Multimodal artificial intelligence-based pathogenomics improves survival prediction in oral squamous cell carcinoma.

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.

Health Economic Consequences Associated With COVID-19-Related Delay in Melanoma Diagnosis in Europe.

Importance: The COVID-19 pandemic resulted in delayed access to medical care. Restrictions to health care specialists, staff shortages, and fear of SARS-CoV-2 infection led to interruptions in routine care, such as early melanoma detection; however, premature mortality and economic burden associated with this postponement have not been studied yet. Objective: To determine the premature mortality and economic costs associated with suspended melanoma screenings during COVID-19 pandemic lockdowns by estimating the total burden of delayed melanoma diagnoses for Europe. Design, Setting, and Participants: This multicenter economic evaluation used population-based data from patients aged at least 18 years with invasive primary cutaneous melanomas stages I to IV according to the American Joint Committee on Cancer (AJCC) seventh and eighth editions, including melanomas of unknown primary (T0). Data were collected from January 2017 to December 2021 in Switzerland and from January 2019 to December 2021 in Hungary. Data were used to develop an estimation of melanoma upstaging rates in AJCC stages, which was verified with peripandemic data. Years of life lost (YLL) were calculated and were, together with cost data, used for financial estimations. The total financial burden was assessed through direct and indirect treatment costs. Models were building using data from 50 072 patients aged 18 years and older with invasive primary cutaneous melanomas stages I to IV according to the AJCC seventh and eighth edition, including melanomas of unknown primary (T0) from 2 European tertiary centers. Data from European cancer registries included patient-based direct and indirect cost data, country-level economic indicators, melanoma incidence, and population rates per country. Data were analyzed from July 2021 to September 2022. Exposure: COVID-19 lockdown-related delay of melanoma detection and consecutive public health and economic burden. As lockdown restrictions varied by country, lockdown scenario was defined as elimination of routine medical examinations and severely restricted access to follow-up examinations for at least 4 weeks. Main Outcomes and Measures: Primary outcomes were the total burden of a delay in melanoma diagnosis during COVID-19 lockdown periods, measured using the direct (in US$) and indirect (calculated as YLL plus years lost due to disability [YLD] and disability-adjusted life-years [DALYs]) costs for Europe. Secondary outcomes included estimation of upstaging rate, estimated YLD, YLL, and DALY for each European country, absolute direct and indirect treatment costs per European country, proportion of the relative direct and indirect treatment costs for the countries, and European health expenditure. Results: There were an estimated 111 464 (range, 52 454-295 051) YLL due to pandemic-associated delay in melanoma diagnosis in Europe, and estimated total additional costs were $7.65 (range, $3.60 to $20.25) billion. Indirect treatment costs were the main cost driver, accounting for 94.5% of total costs. Estimates for YLD in Europe resulted in 15 360 years for the 17% upstaging model, ranging from 7228 years (8% upstaging model) to 40 660 years (45% upstaging model). Together, YLL and YLD constitute the overall disease burden, ranging from 59 682 DALYs (8% upstaging model) to 335 711 DALYs (45% upstaging model), with 126 824 DALYs for the real-world 17% scenario. Conclusions and Relevance: This economic analysis emphasizes the importance of continuing secondary skin cancer prevention measures during pandemics. Beyond the personal outcomes of a delayed melanoma diagnosis, the additional economic and public health consequences are underscored, emphasizing the need to include indirect economic costs in future decision-making processes. These estimates on DALYs and the associated financial losses complement previous studies highlighting the cost-effectiveness of screening for melanoma.

Possible Explanations for Rising Melanoma Rates Despite Increased Sunscreen Use over the Past Several Decades.

The incidence of cutaneous melanoma continues to rise despite the increased use of sunscreens within the last several decades. Some research even suggests that the use of sunscreen is associated with increased rates of melanoma. Given the aggressive, and often deadly, nature of cutaneous melanoma, the aim of this communication is to better elucidate the relationship between sunscreen use and melanoma development and if there are other preventative measures to be aware of. A search was performed to identify the studies that have investigated melanoma development in individuals who used sunscreen and those who did not. Study limitations and possible confounding variables were identified, which guided a subsequent search to determine what data were available to support that these limitations and confounding variables may explain the perplexing association between sunscreen use and melanoma development. Five hypotheses were generated, which were related to increased awareness and reporting, the relationship between sunscreen use and the duration of sun exposure, the importance of broad-spectrum protection, and the effect of sunscreen on reactive oxygen species formation. The main conclusion is that more recent studies that control for confounding variables are required to determine the true effect of adequate broad-spectrum sunscreen use today on the development of melanoma.

