Greater Accuracy Research Articles

Predicting Therapeutic Response to Hypoglossal Nerve Stimulation Using Deep Learning.

To develop and validate machine learning (ML) and deep learning (DL) models using drug-induced sleep endoscopy (DISE) images to predict the therapeutic efficacy of hypoglossal nerve stimulator (HGNS) implantation. Patients who underwent DISE and subsequent HGNS implantation at a tertiary care referral center were included. Six DL models and five ML algorithms were trained on images from the base of tongue (BOT) and velopharynx (VP) from patients classified as responders or non-responders as defined by Sher's criteria (50% reduction in apnea-hypopnea index (AHI) and AHI < 15 events/h). Precision, recall, F1 score, and overall accuracy were evaluated as measures of performance. In total, 25,040 images from 127 patients were included, of which 16,515 (69.3%) were from responders and 8,262 (30.7%) from non-responders. Models trained on the VP dataset had greater overall accuracy when compared to BOT alone and combined VP and BOT image sets, suggesting that VP images contain discriminative features for identifying therapeutic efficacy. The VCG-16 DL model had the best overall performance on the VP image set with high training accuracy (0.833), F1 score (0.78), and recall (0.883). Among ML models, the logistic regression model had the greatest accuracy (0.685) and F1 score (0.813). Deep neural networks have potential to predict HGNS therapeutic efficacy using images from DISE, facilitating better patient selection for implantation. Development of multi-institutional data and image sets will allow for development of generalizable predictive models. NA Laryngoscope, 134:5210-5216, 2024.

Crime Prevention and Addressing Violence Against Women

The safety of women remains a pressing societal concern, with many facing threats like harassment, rape, molestation, and domestic abuse due to various sociocultural factors. The Internet of Things (IoT) has emerged as a promising tool to address these issues. This study systematically reviews research papers on IoT-based devices for women's safety, analyzing key features, wearable components, sensor types, and machine learning algorithms used. The review covers articles published between 2016 and 2022. It finds that pulse-rate and pressure sensors are commonly used to monitor women in distress, while technologies like GPS, GSM, and Raspberry Pi enable alert transmission. Machine learning algorithms such as logistic regression, hidden Markov models, and decision trees help identify women at risk and prevent dangerous situations. The review also highlights the need for improved systems that focus on automatic alert generation with minimal human interaction and greater accuracy. In addition, the study proposes a taxonomy categorizing various techniques, features, and sensors, along with an architectural model for developing IoT-based safety devices. Finally, it underscores the importance of integrating multiple sensors to enhance threat detection accuracy, while identifying gaps and challenges in practical applications.

MIX DESIGN AND LABORATORY COMPACTION METHODS OF RCCP – A REVIEW

This article analyzes the difficulties in reproducing the actual circumstances of concrete compaction with rollers in the laboratory and mix design аfeatures of RCCP (Roller Compacted Concrete Pavement). While quick and straightforward, the Proctor compaction test may not adequately reflect the field compaction achieved with rollers. Gyratory compactors simulate the movement of the rollers, providing a more realistic situation with greater accuracy. However, they are expensive and require specialized knowledge. Due to the Vibratory hammer limited application and unrealistic vibration pattern compared to rollers, it is mostly used in laboratories for sample preparation rather than compaction. A vibrating table is effective for less rigid RCC mixture combinations and provides good compaction, although it does not perfectly simulate the action of a roller. The paper also underlines the need to create a link between density and moisture content to achieve maximum RCC performance.

Investigating the fractional Caudrey-Dodd-Gibbon-Sawada-Kotera equation: an iterative framework for nonlinear wave dynamics

Abstract This paper addresses the solution of the fractional Caudrey-Dodd-Gibbon-Sawada-Kotera (CDGSK) equation using the Conformable Laplace Decomposition Method (CLDM). The CDGSK equation, a fundamental model in wave dynamics and fluid mechanics, is explored for its applications in quantum mechanics and nonlinear optics. By employing fractional calculus, we demonstrate how fractional derivatives influence the physical characteristics of wave propagation in both optical and quantum systems. The exact solutions obtained provide insight into soliton behavior, essential for understanding wave-particle interactions in quantum fields and light–matter interactions in optics. The fractional nature of the equation allows for more accurate modeling of non-integer order dynamics commonly found in optical fibers and quantum waveguides. The CLDM method proves to be highly effective, providing approximate solutions with minimal computational effort. These findings offer significant contributions to the fields of quantum mechanics and nonlinear optics, where the fractional CDGSK equation can be applied to solve complex wave equations with great accuracy.

