Expandable Cages for Lumbar Interbody Fusion: A Narrative Review.

Lumbar fusion surgery for treating degenerative spinal diseases has undergone significant advancements in recent years. In addition to posterior instrumentation, anterior interbody fusion techniques have been developed along with various cages for interbody fusion. Recently, expandable cages capable of altering height, lordotic angle, and footprint within the disc space have garnered significant attention. In this manuscript, we review the current status, clinical outcomes, and future prospects of expandable cages for lumbar interbody fusion based on the existing literature. Expandable cages are suitable for minimally invasive spinal surgeries. Small-sized cages can be inserted and subsequently expanded to a larger size within the disc space. While expandable cages generally demonstrate superior clinical outcomes compared to static cages, some studies have suggested comparable or even poorer outcomes with expandable cages than static cages. Careful interpretation through additional long-term follow-ups is required to assess the utility of expandable cages. If these shortcomings are addressed and the advantages are further developed, expandable cages could become suitable surgical instruments for minimally invasive spinal surgeries.

Contagious infection-free medical interaction system with machine vision controlled by remote hand gesture during an operation.

Background and objective: Medical image visualization is a requirement in many types of surgery such as orthopaedic, spinal, thoracic procedures or tumour resection to eliminate risk such as "wrong level surgery". However, direct contact with physical devices such as mice or touch screens to control images is a challenge because of the potential risk of infection. To prevent the spread of infection in sterile environments, a contagious infection-free medical interaction system has been developed for manipulating medical images. Methods: We proposed an integrated system with three key modules: hand landmark detection, hand pointing, and hand gesture recognition. A proposed depth enhancement algorithm is combined with a deep learning hand landmark detector to generate hand landmarks. Based on the designed system, a proposed hand-pointing system combined with projection and ray-pointing techniques allows for reducing fatigue during manipulation. A proposed landmark geometry constraint algorithm and deep learning method were applied to detect six gestures including click, open, close, zoom, drag, and rotation. Additionally, a control menu was developed to effectively activate common functions. Results: The proposed hand-pointing system allowed for a large control range of up to 1200 mm in both vertical and horizontal direction. The proposed hand gesture recognition method showed high accuracy of over 97% and real-time response. Conclusion: This paper described the contagious infection-free medical interaction system that enables precise and effective manipulation of medical images within the large control range, while minimizing hand fatigue.

Revolutionizing Robotic Depalletizing: AI-Enhanced Parcel Detecting with Adaptive 3D Machine Vision and RGB-D Imaging for Automated Unloading.

Detecting parcels accurately and efficiently has always been a challenging task when unloading from trucks onto conveyor belts because of the diverse and complex ways in which parcels are stacked. Conventional methods struggle to quickly and accurately classify the various shapes and surface patterns of unordered parcels. In this paper, we propose a parcel-picking surface detection method based on deep learning and image processing for the efficient unloading of diverse and unordered parcels. Our goal is to develop a systematic image processing algorithm that emphasises the boundaries of parcels regardless of their shape, pattern, or layout. The core of the algorithm is the utilisation of RGB-D technology for detecting the primary boundary lines regardless of obstacles such as adhesive labels, tapes, or parcel surface patterns. For cases where detecting the boundary lines is difficult owing to narrow gaps between parcels, we propose using deep learning-based boundary line detection through the You Only Look at Coefficients (YOLACT) model. Using image segmentation techniques, the algorithm efficiently predicts boundary lines, enabling the accurate detection of irregularly sized parcels with complex surface patterns. Furthermore, even for rotated parcels, we can extract their edges through complex mathematical operations using the depth values of the specified position, enabling the detection of the wider surfaces of the rotated parcels. Finally, we validate the accuracy and real-time performance of our proposed method through various case studies, achieving mAP (50) values of 93.8% and 90.8% for randomly sized and rotationally covered boxes with diverse colours and patterns, respectively.

Novel Procedure for Automatic Registration between Cone-Beam Computed Tomography and Intraoral Scan Data Supported with 3D Segmentation.

