONS

 AI has a wide range of application in the field of spine care, and an increasing number of scholars are committed to research on the use of AI in the field of spine care. Bibliometric analysis in the field of AI and spine provides an overall perspective, and the appreciation and research of these influential publications are useful for future research.

Gait analysis using digital biomarkers including smart shoes in lumbar spinal canal stenosis: a scoping review

Lumbar spinal canal stenosis (LSS) is characterized by gait abnormalities, and objective quantitative gait analysis is useful for diagnosis and treatment. This review aimed to provide a review of objective quantitative gait analysis in LSS and note the current status and potential of smart shoes in diagnosing and treating LSS. The characteristics of gait deterioration in LSS include decreased gait velocity and asymmetry due to neuropathy (muscle weakness and pain) in the lower extremities. Previous laboratory objective and quantitative gait analyses mainly comprised marker-based three-dimensional motion analysis and ground reaction force. However, workforce, time, and costs pose some challenges. Recent developments in wearable sensor technology and markerless motion analysis systems have made gait analysis faster, easier, and less expensive outside the laboratory. Smart shoes can provide more accurate gait information than other wearable sensors. As only a few reports exist on gait disorders in patients with LSS, future studies should focus on the accuracy and cost-effectiveness of gait analysis using smart shoes.

AOA Critical Issues Symposium: Shaping the Impact of Artificial Intelligence within Orthopaedic Surgery

ABSTRACT

Artificial intelligence (AI) is a broad term that is widely used but inconsistently understood. It refers to the ability of any machine to exhibit human-like intelligence by making decisions, solving problems, or learning from experience. With its ability to rapidly process large amounts of information, AI has already transformed many industries such as entertainment, transportation, and communications through consumer-facing products and business-to-business applications. Given its potential, AI is also anticipated to impact the practice of medicine and the delivery of health care. Interest in AI-based techniques has grown rapidly within the orthopaedic community, resulting in an increasing number of publications on this topic. Topics of interest have ranged from the use of AI for imaging interpretation to AI-based techniques for predicting postoperative outcomes.The highly technical and data-driven nature of orthopaedic surgery creates the potential for AI, and its subdisciplines machine learning (ML) and deep learning (DL), to fundamentally transform our understanding of musculoskeletal care. However, AI-based techniques are not well known to most orthopaedic surgeons, nor are they taught with the same level of insight and critical thinking as traditional statistical methodology. With a clear understanding of the science behind AI-based techniques, orthopaedic surgeons will be able to identify the potential pitfalls of the application of AI to musculoskeletal health. Additionally, with increased understanding of AI, surgeons and their patients may have more trust in the results of AI-based analytics, thereby expanding the potential use of AI in clinical care and amplifying the impact it could have in improving quality and value. The purpose of this American Orthopaedic Association (AOA) symposium was to facilitate understanding and development of AI and AI-based techniques within orthopaedic surgery by defining common terminology related to AI, demonstrating the existing clinical utility of AI, and presenting future applications of AI in surgical care.

Personalized Medicine in Orthopaedic Surgery: The Case of Spine Surgery

Personalized medicine has made a tremendous impact on patient care. Although initially, it revolutionized pharmaceutical development and targeted therapies in oncology, it has also made an important impact in orthopaedic surgery. The field of spine surgery highlights the effect of personalized medicine because the improved understanding of spinal pathologies and technological innovations has made personalized medicine a key component of patient care. There is evidence for several of these advancements to support their usage in improving patient care. Proper understanding of normative spinal alignment and surgical planning software has enabled surgeons to predict postoperative alignment accurately. Furthermore, 3D printing technologies have demonstrated the ability to improve pedicle screw placement accuracy compared with free-hand techniques. Patient-specific, precontoured rods have shown improved biomechanical properties, which reduces the risk of postoperative rod fractures. Moreover, approaches such as multidisciplinary evaluations tailored to specific patient needs have demonstrated the ability to decrease complications. Personalized medicine has shown the ability to improve care in all phases of surgical management, and several of these approaches are now readily available to orthopaedic surgeons.

External Validation of SpineNet, an Open-Source Deep Learning Model for Grading Lumbar Disk Degeneration MRI Features, Using the Northern Finland Birth Cohort 1966

STUDY DESIGN

This is a retrospective observational study to externally validate a deep learning image classification model.