CBCT analysis of mandibular position and bilateral symmetry in adult female patients with a deep overbite before and after flat bite plate treatment: a retrospective study.

To determine whether three-dimensional measurements can assess symmetry and mandibular position with greater accuracy than traditional two-dimensional measurements. Mimics 19 software was used for three-dimensional measurements of 20 adult female patients' cone-beam computed tomography with deep overbite before and after flat bite plate treatment. Differences in mandible position and bilateral symmetry were assessed using the paired t test. Three-dimensional measurements showed that the condylar distance in the pogonion-Frankfort horizontal, pogonion-coronal, right and left gonion-coronal planes was significantly higher after flat bite plate treatment. However, flat bite treatment did not affect the distance between the vertex of the condyles and the gonion of each measurement reference plane. After flat bite plate treatment, the mandibles of adult female patients had rotated backward and downward, with the condyle as the centre; the chin had been backward. Bilateral symmetry remained unchanged. Flat bite plate treatment in adult female patients resulted in a downward and downward rotation of the mandible, with the condyle as the centre, chin backward, and no significant change in bilateral symmetry.

An interpretable (explainable) model based on machine learning and SHAP interpretation technique for mapping wind erosion hazard.

Soil erosion by wind poses a significant threat to various regions across the globe, such as drylands in the Middle East and Iran. Wind erosion hazard maps can assist in identifying the regions of highest wind erosion risk and are a valuable tool for the mitigation of its destructive consequences. This study aims to map wind erosion hazards by developing an interpretable (explainable) model based on machine learning (ML) and Shapley additive exPlanation (SHAP) interpretation techniques. Four ML models, namely random forest (RF), support vector machine (SVM), extreme gradient boosting (XGB), and quadratic discriminant analysis (QDA) were used. Thirteen features associated with wind erosion were mapped spatially and then subjected to a multivariate adaptive regression spline (MARS) feature selection algorithm, and then, tolerance coefficient (TC) and variance inflation factor (VIF) statistical tests were used to explore multicollinearity among the variables. MARS analysis shows that eight features consisting of elevation (or DEM), soil bulk density, precipitation, aspect, slope, soil sand content, vegetation cover (or NDVI), and lithology were the most effective for wind erosion, while no collinearity existed among these variables. The ML models were used for ranking the effective features, and the research introduces the application of an interpretable ML model for the interpretation of predictive model's output. The ranking of effective features by RF-as the most typical ML model-revealed that elevation and soil bulk density were the two most important features. According to the area under the receiver operating characteristic curve (AUROC) (with a value > 90%) and precision-recall (PR) (with a value > 90%) curves, all four ML models performed with great accuracy. According to the PR curve, the SVM model performed slightly better than others, and its results revealed that 20.9%, 23%, and 16.6% of the total area in Hormozgan Province is characterized by moderate, high, and very high hazard classes to wind erosion, respectively. SHAP revealed that soil sand content and elevation are the most important variables contributing to the predictive model output. Overall, our research is one of the pioneering applications of interpretable ML models in mapping wind erosion hazards in Southern Iran. We recommend that future research should address the aspect of interpretability in order to better understand predictive model outputs.

Sonography Could Predict Complete/Near-Complete Necrosis of Thyroid Tumors After Fine-Needle Biopsy: Necrotic Ring Sign.

Necrosis, a rare histologic alteration caused by the fine-needle aspiration biopsy (FNA) of thyroid tumors, could be problematic in the clinical management of such cases due to the difficulty in making a definitive histopathologic diagnosis. The aim of this study was to define US features that may help to predict FNA-induced necrosis which is more common in oncocytic tumors. This cross-sectional study consists of all patients who underwent thyroid surgery in our center between January 2011 and December 2023. All nodules, which complete/near-complete necrosis reported on final histopathology, were included into study when full video record of preoperative US exam also available for re-evaluation. US findings of necrotic tumors compared with control group consisting of similar histopathology without necrosis. A total of 11 patients have met the inclusion criteria during study period. Of these cases, seven were papillary thyroid carcinomas and four were follicular adenomas. Among the evaluated distinctive US features, necrotic ring offers the best accuracy (88%) and area under the curve (0.91) values in detecting necrosis after FNA and most valuable when combined with lack of intranodular vascularity. In the preoperative evaluation of surgical candidates, necrotic ring, refractive edge shadowing, posterior acoustic enhancement, and lack of vascular signal may predict complete/near-complete necrosis with great accuracy.