Synthetic 3D Spinal Vertebrae Reconstruction from Biplanar X-rays Utilizing Generative Adversarial Networks.

Computed tomography (CT) offers detailed insights into the internal anatomy of patients, particularly for spinal vertebrae examination. However, CT scans are associated with higher radiation exposure and cost compared to conventional X-ray imaging. In this study, we applied a Generative Adversarial Network (GAN) framework to reconstruct 3D spinal vertebrae structures from synthetic biplanar X-ray images, specifically focusing on anterior and lateral views. The synthetic X-ray images were generated using the DRRGenerator module in 3D Slicer by incorporating segmentations of spinal vertebrae in CT scans for the region of interest. This approach leverages a novel feature fusion technique based on X2CT-GAN to combine information from both views and employs a combination of mean squared error (MSE) loss and adversarial loss to train the generator, resulting in high-quality synthetic 3D spinal vertebrae CTs. A total of n = 440 CT data were processed. We evaluated the performance of our model using multiple metrics, including mean absolute error (MAE) (for each slice of the 3D volume (MAE0) and for the entire 3D volume (MAE)), cosine similarity, peak signal-to-noise ratio (PSNR), 3D peak signal-to-noise ratio (PSNR-3D), and structural similarity index (SSIM). The average PSNR was 28.394 dB, PSNR-3D was 27.432, SSIM was 0.468, cosine similarity was 0.484, MAE0 was 0.034, and MAE was 85.359. The results demonstrated the effectiveness of this approach in reconstructing 3D spinal vertebrae structures from biplanar X-rays, although some limitations in accurately capturing the fine bone structures and maintaining the precise morphology of the vertebrae were present. This technique has the potential to enhance the diagnostic capabilities of low-cost X-ray machines while reducing radiation exposure and cost associated with CT scans, paving the way for future applications in spinal imaging and diagnosis.

Artificial intelligence-based analysis of associations between learning curve and clinical outcomes in endoscopic and microsurgical lumbar decompression surgery.

PURPOSE: A common spine surgery procedure involves decompression of the lumbar spine. The impact of the surgeon's learning curve on relevant clinical outcomes is currently not well examined in the literature. A variety of machine learning algorithms have been investigated in this study to determine how a surgeon's learning curve and other clinical parameters will influence prolonged lengths of stay (LOS), extended operating times (OT), and complications, as well as whether these clinical parameters can be reliably predicted. METHODS: A retrospective monocentric cohort study of patients with lumbar spinal stenosis treated with microsurgical (MSD) and full-endoscopic (FED) decompression was conducted. The study included 206 patients with lumbar spinal stenosis who underwent FED (63; 30.6%) and MSD (118; 57.3%). Prolonged LOS and OT were defined as those exceeding the 75th percentile of the cohort. Furthermore, complications were assessed as a dependent variable. Using unsupervised learning, clusters were identified in the data, which helped distinguish between the early learning curve (ELC) and the late learning curve (LLC). From 15 algorithms, the top five algorithms that best fit the data were selected for each prediction task. We calculated the accuracy of prediction (Acc) and the area under the curve (AUC). The most significant predictors were determined using a feature importance analysis. RESULTS: For the FED group, the median number of surgeries with case surgery type at the time of surgery was 72 in the ELC group and 274 in the LLC group. FED patients did not significantly differ in outcome variables (LOS, OT, complication rate) between the ELC and LLC group. The random forest model demonstrated the highest mean accuracy and AUC across all folds for each classification task. For OT, it achieved an accuracy of 76.08% and an AUC of 0.89. For LOS, the model reached an accuracy of 83.83% and an AUC of 0.91. Lastly, in predicting complications, the random forest model attained the highest accuracy of 89.90% and an AUC of 0.94. Feature importance analysis indicated that LOS, OT, and complications were more significantly affected by patient characteristics than the surgical technique (FED versus MSD) or the surgeon's learning curve. CONCLUSIONS: A median of 72 cases of FED surgeries led to comparable clinical outcomes in the early learning curve phase compared to experienced surgeons. These outcomes seem to be more significantly affected by patient characteristics than the learning curve or the surgical technique. Several study variables, including the learning curve, can be used to predict whether lumbar decompression surgery will result in an increased LOS, OT, or complications. To introduce the provided prediction tools into clinics, the algorithms need to be implemented into open-source software and externally validated through large-scale randomized controlled trials.