OBJECTIVE

Deep learning models such as SpineNet offer the possibility of automating the process of disk degeneration (DD) classification from magnetic resonance imaging (MRI). External validation is an essential step to their development. The aim of this study was to externally validate SpineNet predictions for DD using Pfirrmann classification and Modic changes (MCs) on data from the Northern Finland Birth Cohort 1966 (NFBC1966).

SUMMARY OF DATA

We validated SpineNet using data from 1331 NFBC1966 participants for whom both lumbar spine MRI data and consensus DD gradings were available.

MATERIALS AND METHODS

SpineNet returned Pfirrmann grade and MC presence from T2-weighted sagittal lumbar MRI sequences from NFBC1966, a data set geographically and temporally separated from its training data set. A range of agreement and reliability metrics were used to compare predictions with expert radiologists. Subsets of data that match SpineNet training data more closely were also tested.

RESULTS

Balanced accuracy for DD was 78% (77%-79%) and for MC 86% (85%-86%). Interrater reliability for Pfirrmann grading was Lin concordance correlation coefficient=0.86 (0.85-0.87) and Cohen κ=0.68 (0.67-0.69). In a low back pain subset, these reliability metrics remained largely unchanged. In total, 20.83% of disks were rated differently by SpineNet compared with the human raters, but only 0.85% of disks had a grade difference >1. Interrater reliability for MC detection was κ=0.74 (0.72-0.75). In the low back pain subset, this metric was almost unchanged at κ=0.76 (0.73-0.79).

CONCLUSIONS

In this study, SpineNet has been benchmarked against expert human raters in the research setting. It has matched human reliability and demonstrates robust performance despite the multiple challenges facing model generalizability.

The role of Artificial intelligence in the assessment of the spine and spinal cord

Artificial intelligence (AI) application development is underway in all areas of radiology where many promising tools are focused on the spine and spinal cord. In the past decade, multiple spine AI algorithms have been created based on radiographs, computed tomography, and magnetic resonance imaging. These algorithms have wide-ranging purposes including automatic labeling of vertebral levels, automated description of disc degenerative changes, detection and classification of spine trauma, identification of osseous lesions, and the assessment of cord pathology. The overarching goals for these algorithms include improved patient throughput, reducing radiologist workload burden, and improving diagnostic accuracy. There are several pre-requisite tasks required in order to achieve these goals, such as automatic image segmentation, facilitating image acquisition and postprocessing. In this narrative review, we discuss some of the important imaging AI solutions that have been developed for the assessment of the spine and spinal cord. We focus on their practical applications and briefly discuss some key requirements for the successful integration of these tools into practice. The potential impact of AI in the imaging assessment of the spine and cord is vast and promises to provide broad reaching improvements for clinicians, radiologists, and patients alike.

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.

Predicting surgical operative time in primary total knee arthroplasty utilizing machine learning models

BACKGROUND

Prolonged surgical operative time is associated with postoperative adverse outcomes following total knee arthroplasty (TKA). Increasing operating room efficiency necessitates the accurate prediction of surgical operative time for each patient. One potential way to increase the accuracy of predictions is to use advanced predictive analytics, such as machine learning. The aim of this study is to use machine learning to develop an accurate predictive model for surgical operative time for patients undergoing primary total knee arthroplasty.

METHODS

A retrospective chart review of electronic medical records was conducted to identify patients who underwent primary total knee arthroplasty at a tertiary referral center. Three machine learning algorithms were developed to predict surgical operative time and were assessed by discrimination, calibration and decision curve analysis. Specifically, we used: (1) Artificial Neural Networks (ANNs), (2) Random Forest (RF), and (3) K-Nearest Neighbor (KNN).

RESULTS

We analyzed the surgical operative time for 10,021 consecutive patients who underwent primary total knee arthroplasty. The neural network model achieved the best performance across discrimination (AUC = 0.82), calibration and decision curve analysis for predicting surgical operative time. Based on this algorithm, younger age (< 45 years), tranexamic acid non-usage, and a high BMI (> 40 kg/m²) were the strongest predictors associated with surgical operative time.

CONCLUSIONS

This study shows excellent performance of machine learning models for predicting surgical operative time in primary total knee arthroplasty. The accurate estimation of surgical duration is important in enhancing OR efficiency and identifying patients at risk for prolonged surgical operative time.

LEVEL OF EVIDENCE

Level III, case control retrospective analysis.

Artificial intelligence in spine care: current applications and future utility

PURPOSE

The field of artificial intelligence is ever growing and the applications of machine learning in spine care are continuously advancing. Given the advent of the intelligence-based spine care model, understanding the evolution of computation as it applies to diagnosis, treatment, and adverse event prediction is of great importance. Therefore, the current review sought to synthesize findings from the literature at the interface of artificial intelligence and spine research.