Automatic face detection based on bidirectional recurrent neural network optimized by improved Ebola optimization search algorithm.

Face detection is a multidisciplinary research subject that employs fundamental computer algorithms, image processing, and patterning. Neural networks, on the other hand, have been widely developed to solve challenges in the domains of feature extraction, pattern detection, and the like in general. The presented study investigates the DNN (deep neural networks) use in the creation of facial detection operating systems. In this study, a novel optimized deep network has been presented to face detection. In this paper, after using some preprocessing stages for contrast enhancement and increasing the data number for the next deep tool, they fed to a bidirectional recurrent neural network (BRNN). The network is optimized via a novel enhanced version of Ebola optimization algorithm to provide far greater accuracy. The suggested procedure is examined on GTFD (Georgia Tech Face Database) and the results indicate that the proposed technique significantly outperforms other comparative methods, attaining an accuracy of 94.3%, a precision of 93.51%, a recall of 94.53%, and an F1-score of 92.47%. Furthermore, the method exhibits resilience against various challenges, achieving an accuracy of 95.6% under occlusions, 96.3% under lighting variations, 94.8% under pose variations, and 92.4% under low resolution conditions. Simulation results depict that the suggested technique gives far greater accuracy in comparison with the other comparative approaches.

Circular supply chain and circular practices: An analysis from the perspective of sustainability and Industry 4.0

AbstractThe objective of the study was to analyse how the integration of Industry 4.0 technologies can promote circular practices within supply chains, aiming for efficiency, transparency and sustainability. The theoretical framework of the research included concepts from Industry 4.0, sustainability, circular supply chains and emerging technologies, underpinning the analysis of the integration of these elements. The research was conducted through a systematic literature review, analysing 28 articles published between 2017 and 2024 that discussed the integration of Industry 4.0 technologies into circular supply chains. The results indicate that the integration of Industry 4.0 technologies predominantly occurs through the paradox of economic growth, efficiency, and technological innovation. This integration aids in promoting circular practices by identifying waste, opportunities for material reuse, tracking waste and by‐products and predicting product demand with greater accuracy, among other benefits. The conclusion of the research highlights the importance of sustainability, innovation and interdisciplinary collaboration in driving more efficient and sustainable practices within supply chains. The contributions include the expansion of knowledge on the integration of Industry 4.0 technologies into circular supply chains, the identification of opportunities for future research, the encouragement of interdisciplinarity and collaboration, and the promotion of more efficient and sustainable practices across various sectors of the economy.

Evaluating the effectiveness of AI-powered UrologiQ's in accurately measuring kidney stone volume in urolithiasis patients.

Kidney stones and urolithiasis are kidney diseases that have a significant impact on health and well-being, and their incidence is increasing annually owing to factors such as age, sex, ethnicity, and geographical location. Accurate identification and volume measurement of kidney stones are critical for determining the appropriate surgical approach, as timely and precise treatment is essential to prevent complications and ensure successful outcomes. Larger stones often require more invasive procedures, and precise volume measurements are essential for effective surgical planning and patient outcomes. This study aimed to compare the ability of artificial intelligence (AI) to detect and measure kidney stone volume via CT-KUB images. CT KUB imaging data were analyzed to determine the effectiveness of AI in identifying the volume of kidney stones. The results were compared with measurements taken by radiologists. Compared with radiologists, the AI had greater accuracy, efficiency, and consistency in measuring kidney stone volume. The AI calculates the volume of kidney stones with an average difference of 80% compared with the volumes calculated by radiologists, highlighting a significant discrepancy that is critical for accurate surgical planning. The results suggest that artificial intelligence (AI) outperforms radiologists' manual calculations in measuring kidney stone volume. By integrating AI with kidney stone detection and treatment, there is potential for greater diagnostic precision and treatment effectiveness, which could ultimately improve patient outcomes.