Capillary leak and endothelial permeability in critically ill patients: a current overview.

Capillary leak syndrome (CLS) represents a phenotype of increased fluid extravasation, resulting in intravascular hypovolemia, extravascular edema formation and ultimately hypoperfusion. While endothelial permeability is an evolutionary preserved physiological process needed to sustain life, excessive fluid leak-often caused by systemic inflammation-can have detrimental effects on patients' outcomes. This article delves into the current understanding of CLS pathophysiology, diagnosis and potential treatments. Systemic inflammation leading to a compromise of endothelial cell interactions through various signaling cues (e.g., the angiopoietin-Tie2 pathway), and shedding of the glycocalyx collectively contribute to the manifestation of CLS. Capillary permeability subsequently leads to the seepage of protein-rich fluid into the interstitial space. Recent insights into the importance of the sub-glycocalyx space and preserving lymphatic flow are highlighted for an in-depth understanding. While no established diagnostic criteria exist and CLS is frequently diagnosed by clinical characteristics only, we highlight more objective serological and (non)-invasive measurements that hint towards a CLS phenotype. While currently available treatment options are limited, we further review understanding of fluid resuscitation and experimental approaches to target endothelial permeability. Despite the improved understanding of CLS pathophysiology, efforts are needed to develop uniform diagnostic criteria, associate clinical consequences to these criteria, and delineate treatment options.

Performance of artificial intelligence-based algorithms to predict prolonged length of stay after head and neck cancer surgery.

Background: Medical resource management can be improved by assessing the likelihood of prolonged length of stay (LOS) for head and neck cancer surgery patients. The objective of this study was to develop predictive models that could be used to determine whether a patient's LOS after cancer surgery falls within the normal range of the cohort. Methods: We conducted a retrospective analysis of a dataset consisting of 300 consecutive patients who underwent head and neck cancer surgery between 2017 and 2022 at a single university medical center. Prolonged LOS was defined as LOS exceeding the 75th percentile of the cohort. Feature importance analysis was performed to evaluate the most important predictors for prolonged LOS. We then constructed 7 machine learning and deep learning algorithms for the prediction modeling of prolonged LOS. Results: The algorithms reached accuracy values of 75.40 (radial basis function neural network) to 97.92 (Random Trees) for the training set and 64.90 (multilayer perceptron neural network) to 84.14 (Random Trees) for the testing set. The leading parameters predicting prolonged LOS were operation time, ischemia time, the graft used, the ASA score, the intensive care stay, and the pathological stages. The results revealed that patients who had a higher number of harvested lymph nodes (LN) had a lower probability of recurrence but also a greater LOS. However, patients with prolonged LOS were also at greater risk of recurrence, particularly when fewer (LN) were extracted. Further, LOS was more strongly correlated with the overall number of extracted lymph nodes than with the number of positive lymph nodes or the ratio of positive to overall extracted lymph nodes, indicating that particularly unnecessary lymph node extraction might be associated with prolonged LOS. Conclusions: The results emphasize the need for a closer follow-up of patients who experience prolonged LOS. Prospective trials are warranted to validate the present results.

Finetuning of GLIDE stable diffusion model for AI-based text-conditional image synthesis of dermoscopic images.