METHODS

A narrative review was performed based on the literature of three databases (MEDLINE, CINAHL, and Scopus) from January 2015 to March 2021 that examined historical and recent advancements in the understanding of artificial intelligence and machine learning in spine research. Studies were appraised for their role in, or description of, advancements within image recognition and predictive modeling for spinal research. Only English articles that fulfilled inclusion criteria were ultimately incorporated in this review.

RESULTS

This review briefly summarizes the history and applications of artificial intelligence and machine learning in spine. Three basic machine learning training paradigms: supervised learning, unsupervised learning, and reinforced learning are also discussed. Artificial intelligence and machine learning have been utilized in almost every facet of spine ranging from localization and segmentation techniques in spinal imaging to pathology specific algorithms which include but not limited to; preoperative risk assessment of postoperative complications, screening algorithms for patients at risk of osteoporosis and clustering analysis to identify subgroups within adolescent idiopathic scoliosis. The future of artificial intelligence and machine learning in spine surgery is also discussed with focusing on novel algorithms, data collection techniques and increased utilization of automated systems.

CONCLUSION

Improvements to modern-day computing and accessibility to various imaging modalities allow for innovative discoveries that may arise, for example, from management. Given the imminent future of AI in spine surgery, it is of great importance that practitioners continue to inform themselves regarding AI, its goals, use, and progression. In the future, it will be critical for the spine specialist to be able to discern the utility of novel AI research, particularly as it continues to pervade facets of everyday spine surgery.

Artificial intelligence in predicting early-onset adjacent segment degeneration following anterior cervical discectomy and fusion

PURPOSE

Anterior cervical discectomy and fusion (ACDF) is a common surgical treatment for degenerative disease in the cervical spine. However, resultant biomechanical alterations may predispose to early-onset adjacent segment degeneration (EO-ASD), which may become symptomatic and require reoperation. This study aimed to develop and validate a machine learning (ML) model to predict EO-ASD following ACDF.

METHODS

Retrospective review of prospectively collected data of patients undergoing ACDF at a quaternary referral medical center was performed. Patients > 18 years of age with > 6 months of follow-up and complete pre- and postoperative X-ray and MRI imaging were included. An ML-based algorithm was developed to predict EO-ASD based on preoperative demographic, clinical, and radiographic parameters, and model performance was evaluated according to discrimination and overall performance.

RESULTS

In total, 366 ACDF patients were included (50.8% male, mean age 51.4 ± 11.1 years). Over 18.7 ± 20.9 months of follow-up, 97 (26.5%) patients developed EO-ASD. The model demonstrated good discrimination and overall performance according to precision (EO-ASD: 0.70, non-ASD: 0.88), recall (EO-ASD: 0.73, non-ASD: 0.87), accuracy (0.82), F1-score (0.79), Brier score (0.203), and AUC (0.794), with C4/C5 posterior disc bulge, C4/C5 anterior disc bulge, C6 posterior superior osteophyte, presence of osteophytes, and C6/C7 anterior disc bulge identified as the most important predictive features.

CONCLUSIONS

Through an ML approach, the model identified risk factors and predicted development of EO-ASD following ACDF with good discrimination and overall performance. By addressing the shortcomings of traditional statistics, ML techniques can support discovery, clinical decision-making, and precision-based spine care.

Mechanisms and clinical implications of intervertebral disc calcification

Low back pain is a leading cause of disability worldwide. Intervertebral disc (IVD) degeneration is often associated with low back pain but is sometimes asymptomatic. IVD calcification is an often overlooked disc phenotype that might have considerable clinical impact. IVD calcification is not a rare finding in ageing or in degenerative and scoliotic spinal conditions, but is often ignored and under-reported. IVD calcification may lead to stiffer IVDs and altered segmental biomechanics, more severe IVD degeneration, inflammation and low back pain. Calcification is not restricted to the IVD but is also observed in the degeneration of other cartilaginous tissues, such as joint cartilage, and is involved in the tissue inflammatory process. Furthermore, IVD calcification may also affect the vertebral endplate, leading to Modic changes (non-neoplastic subchondral vertebral bone marrow lesions) and the generation of pain. Such effects in the spine might develop in similar ways to the development of subchondral marrow lesions of the knee, which are associated with osteoarthritis-related pain. We propose that IVD calcification is a phenotypic biomarker of clinically relevant disc degeneration and endplate changes. As IVD calcification has implications for the management and prognosis of degenerative spinal changes and could affect targeted therapeutics and regenerative approaches for the spine, awareness of IVD calcification should be raised in the spine community.