In contemporary practice, intraoral scans and cone-beam computed tomography (CBCT) are widely adopted techniques for tooth localization and the acquisition of comprehensive three-dimensional models. Despite their utility, each dataset presents inherent merits and limitations, prompting the pursuit of an amalgamated solution for optimization. Thus, this research introduces a novel 3D registration approach aimed at harmonizing these distinct datasets to offer a holistic perspective. In the pre-processing phase, a retrained Mask-RCNN is deployed on both sagittal and panoramic projections to partition upper and lower teeth from the encompassing CBCT raw data. Simultaneously, a chromatic classification model is proposed for segregating gingival tissue from tooth structures in intraoral scan data. Subsequently, the segregated datasets are aligned based on dental crowns, employing the robust RANSAC and ICP algorithms. To assess the proposed methodology's efficacy, the Euclidean distance between corresponding points is statistically evaluated. Additionally, dental experts, including two orthodontists and an experienced general dentist, evaluate the clinical potential by measuring distances between landmarks on tooth surfaces. The computed error in corresponding point distances between intraoral scan data and CBCT data in the automatically registered datasets utilizing the proposed technique is quantified at 0.234 ± 0.019 mm, which is significantly below the 0.3 mm CBCT voxel size. Moreover, the average measurement discrepancy among expert-identified landmarks ranges from 0.368 to 1.079 mm, underscoring the promise of the proposed method.

Enhancement of Hip X-ray with Convolutional Autoencoder for Increasing Prediction Accuracy of Bone Mineral Density.

It is very important to keep track of decreases in the bone mineral density (BMD) of elderly people since it can be correlated with the risk of incidence of major osteoporotic fractures leading to fatal injuries. Even though dual-energy X-ray absorptiometry (DXA) is the one of the most precise measuring techniques used to quantify BMD, most patients have restricted access to this machine due to high cost of DXA equipment, which is also rarely distributed to local clinics. Meanwhile, the conventional X-rays, which are commonly used for visualizing conditions and injuries due to their low cost, combine the absorption of both soft and bone tissues, consequently limiting its ability to measure BMD. Therefore, we have proposed a specialized automated smart system to quantitatively predict BMD based on a conventional X-ray image only by reducing the soft tissue effect supported by the implementation of a convolutional autoencoder, which is trained using proposed synthesized data to generate grayscale values of bone tissue alone. From the enhanced image, multiple features are calculated from the hip X-ray to predict the BMD values. The performance of the proposed method has been validated through comparison with the DXA value, which shows high consistency with correlation coefficient of 0.81 and mean absolute error of 0.069 g/cm2.

Machine Learning-Based Measurement of Regional and Global Spinal Parameters Using the Concept of Incidence Angle of Inflection Points.

This study delves into the application of convolutional neural networks (CNNs) in evaluating spinal sagittal alignment, introducing the innovative concept of incidence angles of inflection points (IAIPs) as intuitive parameters to capture the interplay between pelvic and spinal alignment. Pioneering the fusion of IAIPs with machine learning for sagittal alignment analysis, this research scrutinized whole-spine lateral radiographs from hundreds of patients who visited a single institution, utilizing high-quality images for parameter assessments. Noteworthy findings revealed robust success rates for certain parameters, including pelvic and C2 incidence angles, but comparatively lower rates for sacral slope and L1 incidence. The proposed CNN-based machine learning method demonstrated remarkable efficiency, achieving an impressive 80 percent detection rate for various spinal angles, such as lumbar lordosis and thoracic kyphosis, with a precise error threshold of 3.5°. Further bolstering the study's credibility, measurements derived from the novel formula closely aligned with those directly extracted from the CNN model. In conclusion, this research underscores the utility of the CNN-based deep learning algorithm in delivering precise measurements of spinal sagittal parameters, and highlights the potential for integrating machine learning with the IAIP concept for comprehensive data accumulation in the domain of sagittal spinal alignment analysis, thus advancing our understanding of spinal health.

Prediction of Hole Expansion Ratio for Advanced High-Strength Steel with Image Feature Analysis of Sheared Edge.

The punching process of AHSS induces edge cracks in successive process, limiting the application of AHSS for vehicle bodies. Controlling and predicting edge quality is substantially difficult due to the large variation in edge quality, die wear induced by high strength, and the complex effect of phase distribution. To overcome this challenge, a quality prediction model that considers the variation of the entire edge should be developed. In this study, the image of the entire edge was analyzed to provide a comprehensive evaluation of its quality. Statistical features were extracted from the edge images to represent the edge quality of DP780, DP980, and MART1500 steels. Combined with punching monitoring signals, a prediction model for hole expansion ratio was developed under punch conditions of varying clearance, punch angle, and punch edge radius. It was found that the features of grayscale variation are affected by the punching conditions and are related to the double burnish and uneven burr, which degrades the edge quality. Prediction of HER was possible based on only edge image and monitoring signals, with the same performance as the prediction based solely on punching parameters and material properties. The prediction performance improved when using all the features.