Background: The development of artificial intelligence (AI)-based algorithms and advances in medical domains rely on large datasets. A recent advancement in text-to-image generative AI is GLIDE (Guided Language to Image Diffusion for Generation and Editing). There are a number of representations available in the GLIDE model, but it has not been refined for medical applications. Methods: For text-conditional image synthesis with classifier-free guidance, we have fine-tuned GLIDE using 10,015 dermoscopic images of seven diagnostic entities, including melanoma and melanocytic nevi. Photorealistic synthetic samples of each diagnostic entity were created by the algorithm. Following this, an experienced dermatologist reviewed 140 images (20 of each entity), with 10 samples originating from artificial intelligence and 10 from original images from the dataset. The dermatologist classified the provided images according to the seven diagnostic entities. Additionally, the dermatologist was asked to indicate whether or not a particular image was created by AI. Further, we trained a deep learning model to compare the diagnostic results of dermatologist versus machine for entity classification. Results: The results indicate that the generated images possess varying degrees of quality and realism, with melanocytic nevi and melanoma having higher similarity to real images than other classes. The integration of synthetic images improved the classification performance of the model, resulting in higher accuracy and precision. The AI assessment showed superior classification performance compared to dermatologist. Conclusion: Overall, the results highlight the potential of synthetic images for training and improving AI models in dermatology to overcome data scarcity.

Clinical and radiomics feature-based outcome analysis in lumbar disc herniation surgery.

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.

Accuracy of Computerized Optical Impression Making in Fabrication of Removable Dentures for Partially Edentulous Jaws: An In Vivo Feasibility Study.

The use of computerized optical impression making (COIM) for the fabrication of removable dentures for partially edentulous jaws is a rising trend in dental prosthetics. However, the accuracy of this method compared with that of traditional impression-making techniques remains uncertain. We therefore decided to evaluate the accuracy of COIM in the context of partially edentulous jaws in an in vivo setting. Twelve partially edentulous patients with different Kennedy classes underwent both a conventional impression (CI) and a computerized optical impression (COI) procedure. The CI was then digitized and compared with the COI data using 3D analysis software. Four different comparison situations were assessed: Whole Jaw (WJ), Mucosa with Residual Teeth (M_RT), Isolated Mucosa (IM), and Isolated Abutment Teeth (AT). Statistical analyses were conducted to evaluate group differences by quantifying the deviation values between the CIs and COIs. The mean deviations between the COIs and CIs varied significantly across the different comparison situations, with mucosal areas showing higher deviations than dental hard tissue. However, no statistically significant difference was found between the maxilla and mandible. Although COIM offers a no-pressure impression method that captures surfaces without irritation, it was found to capture mucosa less accurately than dental hard tissue. This discrepancy can likely be attributed to software algorithms that automatically filter out mobile tissues. Clinically, these findings suggest that caution is required when using COIM for prosthetics involving mucosal tissues as deviations could compromise the fit and longevity of the prosthetic appliance. Further research is warranted to assess the clinical relevance of these deviations.

Automated Detection and Measurement of Dural Sack Cross-Sectional Area in Lumbar Spine MRI Using Deep Learning.

Lumbar spine magnetic resonance imaging (MRI) is a critical diagnostic tool for the assessment of various spinal pathologies, including degenerative disc disease, spinal stenosis, and spondylolisthesis. The accurate identification and quantification of the dural sack cross-sectional area are essential for the evaluation of these conditions. Current manual measurement methods are time-consuming and prone to inter-observer variability. Our study developed and validated deep learning models, specifically U-Net, Attention U-Net, and MultiResUNet, for the automated detection and measurement of the dural sack area in lumbar spine MRI, using a dataset of 515 patients with symptomatic back pain and externally validating the results based on 50 patient scans. The U-Net model achieved an accuracy of 0.9990 and 0.9987 on the initial and external validation datasets, respectively. The Attention U-Net model reported an accuracy of 0.9992 and 0.9989, while the MultiResUNet model displayed a remarkable accuracy of 0.9996 and 0.9995, respectively. All models showed promising precision, recall, and F1-score metrics, along with reduced mean absolute errors compared to the ground truth manual method. In conclusion, our study demonstrates the potential of these deep learning models for the automated detection and measurement of the dural sack cross-sectional area in lumbar spine MRI. The proposed models achieve high-performance metrics in both the initial and external validation datasets, indicating their potential utility as valuable clinical tools for the evaluation of lumbar spine pathologies. Future studies with larger sample sizes and multicenter data are warranted to validate the generalizability of the model further and to explore the potential integration of this approach into routine clinical practice.

