Explore the vast range of titles available.

Healthcare systems worldwide generate vast amounts of data from many different sources. Although of high complexity for a human being, it is essential to determine the patterns and minor variations in the genomic, radiological, laboratory, or clinical data that reliably differentiate phenotypes or allow high predictive accuracy in health-related tasks. Convolutional neural networks (CNN) are increasingly applied to image data for various tasks. Its use for non-imaging data becomes feasible through different modern machine learning techniques, converting non-imaging data into images before inputting them into the CNN model. Considering also that healthcare providers do not solely use one data modality for their decisions, this approach opens the door for multi-input/mixed data models which use a combination of patient information, such as genomic, radiological, and clinical data, to train a hybrid deep learning model. Thus, this reflects the main characteristic of artificial intelligence: simulating natural human behavior. The present review focuses on key advances in machine and deep learning, allowing for multi-perspective pattern recognition across the entire information set of patients in spine surgery. This is the first review of artificial intelligence focusing on hybrid models for deep learning applications in spine surgery, to the best of our knowledge. This is especially interesting as future tools are unlikely to use solely one data modality. The techniques discussed could become important in establishing a new approach to decision-making in spine surgery based on three fundamental pillars: (1) patient-specific, (2) artificial intelligence-driven, (3) integrating multimodal data. The findings reveal promising research that already took place to develop multi-input mixed-data hybrid decision-supporting models. Their implementation in spine surgery may hence be only a matter of time.

Evidence has arisen in recent years suggesting that a tissue renin-angiotensin system (tRAS) is involved in the progression of various human diseases. This system contains two regulatory pathways: a pathological pro-inflammatory pathway containing the Angiotensin Converting Enzyme (ACE)/Angiotensin II (AngII)/Angiotensin II receptor type 1 (AGTR1) axis and a protective anti-inflammatory pathway involving the Angiotensin II receptor type 2 (AGTR2)/ACE2/Ang1–7/MasReceptor axis. Numerous studies reported the positive effects of pathologic tRAS pathway inhibition and protective tRAS pathway stimulation on the treatment of cardiovascular, inflammatory, and autoimmune disease and the progression of neuropathic pain. Cell senescence and aging are known to be related to RAS pathways. Further, this system directly interacts with SARS-CoV 2 and seems to be an important target of interest in the COVID-19 pandemic. This review focuses on the involvement of tRAS in the progression of the mentioned diseases from an interdisciplinary clinical perspective and highlights therapeutic strategies that might be of major clinical importance in the future.

In this study, we aimed to develop a novel prognostic algorithm for oral squamous cell carcinoma (OSCC) using a combination of pathogenomics and AI-based techniques. We collected comprehensive clinical, genomic, and pathology data from a cohort of OSCC patients in the TCGA dataset and used machine learning and deep learning algorithms to identify relevant features that are predictive of survival outcomes. Our analyses included 406 OSCC patients. Initial analyses involved gene expression analyses, principal component analyses, gene enrichment analyses, and feature importance analyses. These insights were foundational for subsequent model development. Furthermore, we applied five machine learning/deep learning algorithms (Random Survival Forest, Gradient Boosting Survival Analysis, Cox PH, Fast Survival SVM, and DeepSurv) for survival prediction. Our initial analyses revealed relevant gene expression variations and biological pathways, laying the groundwork for robust feature selection in model building. The results showed that the multimodal model outperformed the unimodal models across all methods, with c-index values of 0.722 for RSF, 0.633 for GBSA, 0.625 for FastSVM, 0.633 for CoxPH, and 0.515 for DeepSurv. When considering only important features, the multimodal model continued to outperform the unimodal models, with c-index values of 0.834 for RSF, 0.747 for GBSA, 0.718 for FastSVM, 0.742 for CoxPH, and 0.635 for DeepSurv. Our results demonstrate the potential of pathogenomics and AI-based techniques in improving the accuracy of prognostic prediction in OSCC, which may ultimately aid in the development of personalized treatment strategies for patients with this devastating disease.