Virtual Yoga Assistant using Machine Learning and Artificial Intelligence

In recent years, yoga has become an integral part of life for people worldwide. This growing interest has created a demand for scientific analysis of yoga postures. Pose detection techniques offer a promising approach to identify and assist people in performing yoga poses with greater accuracy. However, posture recognition remains a challenging task due to the limited availability of datasets and the difficulty of real-time posture detection. To address this, a large dataset has been developed with over 5,500 images representing ten different yoga poses. A tf-pose estimation algorithm is used to create a skeletal overlay on each image in real time, drawing the human body’s skeleton to extract joint angles. These joint angles serve as features for training various machine learning models. The dataset is split, with 80% used for training and 20% for testing. This approach has been tested on multiple machine learning classification models, achieving an accuracy of 99.04% with a Random Forest Classifier.

On a Stable Multiplicative Calculus-Based Hybrid Parallel Scheme for Nonlinear Equations

Fractional-order nonlinear equation-solving methods are crucial in engineering, where complex system modeling requires great precision and accuracy. Engineers may design more reliable mechanisms, enhance performance, and develop more accurate predictions regarding outcomes across a range of applications where these problems are effectively addressed. This research introduces a novel hybrid multiplicative calculus-based parallel method for solving complex nonlinear models in engineering. To speed up the method’s rate of convergence, we utilize a second-order multiplicative root-finding approach as a corrector in the parallel framework. Using rigorous theoretical analysis, we illustrate how the hybrid parallel technique based on multiplicative calculus achieves a remarkable convergence order of 12, indicating its effectiveness and efficiency in solving complex nonlinear equations. The intrinsic stability and consistency of the approach—when applied to nonlinear situations—are clearly indicated by the symmetry seen in the dynamical planes for various parameter values. The method’s symmetrical behavior indicates that it produces accurate findings under a range of scenarios. Using a dynamical system procedure, the ideal parameter values are systematically analyzed in order to further improve the method’s performance. Implementing the aforementioned parameter values using the parallel approach yields very reliable and consistent outcomes. The method’s effectiveness, reliability, and consistency are evaluated through the analysis of numerous nonlinear engineering problems. The analysis provides a detailed comparison with current techniques, emphasizing the benefits and potential improvements of the novel approach.

Unveiling Symptomatic Pathways of COVID-19: An In-depth Analysis Employing SLIM Association Rule Mining

In order to achieve a thorough comprehension of COVID-19, it is imperative to elucidate the interconnections among its symptoms. The proposed methodology aims to discern patterns that can effectively diagnose the emergence of symptoms, thereby improving diagnostic accuracy and potentially yielding significant breakthroughs. The current study facilitates identifying patterns that signify symptoms' occurrence and convergence. The SLIM algorithm is introduced as a novel method for systematically analyzing large symptom datasets, and its application is presented in this study. The reason for this goes beyond the capabilities of conventional methods, enabling greater diagnostic precision and illuminating new investigational avenues. The present study predicts and identifies COVID-19-related symptoms with greater accuracy, which enhances our understanding of the disease's symptomatology and encourages further study in the area. The results of this study have improved the experience and management of COVID-19's diagnosis, treatment, and pathophysiology, thereby contributing to a better grasp of the virus and bolstering global efforts to manage and mitigate it.

M-IPISincNet: An explainable multi-source physics-informed neural network based on improved SincNet for rolling bearings fault diagnosis

Timely and accurate diagnosis of bearing faults can effectively reduce the chance of accidents in equipment. However, deep learning methods are mostly completely dependent on data and lack interpretability. It is difficult to deal with the differences between real-time data and training data under changing working conditions and noisy environments. In this study, we proposed M-IPISincNet, an explainability multi-source physics-informed convolutional network based on improved SincNet. Rolling bearing fault diagnosis is realized by extracting fault features from vibration and current signals. Firstly, a physics-informed convolutional layer is designed based on inverse Fourier transform and bandpass filters. Fault features are extracted by multi-scale convolution and multi-layer nonlinear mapping. A DBN network is applied extract unsupervised hidden fusion features in the vibration and current signals. The proposed method is validated under the datasets of Paderborn University (PU) and Case Western Reserve University (CWRU), which proves that the proposed method has explainability, robustness and great accuracy under multiple working conditions and noises.