Angiopoietin-2 is associated with capillary leak and predicts complications after cardiac surgery.

BACKGROUND: Patients undergoing cardiac surgery are prone to numerous complications. Increased vascular permeability may be associated with morbidity and mortality due to hemodynamic instability, fluid overload, and edema formation. We hypothesized that markers of endothelial injury and inflammation are associated with capillary leak, ultimately increasing the risk of postoperative complications. METHODS: In this prospective, observational, multidisciplinary cohort study at our tertiary academic medical center, we recruited 405 cardiac surgery patients. Patients were assessed daily using body impedance electrical analysis, ultrasound, sublingual intravital microscopy, and analysis of serum biomarkers. Multivariable models, as well as machine learning, were used to study the association of angiopoietin-2 with extracellular water as well as common complications after cardiac surgery. RESULTS: The majority of patients underwent coronary artery bypass grafting, valvular, or aortic surgeries. Across the groups, extracellular water increased postoperatively (20 ± 6 preoperatively to 29 ± 7L on postoperative day 2; P < 0.001). Concomitantly, the levels of the biomarker angiopoietin-2 rose, showing a strong correlation based on the time points of measurements (r = 0.959, P = 0.041). Inflammatory (IL-6, IL-8, CRP) and endothelial biomarkers (VE-Cadherin, syndecan-1, ICAM-1) suggestive of capillary leak were increased. After controlling for common risk factors of edema formation, we found that an increase of 1 ng/mL in angiopoietin-2 was associated with a 0.24L increase in extracellular water (P < 0.001). Angiopoietin-2 showed increased odds for the development of acute kidney injury (OR 1.095 [95% CI 1.032, 1.169]; P = 0.004) and was furthermore associated with delayed extubation, longer time in the ICU, and a higher chance of prolonged dependence on vasoactive medication. Machine learning predicted postoperative complications when capillary leak was added to standard risk factors. CONCLUSIONS: Capillary leak and subsequent edema formation are relevant problems after cardiac surgery. Levels of angiopoietin-2 in combination with extracellular water show promising potential to predict postoperative complications after cardiac surgery. TRIAL REGISTRATION NUMBER: German Clinical Trials Registry (DRKS No. 00017057), Date of registration 05/04/2019, www.drks.de.

Case-matched radiological and clinical outcome evaluation of interlaminar versus microsurgical decompression of lumbar spinal stenosis.

PURPOSE: Endoscopic spine surgery is a globally expanding technique advocated as less invasive for spinal stenosis treatment compared to the microsurgical approach. However, evidence on the efficiency of interlaminar full-endoscopic decompression (FED) vs. conventional microsurgical decompression (MSD) in patients with lumbar spinal stenosis is still scarce. We conducted a case-matched comparison for treatment success with consideration of clinical, laboratory, and radiologic predictors. METHODS: We included 88 consecutive patients (FED: 36/88, 40.9%; MSD: 52/88, 59.1%) presenting with lumbar central spinal stenosis. Surgery-related (operation time, complications, length of stay (LOS), American Society of Anesthesiologists physical status (ASA) score, C-reactive protein (CRP), white blood cell count, side of approach (unilateral/bilateral), patient-related outcome measures (PROMs) (Oswestry disability index (ODI), numeric rating scale of pain (NRS; leg-, back pain), EuroQol questionnaire (eQ-5D), core outcome measures index (COMI)), and radiological (dural sack cross-sectional area, Schizas score (SC), left and right lateral recess heights, and facet angles, respectively) parameters were extracted at different time points up to 1-year follow-up. The relationship of PROMs was analyzed using Spearman's rank correlation. Surgery-related outcome parameters were correlated with patient-centered and radiological outcomes utilizing a regression model to determine predictors for propensity score matching. RESULTS: Complication (most often residual sensorimotor deficits and restenosis due to hematoma) rates were higher in the FED (33.3%) than MSD (13.5%) group (p < 0.05), while all complications in the FED group were observed within the first 20 FED patients. Operation time was higher in the FED, whereas LOS was higher in the MSD group. Age, SC, CRP revealed significant associations with PROMs. We did not observe significant differences in the endoscopic vs. microsurgical group in PROMs. The correlation between ODI and COMI was significantly high, and both were inversely correlated with eQ-5D, whereas the correlations of these PROMs with NRS findings were less pronounced. CONCLUSIONS: Endoscopic treatment of lumbar spinal stenosis was similarly successful as the conventional microsurgical approach. Although FED was associated with higher complication rates in our single-center study experience, the distribution of complications indicated surgical learning curves to be the main factor of these findings. Future long-term prospective studies considering the surgical learning curve are warranted for reliable comparisons of these techniques.