Background Low back pain is a widely prevalent symptom and the foremost cause of disability on a global scale. Although various degenerative imaging findings observed on magnetic resonance imaging (MRI) have been linked to low back pain and disc herniation, none of them can be considered pathognomonic for this condition, given the high prevalence of abnormal findings in asymptomatic individuals. Nevertheless, there is a lack of knowledge regarding whether radiomics features in MRI images combined with clinical features can be useful for prediction modeling of treatment success. The objective of this study was to explore the potential of radiomics feature analysis combined with clinical features and artificial intelligence-based techniques (machine learning/deep learning) in identifying MRI predictors for the prediction of outcomes after lumbar disc herniation surgery. Methods We included n = 172 patients who underwent discectomy due to disc herniation with preoperative T2-weighted MRI examinations. Extracted clinical features included sex, age, alcohol and nicotine consumption, insurance type, hospital length of stay (LOS), complications, operation time, ASA score, preoperative CRP, surgical technique (microsurgical versus full-endoscopic), and information regarding the experience of the performing surgeon (years of experience with the surgical technique and the number of surgeries performed at the time of surgery). The present study employed a semiautomatic region-growing volumetric segmentation algorithm to segment herniated discs. In addition, 3D-radiomics features, which characterize phenotypic differences based on intensity, shape, and texture, were extracted from the computed magnetic resonance imaging (MRI) images. Selected features identified by feature importance analyses were utilized for both machine learning and deep learning models (n = 17 models). Results The mean accuracy over all models for training and testing in the combined feature set was 93.31 ± 4.96 and 88.17 ± 2.58. The mean accuracy for training and testing in the clinical feature set was 91.28 ± 4.56 and 87.69 ± 3.62. Conclusions Our results suggest a minimal but detectable improvement in predictive tasks when radiomics features are included. However, the extent of this advantage should be considered with caution, emphasizing the potential of exploring multimodal data inputs in future predictive modeling.

Abstract BACKGROUND Minimally invasive transforaminal lumbar interbody fusion (MIS-TLIF) is an increasingly popular procedure with several potential advantages over traditional open TLIF. OBJECTIVE The current study aimed to compare fusion rates of different graft materials used in MIS-TLIF, via meta-analysis of the published literature. METHODS A Medline search was performed and a database was created including patient's type of graft, clinical outcome, fusion rate, fusion assessment modality, and duration of follow-up. Meta-analysis of the fusion rate was performed using StatsDirect software (StatsDirect Ltd, Cheshire, United Kingdom). RESULTS A total of 1533 patients from 40 series were included. Fusion rates were high, ranging from 91.8% to 99%. The imaging modalities used to assess fusion were computed tomography scans (30%) and X-rays (70%). Comparison of all recombinant human bone morphogenetic protein (rhBMP) series with all non-rhBMP series showed fusion rates of 96.6% and 92.5%, respectively. The lowest fusion rate was seen with isolated use of autologous local bone (91.8%). The highest fusion rate was observed with combination of autologous local bone with bone extender and rhBMP (99.1%). The highest fusion rate without the use of BMP was seen with autologous local bone + bone extender (93.1%). The reported complication rate ranged from 0% to 35.71%. Clinical improvement was observed in all studies. CONCLUSION Fusion rates are generally high with MIS-TLIF regardless of the graft material used. Given the potential complications of iliac bone harvesting and rhBMP, use of other bone graft options for MIS-TLIF is reasonable. The highest fusion rate without the use of rhBMP was seen with autologous local bone plus bone extender (93.1%).