Artificial Intelligence and Computer Aided Diagnosis in Chronic Low Back Pain: A Systematic Review

Low Back Pain (LBP) is currently the first cause of disability in the world, with a significant socioeconomic burden. Diagnosis and treatment of LBP often involve a multidisciplinary, individualized approach consisting of several outcome measures and imaging data along with emerging technologies. The increased amount of data generated in this process has led to the development of methods related to artificial intelligence (AI), and to computer-aided diagnosis (CAD) in particular, which aim to assist and improve the diagnosis and treatment of LBP. In this manuscript, we have systematically reviewed the available literature on the use of CAD in the diagnosis and treatment of chronic LBP. A systematic research of PubMed, Scopus, and Web of Science electronic databases was performed. The search strategy was set as the combinations of the following keywords: “Artificial Intelligence”, “Machine Learning”, “Deep Learning”, “Neural Network”, “Computer Aided Diagnosis”, “Low Back Pain”, “Lumbar”, “Intervertebral Disc Degeneration”, “Spine Surgery”, etc. The search returned a total of 1536 articles. After duplication removal and evaluation of the abstracts, 1386 were excluded, whereas 93 papers were excluded after full-text examination, taking the number of eligible articles to 57. The main applications of CAD in LBP included classification and regression. Classification is used to identify or categorize a disease, whereas regression is used to produce a numerical output as a quantitative evaluation of some measure. The best performing systems were developed to diagnose degenerative changes of the spine from imaging data, with average accuracy rates >80%. However, notable outcomes were also reported for CAD tools executing different tasks including analysis of clinical, biomechanical, electrophysiological, and functional imaging data. Further studies are needed to better define the role of CAD in LBP care.

MRI-- and CT--based metrics for the quantification of arthroscopic bone resections in femoroacetabular impingement syndrome

The purpose of this in vitro study was to quantify the bone resected from the proximal femur during hip arthroscopy using metrics generated from magnetic resonance imaging (MRI) and computed tomography (CT) reconstructed three-dimensional (3D) bone models. Seven cadaveric hemi-pelvises underwent both a 1.5 T MRI and CT scan before and following an arthroscopic proximal femoral osteochondroplasty. The images from MRI and CT were segmented to generate 3D proximal femoral surface models. A validated 3D--3D registration method was used to compare surface--to--surface distances between the 3D models before and following surgery. The new metrics of maximum height, mean height, surface area and volume, were computed to quantify bone resected during osteochondroplasty. Stability of the metrics across imaging modalities was established through paired sample t--tests and bivariate correlation. Bivariate correlation analyses indicated strong correlations between all metrics (r = 0.728--0.878) computed from MRI and CT derived models. There were no differences in the MRI- and CT-based metrics used to quantify bone resected during femoral osteochondroplasty. Preoperative- and postoperative MRI and CT derived 3D bone models can be used to quantify bone resected during femoral osteochondroplasty, without significant differences between the imaging modalities.

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.

The Modic-endplate-complex phenotype in cervical spine patients: Association with symptoms and outcomes

This study describes a novel, combined Modic changes (MC) and structural endplate abnormality phenotype of the cervical spine, which we have termed the Modic-Endplate-Complex (MEC), and its association with preoperative symptoms and outcomes in anterior cervical discectomy and fusion (ACDF) patients. This was a retrospective study of prospectively collected data at a single institution. Preoperative cervical magnetic resonance imagings were used to assess the presence of MC and endplate abnormalities. Patients were divided into four groups: MC-only, endplate abnormality-only, the MEC and controls. The MEC was defined as the presence of both a MC and endplate abnormality in the cervical spine. Phenotypes were further stratified by location and compared to controls. Associations with patient-reported outcome measures were assessed using regression controlling for baseline characteristics. A total of 628 patients were included, with 84 MC-only, 166 endplate abnormality-only, and 187 MEC patients. Both MC (p < 0.001) and endplate abnormalities (p < 0.001) were independently associated with one another. MC at the adjacent level (p = 0.018), endplate abnormalities (regardless of location) (p = 0.001), and the MEC within the fusion segment (p = 0.027) were all associated with higher Neck Disability Index scores. Both MC within the fusion segment (p = 0.008) and endplate abnormalities within the fusion segment (p = 0.017) associated with lower Veteran's Rand 12-item scores. MC and structural endplate abnormalities commonly manifest concomitantly in patients indicated for ACDF for degenerative pathology. Patients with the endplate pathology, including the MEC phenotype, reported significantly higher levels of postoperative disability following ACDF. These findings add valuable data to the prognostic assessment of degenerative cervical spine patients.