Realizing in-house algorithm-driven free fibula flap set up within 24 hours: a pilot study evaluating accuracy with open-source tools.

Objective: This study aims to critically evaluate the effectiveness and accuracy of a time safing and cost-efficient open-source algorithm for in-house planning of mandibular reconstructions using the free osteocutaneous fibula graft. The evaluation focuses on quantifying anatomical accuracy and assessing the impact on ischemia time. Methods: A pilot study was conducted, including patients who underwent in-house planned computer-aided design and manufacturing (CAD/CAM) of free fibula flaps between 2021 and 2023. Out of all patient cases, we included all with postoperative 3D imaging in the study. The study utilized open-source software tools for the planning step, and three-dimensional (3D) printing techniques. The Hausdorff distance and Dice coefficient metrics were used to evaluate the accuracy of the planning procedure. Results: The study assessed eight patients (five males and three females, mean age 61.75 ± 3.69 years) with different diagnoses such as osteoradionecrosis and oral squamous cell carcinoma. The average ischemia time was 68.38 ± 27.95 min. For the evaluation of preoperative planning vs. the postoperative outcome, the mean Hausdorff Distance was 1.22 ± 0.40. The Dice Coefficients yielded a mean of 0.77 ± 0.07, suggesting a satisfactory concordance between the planned and postoperative states. Dice Coefficient and Hausdorff Distance revealed significant correlations with ischemia time (Spearman's rho = -0.810, p = 0.015 and Spearman's rho = 0.762, p = 0.028, respectively). Linear regression models adjusting for disease type further substantiated these findings. Conclusions: The in-house planning algorithm not only achieved high anatomical accuracy, as reflected by the Dice Coefficients and Hausdorff Distance metrics, but this accuracy also exhibited a significant correlation with reduced ischemia time. This underlines the critical role of meticulous planning in surgical outcomes. Additionally, the algorithm's open-source nature renders it cost-efficient, easy to learn, and broadly applicable, offering promising avenues for enhancing both healthcare affordability and accessibility.

Associations between Periodontitis and COPD: An Artificial Intelligence-Based Analysis of NHANES III.

A number of cross-sectional epidemiological studies suggest that poor oral health is associated with respiratory diseases. However, the number of cases within the studies was limited, and the studies had different measurement conditions. By analyzing data from the National Health and Nutrition Examination Survey III (NHANES III), this study aimed to investigate possible associations between chronic obstructive pulmonary disease (COPD) and periodontitis in the general population. COPD was diagnosed in cases where FEV (1)/FVC ratio was below 70% (non-COPD versus COPD; binary classification task). We used unsupervised learning utilizing k-means clustering to identify clusters in the data. COPD classes were predicted with logistic regression, a random forest classifier, a stochastic gradient descent (SGD) classifier, k-nearest neighbors, a decision tree classifier, Gaussian naive Bayes (GaussianNB), support vector machines (SVM), a custom-made convolutional neural network (CNN), a multilayer perceptron artificial neural network (MLP), and a radial basis function neural network (RBNN) in Python. We calculated the accuracy of the prediction and the area under the curve (AUC). The most important predictors were determined using feature importance analysis. Results: Overall, 15,868 participants and 19 feature variables were included. Based on k-means clustering, the data were separated into two clusters that identified two risk characteristic groups of patients. The algorithms reached AUCs between 0.608 (DTC) and 0.953% (CNN) for the classification of COPD classes. Feature importance analysis of deep learning algorithms indicated that age and mean attachment loss were the most important features in predicting COPD. Conclusions: Data analysis of a large population showed that machine learning and deep learning algorithms could predict COPD cases based on demographics and oral health feature variables. This study indicates that periodontitis might be an important predictor of COPD. Further prospective studies examining the association between periodontitis and COPD are warranted to validate the present results.