The success rate of dental implants depends on primary and secondary stability. We investigate predictive factors for future risk stratification models. We retrospectively analyze 272 patients with a total of 582 implants. Implant stability is measured with resonance frequency analysis and evaluated based on the implant stability quotient (ISQ). A linear regression model with regression coefficients (reg. coeff.) and its 95% confidence interval (95% CI) is applied to assess predictive factors for implant stability. Implant diameter (reg. coeff.: 3.28; 95% CI: 1.89–4.66, p < 0.001), implant length (reg. coeff.: 0.67, 95% CI: 0.26–1.08, p < 0.001), and implant localization (maxillary vs. mandibular, reg. coeff.: −7.45, 95% CI: −8.70–(−6.20), p < 0.001) are significant prognostic factors for primary implant stability. An increase in ISQ between insertion and exposure is significantly correlated with healing time (reg. coeff.: 0.11, 95% CI: 0.04–0.19). Patients with maxillary implants have lower ISQ at insertion but show a higher increase in ISQ after insertion than patients with mandibular implants. We observe positive associations between primary implant stability and implant diameter, implant length, and localization (mandibular vs. maxillary). An increase in implant stability between insertion and exposure is significantly correlated with healing time and is higher for maxillary implants. These predictive factors should be further evaluated in prospective cohort studies to develop future preoperative risk-stratification models.

Background In vitro studies using nucleus pulposus (NP) cells are commonly used to investigate disc cell biology and pathogenesis, or to aid in the development of new therapies. However, lab‐to‐lab variability jeopardizes the much‐needed progress in the field. Here, an international group of spine scientists collaborated to standardize extraction and expansion techniques for NP cells to reduce variability, improve comparability between labs and improve utilization of funding and resources. Methods The most commonly applied methods for NP cell extraction, expansion, and re‐differentiation were identified using a questionnaire to research groups worldwide. NP cell extraction methods from rat, rabbit, pig, dog, cow, and human NP tissue were experimentally assessed. Expansion and re‐differentiation media and techniques were also investigated. Results Recommended protocols are provided for extraction, expansion, and re‐differentiation of NP cells from common species utilized for NP cell culture. Conclusions This international, multilab and multispecies study identified cell extraction methods for greater cell yield and fewer gene expression changes by applying species‐specific pronase usage, 60–100 U/ml collagenase for shorter durations. Recommendations for NP cell expansion, passage number, and many factors driving successful cell culture in different species are also addressed to support harmonization, rigor, and cross‐lab comparisons on NP cells worldwide. An international group of spine scientists collaborated to standardize extraction and expansion techniques for nucleus pulposus cells to reduce variability, improve comparability between labs, and improve utilization of funding and resources.

Purpose Symptomatic horizontal instability is clinically relevant following acute acromioclavicular joint dislocations. However, the intrinsic healing response is poorly understood. The present study sought to investigate time-dependent healing responses of the human acromioclavicular ligament following acute traumatic rupture. Methods Biopsies of the acromioclavicular ligament were obtained from patients undergoing surgical treatment for acute acromioclavicular joint dislocations. Specimens were stratified by time between trauma and surgery: group 1, 0–7 days ( n  = 5); group 2, 8–14 days ( n  = 6); and group 3, 15–21 days ( n  = 4). Time-dependent changes in cellularity, collagen (type 1 and 3) concentration, and histomorphological appearance were evaluated for the rupture and intact zone of the acromioclavicular ligament. Results Group 1 was characterized by cellular activation and early inflammatory response. The rupture zone exhibited a significantly higher count of CD68-positive cells than the intact zone (15.2 vs 7.4; P  ≤ 0.05). Consistently, synovialization of the rupture end was observed. Within the second week, the rupture zone was subject to proliferation showing more fibroblast-like cells than the intact zone (66.8 vs 43.8; P  ≤ 0.05) and a peak of collagen type 3 expression (group 1: 2.2 ± 0.38, group 2: 3.2 ± 0.18, group 3: 2.8 ± 0.57; P ≤ 0.05). Signs of consolidation and early remodeling were seen in the third week. Conclusions The acromioclavicular ligament exhibits early and dynamic healing responses following acute traumatic rupture. Our histological findings suggest that surgical treatment of acute ACJ dislocations should be performed as early as possible within a timeframe of 1 week after trauma to exploit the utmost biological healing potential. Prospective clinical studies are warranted to investigate whether early surgical treatment of ACJ dislocations translates into clinical benefits.