Single-atom ruthenium nanozyme-induced signal amplification strategy in photoelectrochemical aptasensor for ultrasensitive detection of chloramphenicol

To develop ultrasensitive and rapid antibiotics residue detection method is crucial for ensuring food safety and protecting human health. Herein, a novel photoelectrochemical (PEC) aptasensor integrated with single-atom ruthenium (Ru) nanozyme-mediated catalytic precipitation as a valuable signal amplification strategy, have been established for ultrasensitive chloramphenicol (CAP) detection. Particularly, the exceptional peroxidase-mimicking activity of single-atom Ru nanozyme is responsible for accelerating the oxidation of 4-chloro-1-naphthol (4-CN) to produce insoluble precipitate on the electrode, which in turn causes a notable reduction in the photocurrent. Whereas, when CAP is present, the aptamer is liberated away the electrode because of its potent affinity with CAP, resulting in an elevation of the photocurrent signal, enhancing the detection sensitivity. Importantly, the signal amplification strategy combines the effective photoactive material of Au nanoparticles/CdS quantum dot/TiO2 composites, a PEC aptasensor for determination of CAP with an ultralow detection limit of 4.12 pM is achieved in a self-powered mode with great selectivity and accuracy. This work proposes a novel reasonable approach utilizing high-activity single-atom nanozyme to induce signal amplification strategy for the advancement of single-atom nanozyme in ultrasensitive PEC biosensor, and further creates new avenues for ultrasensitive detection beyond antibiotics residue.

Surface Profile Recovery from Electromagnetic Fields with Physics-Informed Neural Networks

Physics-informed neural networks (PINN) have shown their potential in solving both direct and inverse problems of partial differential equations. In this paper, we introduce a PINN-based deep learning approach to reconstruct one-dimensional rough surfaces from field data illuminated by an electromagnetic incident wave. In the proposed algorithm, the rough surface is approximated by a neural network, with which the spatial derivatives of surface function can be obtained via automatic differentiation, and then the scattered field can be calculated using the method of moments. The neural network is trained by minimizing the loss between the calculated and the observed field data. Furthermore, the proposed method is an unsupervised approach, independent of any surface data, where only the field data are used. Both transverse electric (TE) field (Dirichlet boundary condition) and transverse magnetic (TM) field (Neumann boundary condition) are considered. Two types of field data are used here: full-scattered field data and phaseless total field data. The performance of the method is verified by testing with Gaussian-correlated random rough surfaces. Numerical results demonstrate that the PINN-based method can recover rough surfaces with great accuracy and is robust with respect to a wide range of problem regimes.

Diagnostic significance of ultrasound characteristics in discriminating follicular thyroid carcinoma from adenoma

BackgroundFollicular thyroid carcinoma (FTC) is the second most common cancer of the thyroid gland and has a greater propensity for haematogenous metastasis. However, the preoperative differentiation of FTC from follicular thyroid adenoma (FTA) is not well established. Certain ultrasound characteristics are associated with an increased risk of thyroid malignancy, but mainly for papillary thyroid cancers and not for FTC.ObjectivesThis retrospective study aimed to evaluate the ultrasound characteristics of FTC and the value of ultrasound characteristics in differentiating FTC from FTA.MethodsA total of 96 patients with pathologically confirmed FTC or FTA who underwent preoperative thyroid ultrasound were included in this study. The ultrasound and pathological characteristics were evaluated.ResultsOur data revealed that the incidences of lesions with tubercle-in-nodule, spiculated/microlobulated margins, mixed vascularization, egg-shell calcification, central stellate scarring, extension toward the capsule and chronic lymphocytic thyroiditis were significantly higher in the FTC group (all p < 0.05). After adjusting for confounding factors, lesions with mixed vascularization (odds ratio [OR]: 2.038, P = 0.019), central stellate scarring (OR: 87.992, P = 0.007), extension toward the capsule (OR: 22.587, P = 0.010), and chronic lymphocytic thyroiditis (OR: 9.195, P = 0.006) were independently associated with FTC. Furthermore, combined with chronic lymphocytic thyroiditis, mixed vascularization, central stellate scarring, and extension toward the capsule showed high discriminatory accuracy in predicting FTC (AUC: 0.914; sensitivity: 96.5%; specificity: 71.8%; p < 0.001).ConclusionsIn combination with chronic lymphocytic thyroiditis, mixed vascularization, central stellate scarring, and extension toward the capsule have greater accuracy in differentiating FTCs from FTAs.