Full-endoscopic versus conventional microsurgical therapy of lumbar disc herniation: a prospective, controlled, single-center, comprehensive cohort trial (FEMT-LDH trial).

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.

In Vitro Characterization of a Tissue Renin-Angiotensin System in Human Nucleus Pulposus Cells.

Low back pain is a clinically highly relevant musculoskeletal burden and is associated with inflammatory as well as degenerative processes of the intervertebral disc. However, the pathophysiology and cellular pathways contributing to this devastating condition are still poorly understood. Based on previous evidence, we hypothesize that tissue renin-angiotensin system (tRAS) components, including the SARS-CoV-2 entry receptor angiotensin-converting enzyme 2 (ACE2), are present in human nucleus pulposus (NP) cells and associated with inflammatory and degenerative processes. Experiments were performed with NP cells from four human donors. The existence of angiotensin II, angiotensin II type 1 receptor (AGTR1), AGTR2, MAS-receptor (MasR), and ACE2 in human NP cells was validated with immunofluorescent staining and gene expression analysis. Hereafter, the cell viability was assessed after adding agonists and antagonists of the target receptors as well as angiotensin II in different concentrations for up to 48 h of exposure. A TNF-α-induced inflammatory in vitro model was employed to assess the impact of angiotensin II addition and the stimulation or inhibition of the tRAS receptors on inflammation, tissue remodeling, expression of tRAS markers, and the release of nitric oxide (NO) into the medium. Furthermore, protein levels of IL-6, IL-8, IL-10, and intracellular as well as secreted angiotensin II were assessed after exposing the cells to the substances, and inducible nitric oxide synthase (iNOS) levels were evaluated by utilizing Western blot. The existence of tRAS receptors and angiotensin II were validated in human NP cells. The addition of angiotensin II only showed a mild impact on gene expression markers. However, there was a significant increase in NO secreted by the cells. The gene expression ratios of pro-inflammatory/anti-inflammatory cytokines IL-6/IL-10, IL-8/IL-10, and TNF-α/IL-10 were positively correlated with the AGTR1/AGTR2 and AGTR1/MAS1 ratios, respectively. The stimulation of the AGTR2 MAS-receptor and the inhibition of the AGTR1 receptor revealed beneficial effects on the gene expression of inflammatory and tissue remodeling markers. This finding was also present at the protein level. The current data showed that tRAS components are expressed in human NP cells and are associated with inflammatory and degenerative processes. Further characterization of the associated pathways is warranted. The findings indicate that tRAS modulation might be a novel therapeutic approach to intervertebral disc disease.

Fatigue and Fracture Resistance Testing of Polyether Ether Ketone (PEEK) Implant Abutments in an Ex Vivo Chewing Simulator Model.

Polyether ether ketone (PEEK) has been introduced into implant dentistry as a viable alternative to current implant abutment materials. However, data on its physico-mechanical properties are still scarce. The present study sought to shed light on this topic utilizing an ex vivo chewing simulator model. A total of 48 titanium two-piece implants were allocated into three groups (n = 16 per group): (1) implants with PEEK abutments and an internal butt-joint connection (PBJ), (2) implants with PEEK abutments and an internal conical implant-abutment connection (PC), and (3) implants with zirconia abutments and an internal butt-joint connection (ZA). All abutments were restored with a non-precious metal alloy crown mimicking the upper right central incisor. A dynamic chewing simulation of half (n = 8) of the specimens per group was performed with 5 × 106 cycles and a load of 49 N at a frequency of 1.7 Hz with thermocycling between 5 and 55 °C. The other eight specimens served as unloaded controls. Surface roughness, implant-abutment connection microgaps (IACMs), and the titanium base-abutment interface microgaps (TAIMs) in the loaded groups were evaluated. Finally, a quasi-static loading test was performed in a universal testing machine with all samples to evaluate fracture resistance. Overall, 23 samples survived the artificial chewing process. One abutment screw fracture was observed in the PC group. The ZA group showed higher surface roughness values than PEEK abutments. Furthermore, ZA revealed lower TAIM values compared to PEEK abutments. Similarly, ZA was associated with lower IACM values compared to PBJ. Fracture loads/bending moments were 1018 N/704 N cm for PBJ, 966 N/676 N cm for PC, and 738 N/508 N cm for ZA, with no significant differences compared to the unloaded references. Artificial loading did not significantly affect fracture resistance of the examined materials. PEEK abutments were associated with better load-bearing properties than zirconia abutments, although they showed higher microgap values. PEEK abutments could, therefore, be feasible alternatives to zirconia abutments based on the present ex vivo findings resembling 20 years of clinical service.