Background Lumbar disc herniation is one of the leading causes of chronic low back pain. Surgery remains the therapy of choice when conservative approaches fail. Full-endoscopic approaches represent a promising alternative to the well-established microsurgical technique. However, high-grade evidence comparing these techniques is still scarce. Methods Patients presenting with lumbar disc herniation will be included. The intervention group will obtain full-endoscopic disc decompression, whereas the control group will be treated by microsurgical disc decompression. We will apply a comprehensive cohort study design involving a randomized and a prospective non-randomized study arm. Patients who do not consent to be randomized will be assigned to the non-randomized arm. The primary outcome will be the Oswestry Disability Index (ODI). Secondary outcomes involve the visual analog scale (VAS) of pain and the SF-36 health questionnaire. Furthermore, clinical characteristics including duration of hospital stay, operation time, and complications as well as laboratory markers, such as C-reactive protein, white blood cell counts, and interleukin 6 will be determined and compared. Discussion This study will significantly contribute to the current evidence available in the literature by evaluating the outcome of the full-endoscopic technique against the gold standard for lumbar disc herniation in a clinically relevant study setup. Additionally, the study design allows us to include patients not willing to be randomized in a prospective parallel study arm and to evaluate the impact of randomization on outcomes and include. The results could help to improve the future therapy in patients suffering from lumbar disc herniation. Trial registration This study was prospectively registered in The German Clinical Trials Register (DRKS), a German WHO primary registry, under the registration number: DRKS00025786. Registered on July 7, 2021.

Introduction: Minimal-invasive instrumentation techniques have become a workhorse in spine surgery and require constant clinical evaluations. We sought to analyze patient-reported outcome measures (PROMs) and clinicopathological characteristics of thoracolumbar fracture stabilizations utilizing a minimal-invasive percutaneous dorsal screw-rod system. Methods: We included all patients with thoracolumbar spine fractures who underwent minimal-invasive percutaneous spine stabilization in our clinics since inception and who have at least 1 year of follow-up data. Clinical characteristics (length of hospital stay (LOS), operation time (OT), and complications), PROMs (preoperative (pre-op), 3-weeks postoperative (post-op), 1-year postoperative: eq5D, COMI, ODI, NRS back pain), and laboratory markers (leucocytes, c-reactive protein (CRP)) were analyzed, finding significant associations between these study variables and PROMs. Results: A total of 68 patients (m: 45.6%; f: 54.4%; mean age: 76.9 ± 13.9) were included. The most common fracture types according to the AO classification were A3 (40.3%) and A4 (40.3%), followed by B2 (7.46%) and B1 (5.97%). The Median American Society of Anesthesiologists (ASA) score was 3 (range: 1–4). Stabilized levels ranged from TH4 to L5 (mean number of targeted levels: 4.25 ± 1.4), with TH10-L2 (12/68) and TH11-L3 (11/68) being the most frequent site of surgery. Mean OT and LOS were 92.2 ± 28.2 min and 14.3 ± 6.9 days, respectively. We observed 9/68 complications (13.2%), mostly involving screw misalignments and loosening. CRP increased from 24.9 ± 33.3 pre-op to 34.8 ± 29.9 post-op (p < 0.001), whereas leucocyte counts remained stable. All PROMs showed a marked significant improvement for both 3-week and 1-year evaluations compared to the preoperative situation. Interestingly, we did not find an impact of OT, LOS, lab markers, complications, and other clinical characteristics on PROMs. Notably, a higher number of stabilized levels did not affect PROMs. Conclusions: Minimal-invasive stabilization of thoracolumbar fractures utilizing a dorsal percutaneous approach resulted in significant PROM outcome improvements, although we observed a complication rate of 13.2% for up to 1 year of follow-up. PROMs were not significantly associated with clinicopathological characteristics, technique-related variables, or the number of targeted levels.