Crash Performance of Strength Gradient Tube Induced by Selective Laser Patterning.

This paper presents an investigation of the performance of a 22 MnB5 tube after local heat treatment according to a patterning shape under dynamic crash test conditions to propose the patterning shape with the best energy absorption efficiency. Numerical simulations support experimental results to validate the deformation mode during dynamic crash test as well as the strain distribution of the specimen. The helical patterning not only demonstrates the highest axial loading force and energy absorbance in both static and dynamic crash tests, but also can be easily fabricated in a short time. The helical pattern can optimize different pitch sizes according to the thickness and diameter of the cylindrical tube, and it has the highest energy absorption rate with 83.0% in dynamic conditions.

Direct Writing of Functional Layer by Selective Laser Sintering of Nanoparticles for Emerging Applications: A Review.

Selective laser sintering of nanoparticles enables the direct and rapid formation of a functional layer even on heat-sensitive flexible and stretchable substrates, and is rising as a pioneering fabrication technology for future-oriented applications. To date, laser sintering has been successfully applied to various target nanomaterials including a wide range of metal and metal-oxide nanoparticles, and extensive investigation of relevant experimental schemes have not only reduced the minimum feature size but also have further expanded the scalability of the process. In the beginning, the selective laser sintering process was regarded as an alternative method to conventional manufacturing processes, but recent studies have shown that the unique characteristics of the laser-sintered layer may improve device performance or even enable novel functionalities which were not achievable using conventional fabrication techniques. In this regard, we summarize the current developmental status of the selective laser sintering technique for nanoparticles, affording special attention to recent emerging applications that adopt the laser sintering scheme.

Automated analysis of three-dimensional CBCT images taken in natural head position that combines facial profile processing and multiple deep-learning models.

BACKGROUND AND OBJECTIVES: Analyzing three-dimensional cone beam computed tomography (CBCT) images has become an indispensable procedure for diagnosis and treatment planning of orthodontic patients. Artificial intelligence, especially deep-learning techniques for analyzing image data, shows great potential for medical and dental image analysis and diagnosis. To explore the feasibility of automating measurement of 13 geometric parameters from three-dimensional cone beam computed tomography images taken in natural head position (NHP), this study proposed a smart system that combined a facial profile analysis algorithm with deep-learning models. MATERIALS AND METHODS: Using multiple views extracted from the cone beam computed tomography data of 170 cases as a dataset, our proposed method automatically calculated 13 dental parameters by partitioning, detecting regions of interest, and extracting the facial profile. Subsequently, Mask-RCNN, a trained decentralized convolutional neural network was applied to detect 23 landmarks. All the techniques were integrated into a software application with a graphical user interface designed for user convenience. To demonstrate the system's ability to replace human experts, 30 CBCT data were selected for validation. Two orthodontists and one advanced general dentist located required landmarks by using a commercial dental program. The differences between manual and developed methods were calculated and reported as the errors. RESULTS: The intraclass correlation coefficients (ICCs) and 95% confidence interval (95% CI) for intra-observer reliability were 0.98 (0.97-0.99) for observer 1; 0.95 (0.93-0.97) for observer 2; 0.98 (0.97-0.99) for observer 3 after measuring 13 parameters two times at two weeks interval. The combined ICC for intra-observer reliability was 0.97. The ICCs and 95% CI for inter-observer reliability were 0.94 (0.91-0.97). The mean absolute value of deviation was around 1 mm for the length parameters, and smaller than 2° for angle parameters. Furthermore, ANOVA test demonstrated the consistency between the measurements of the proposed method and those of human experts statistically (Fdis=2.68, ɑ=0.05). CONCLUSIONS: The proposed system demonstrated the high consistency with the manual measurements of human experts and its applicability. This method aimed to help human experts save time and efforts for analyzing three-dimensional CBCT images of orthodontic patients.

Artificial Neural Networks for Predicting Plastic Anisotropy of Sheet Metals Based on Indentation Test.