An Empirical Line approach for Agrowing Camera Aerial Images of inland waters based on exponential fit and in situ water measurements

Abstract. With the advancement of technology in the area of remote sensing, monitoring the Earth's surface using multispectral cameras attached to unmanned aerial vehicles (UAV) has become promising. However, in many Earth observation applications, it is needed to make compatible the spatial data of images captured by high spatial resolution multispectral sensors with the spectral response of targets on the Earth's surface. This relation is obtained through a radiometric calibration. The empirical line method is commonly used to calibrate the spectral bands of sensors. Thus, applications of this method using linear fit have retrieved negative values in water bodies. So that, attempting different adjustments, as well different reference targets, can solve this issue. In this study, the water quality of small bodies of water was analysed using a Agrowing multispectral camera, which derived negative values when applying linear fit. The aim of this study, therefore, was to fit a radiometric calibration based on empirical line method for Agrowing camera in inland water applications. Besides of standard reference targets, water samples also were attempted because showed a lower radiance response than the darkest (black) calibration target. Two empirical line methods were applied to convert the digital number (DN) from the Agrowing images into remote sensing reflectance (Rrs): linear and exponential. The exponential method showed to be more appropriate, with greater accuracy, unlike the linear method.

AI AND MACHINE LEARNING IN BUSINESS PROCESS AUTOMATION: INNOVATING WAYS AI CAN ENHANCE OPERATIONAL EFFICIENCIES OR CUSTOMER EXPERIENCES IN U.S. ENTERPRISES

This study presents a comprehensive review of the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing business processes across various industries. By examining a total of 75 peer-reviewed articles, the review highlights key areas where AI and ML have demonstrated significant impact, including operational efficiency, customer engagement, and strategic decision-making. Findings indicate that AI-driven process optimizations, particularly through predictive maintenance and resource management, have led to substantial cost savings and improved productivity by minimizing downtime and enhancing workflow efficiencies. Additionally, AI’s ability to support personalized customer experiences—through recommendation systems, dynamic pricing, and chatbots—has proven instrumental in driving customer satisfaction, retention, and engagement, making it a critical tool in customer relationship management. Furthermore, the strategic adoption of AI and ML has enabled data-driven decision-making, allowing companies to respond more effectively to market changes and forecast business outcomes with greater accuracy. The study also explores the transition from Robotic Process Automation (RPA) to AI as a foundational step, illustrating how RPA provides a structured entry point for advanced AI applications, creating an automation-ready environment. However, challenges such as technical limitations, ethical concerns, and organizational resistance persist, underscoring the need for careful implementation strategies. Addressing these challenges will be vital for organizations aiming to maximize the benefits of AI-driven automation in an increasingly competitive digital landscape. This review underscores the significant potential of AI and ML in reshaping business models and emphasizes the importance of ongoing research and development in these fields to support sustainable and scalable innovations.

Periradicular Surgery Guided with Erbium Er Cr Laser:YSGG. Case Report

Purpose: The aim of the study is to observe the bone healing process of a guided periradicular surgery, performed in the endodontics postgraduate course of the University of Santo Tomas, in which an osteotomy was performed for a bone window with erbium, chromium: yttrium, scandium, gallium, garnet (Erbium,Chromium:YSGG) laser, and the healing process was evaluated by cone beam computed tomography (CBCT) during the first four months after surgery. Materials and Methods: After completing the clinical history and studying the CBCT scans, a patient was selected with a diagnosis of symptomatic apical periodontitis, periapical lesion less than 5mm with intact vestibular cortex that required periradicular surgery due to the presence of an adapted fixed prosthesis and the proximity of the mental foramen to the apex of the tooth to be treated. The treatment plan was established, the surgical guide was designed and the osteotomy was performed with Erbium,Chromium:YSGG laser. The size of the periradicular lesion and the bone density of the surgical area were measured at three points in time: pre-surgical (T0) and post-surgical at 24 hours (T1), at 4 months (T2) and at one year. Results: Bone density measured in Hounsfield units (HU) on CBCT in the axial section showed preoperative values in the vestibular apex of 1478 and 1435 HU, at 24 hours after surgery -95 and -210 HU, and in the control at 4 months of 992 and 860 HU. In the coronal slice in the mid vestibular area at the apex level, a preoperative value (T0) of 1335 HU was reported, at 24 hours (T1) -18 HU and at 4 months 1269 HU. At one year of evolution, greater bone density was evidenced comparing between month 4 and month 12. Conclusions: By performing a surgical procedure with guided technique and Erbium,Chromium:YSGG laser for the osteotomy, the adverse effects of the surgical procedures are reduced, achieving results with greater accuracy and better prognosis.