Deep Learning on Basal Cell Carcinoma In Vivo Reflectance Confocal Microscopy Data.

BACKGROUND: Extended skin malignancies of the head and neck region are among the most common cancer types and are associated with numerous diagnostic and therapeutical problems. The radical resection of skin cancer in the facial area often leads to severe functional and aesthetic impairment, and precise margin assessments can avoid the extensive safety margins. On the other hand, the complete removal of the cancer is essential to minimize the risk of recurrence. Reliable intraoperative assessments of the wound margins could overcome this discrepancy between minimal invasiveness and safety distance in the head and neck region. With the help of reflectance confocal laser microscopy (RCM), cells can be visualized in high resolution intraoperatively. The combination with deep learning and automated algorithms allows an investigator independent and objective interpretation of specific confocal imaging data. Therefore, we aimed to apply a deep learning algorithm to detect malignant areas in images obtained via in vivo confocal microscopy. We investigated basal cell carcinoma (BCC), as one of the most common entities with well-described in vivo RCM diagnostic criteria, within a preliminary feasibility study. PATIENTS AND METHODS: We included 62 patients with histologically confirmed BCC in the head and neck region. All patients underwent in vivo confocal laser microscope scanning. Approximately 382 images with BCC structures could be obtained, annotated, and proceeded for further deep learning model training. RESULTS: A sensitivity of 46% and a specificity of 85% in detecting BCC regions could be achieved using a convolutional neural network model ("MobileNet"). CONCLUSION: The preliminary results reveal the potential and limitations of the automated detection of BCC with in vivo RCM. Further studies with a larger number of cases are required to obtain better predictability.

Performance of Artificial Intelligence-Based Algorithms to Predict Prolonged Length of Stay after Lumbar Decompression Surgery.

BACKGROUND: Decompression of the lumbar spine is one of the most common procedures performed in spine surgery. Hospital length of stay (LOS) is a clinically relevant metric used to assess surgical success, patient outcomes, and socioeconomic impact. This study aimed to investigate a variety of machine learning and deep learning algorithms to reliably predict whether a patient undergoing decompression of lumbar spinal stenosis will experience a prolonged LOS. METHODS: Patients undergoing treatment for lumbar spinal stenosis with microsurgical and full-endoscopic decompression were selected within this retrospective monocentric cohort study. Prolonged LOS was defined as an LOS greater than or equal to the 75th percentile of the cohort (normal versus prolonged stay; binary classification task). Unsupervised learning with K-means clustering was used to find clusters in the data. Hospital stay classes were predicted with logistic regression, RandomForest classifier, stochastic gradient descent (SGD) classifier, K-nearest neighbors, Decision Tree classifier, Gaussian Naive Bayes (GaussianNB), support vector machines (SVM), a custom-made convolutional neural network (CNN), multilayer perceptron artificial neural network (MLP), and radial basis function neural network (RBNN) in Python. Prediction accuracy and area under the curve (AUC) were calculated. Feature importance analysis was utilized to find the most important predictors. Further, we developed a decision tree based on the Chi-square automatic interaction detection (CHAID) algorithm to investigate cut-offs of predictors for clinical decision-making. RESULTS: 236 patients and 14 feature variables were included. K-means clustering separated data into two clusters distinguishing the data into two patient risk characteristic groups. The algorithms reached AUCs between 67.5% and 87.3% for the classification of LOS classes. Feature importance analysis of deep learning algorithms indicated that operation time was the most important feature in predicting LOS. A decision tree based on CHAID could predict 84.7% of the cases. CONCLUSIONS: Machine learning and deep learning algorithms can predict whether patients will experience an increased LOS following lumbar decompression surgery. Therefore, medical resources can be more appropriately allocated to patients who are at risk of prolonged LOS.