Robotic Replica of a Human Spine Uses Soft Magnetic Sensor Array to Forecast Intervertebral Loads and Posture after Surgery

Cervical disc implants are conventional surgical treatments for patients with degenerative disc disease, such as cervical myelopathy and radiculopathy. However, the surgeon still must determine the candidacy of cervical disc implants mainly from the findings of diagnostic imaging studies, which can sometimes lead to complications and implant failure. To help address these problems, a new approach was developed to enable surgeons to preview the post-operative effects of an artificial disc implant in a patient-specific fashion prior to surgery. To that end, a robotic replica of a person's spine was 3D printed, modified to include an artificial disc implant, and outfitted with a soft magnetic sensor array. The aims of this study are threefold: first, to evaluate the potential of a soft magnetic sensor array to detect the location and amplitude of applied loads; second, to use the soft magnetic sensor array in a 3D printed human spine replica to distinguish between five different robotically actuated postures; and third, to compare the efficacy of four different machine learning algorithms to classify the loads, amplitudes, and postures obtained from the first and second aims. Benchtop experiments showed that the soft magnetic sensor array was capable of precisely detecting the location and amplitude of forces, which were successfully classified by four different machine learning algorithms that were compared for their capabilities: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), and Artificial Neural Network (ANN). In particular, the RF and ANN algorithms were able to classify locations of loads applied 3.25 mm apart with 98.39% ± 1.50% and 98.05% ± 1.56% accuracies, respectively. Furthermore, the ANN had an accuracy of 94.46% ± 2.84% to classify the location that a 10 g load was applied. The artificial disc-implanted spine replica was subjected to flexion and extension by a robotic arm. Five different postures of the spine were successfully classified with 100% ± 0.0% accuracy with the ANN using the soft magnetic sensor array. All results indicated that the magnetic sensor array has promising potential to generate data prior to invasive surgeries that could be utilized to preoperatively assess the suitability of a particular intervention for specific patients and to potentially assist the postoperative care of people with cervical disc implants.

Special Convolutional Neural Network for Identification and Positioning of Interstitial Lung Disease Patterns in Computed Tomography Images

In this paper, automated detection of interstitial lung disease patterns in high resolution computed tomography images is achieved by developing a faster region-based convolutional network based detector with GoogLeNet as a backbone. GoogLeNet is simplified by removing few inception models and used as the backbone of the detector network. The proposed framework is developed to detect several interstitial lung disease patterns without doing lung field segmentation. The proposed method is able to detect the five most prevalent interstitial lung disease patterns: fibrosis, emphysema, consolidation, micronodules and ground-glass opacity, as well as normal. Five-fold cross-validation has been used to avoid bias and reduce over-fitting. The proposed framework performance is measured in terms of F-score on the publicly available MedGIFT database. It outperforms state-of-the-art techniques. The detection is performed at slice level and could be used for screening and differential diagnosis of interstitial lung disease patterns using high resolution computed tomography images.

Combined Artificial Intelligence Approaches Analyzing 1000 Conservative Patients with Back Pain-A Methodological Pathway to Predicting Treatment Efficacy and Diagnostic Groups

Patients with back pain are common and present a challenge in everyday medical practice due to the multitude of possible causes and the individual effects of treatments. Predicting causes and therapy efficien cy with the help of artificial intelligence could improve and simplify the treatment. In an exemplary collective of 1000 conservatively treated back pain patients, it was investigated whether the prediction of therapy efficiency and the underlying diagnosis is possible by combining different artificial intelligence approaches. For this purpose, supervised and unsupervised artificial intelligence methods were analyzed and a methodology for combining the predictions was developed. Supervised AI is suitable for predicting therapy efficiency at the borderline of minimal clinical difference. Non-supervised AI can show patterns in the dataset. We can show that the identification of the underlying diagnostic groups only becomes possible through a combination of different AI approaches and the baseline data. The presented methodology for the combined application of artificial intelligence algorithms shows a transferable path to establish correlations in heterogeneous data sets when individual AI approaches only provide weak results.