This paper mainly proposes two kinds of artificial neural network (ANN) models for predicting the plastic anisotropy properties of sheet metal using spherical indentation test, which minimizes measurement time, costs, and simplifies the process of obtaining the anisotropy properties than the conventional tensile test. The proposed ANN models for predicting anisotropic properties can replace the traditional complex dimensionless analysis. Moreover, this paper is not limited to the prediction of yield strength anisotropy but also further accurately predicts the Lankford coefficient in different orientations. We newly construct an FE spherical indentation model, which is suitable for sheet metal in consideration of actual compliance. To obtain a large dataset for training the ANN, the constructed FE model is utilized to simulate pure and alloyed engineering metals with one thousand elastoplastic parameter conditions. We suggest the specific variables of the residual indentation mark as input parameters, also with the indentation load-depth curve. The profile of the residual indentation, including the height and length in different orientations, are used to analyze the anisotropic properties of the material. Experimental validations have been conducted with three different sheet alloys, TRIP1180 steel, zinc alloy, and aluminum alloy 6063-T6, comparing the proposed ANN model and the uniaxial tensile test. In addition, machine vision was used to efficiently analyze the residual indentation marks and automatically measure the indentation profiles in different orientations. The proposed ANN model exhibits remarkable performance in the prediction of the flow curves and Lankford coefficient of different orientations.

Numerical Simulation of the Effects of Scanning Strategies on the Aluminum Evaporation of Titanium Alloy in the Electron Beam Cold Hearth Melting Process.

In the production of titanium alloy, the electron beam cold hearth melting (EBCHM) process is commonly used due to its effectiveness and the high quality of the end product. However, its main drawback is the significant loss of elements such as aluminum (Al) due to evaporation under the vacuum environment. Numerical coupled thermal-flow models were here developed to investigate the effects of scanning strategies on Al loss in a titanium alloy during EBCHM. The validation model was successful in comparison with previously published experimental data. The Al mass fraction results at the outlet of the water-cooled hearth were strongly influenced by changes in the applied scanning strategies. The results indicated that the Al mass fraction loss could be reduced by using the full-hearth scanning strategies.

Integrated Machine Vision System for Evaluating Hole Expansion Ratio of Advanced High-Strength Steels.

In this paper, we propose a new method to estimate the hole expansion ratio (HER) using an integrated analysis system. To precisely measure the HER, three kinds of analysis methods (computer vision, punch load, and acoustic emission) were utilized to detect edge cracks during a hole expansion test. Cracks can be recognized by employing both computer vision and a punch load analysis system to determine the moment of crack initiation. However, the acoustic emission analysis system has difficulty detecting the instant of crack appearance since the magnitude of the audio signal is drowned out by noise from the press, which interrupts the differentiation of crack configuration. To enhance the accuracy for determining the HER, an integrated analysis system that combines computer vision with punch load analysis, and improves on the shortcomings of each analysis system, is newly suggested.

Intelligent Evaluation of Global Spinal Alignment by a Decentralized Convolutional Neural Network.

Degenerative changes of the spine can cause spinal misalignment, with part of the spine arching beyond normal limits or moving in an incorrect direction, potentially resulting in back pain and significantly limiting a person's mobility. The most important parameters related to spinal misalignment include pelvic incidence, pelvic tilt, lumbar lordosis, thoracic kyphosis, and cervical lordosis. As a general rule, alignment of the spine for diagnosis and surgical treatment is estimated based on geometrical parameters measured manually by experienced doctors. However, these measurements consume the time and effort of experts to perform repetitive tasks that could be automated, especially with the powerful support of current artificial intelligence techniques. This paper focuses on creation of a decentralized convolutional neural network to precisely measure 12 spinal alignment parameters. Specifically, this method is based on detecting regions of interest with its dimensions that decrease by three orders of magnitude to focus on the necessary region to provide the output as key points. Using these key points, parameters representing spinal alignment are calculated. The quality of the method's performance, which is the consistency of the measurement results with manual measurement, is validated by 30 test cases and shows 10 of 12 parameters with a correlation coefficient > 0.8, with pelvic tilt having the smallest absolute deviation of 1.156°.

A Contemporary Review of Aluminium MMC Developed through Stir-Casting Route.

The growing demand for composite materials with improved properties is attracting a lot of attention from industries such as automotive, aerospace, military, aviation, and other manufacturing. Aluminium metal matrix composites (AMMCs), with various reinforcements such as continuous/discontinuous fibers, whiskers, and particulates, have captured the attention due to their superior tribological, mechanical, and microstructural characteristics as compared to bare Al alloy. AMMCs have undergone extensive research and development with different reinforcements in order to obtain the materials with the desired characteristics. In this paper, we present a review on AMMCs produced through stir casting routes. This review focuses on the following aspects: (i) different reinforcing materials in AMMCs; (ii) microstructural study of reinforced metal matrix composites (MMCs) through stir casting. Both reinforcing micro- and nanoparticles are focused. Micro- and nanoreinforced AMMCs have the attractive properties of combination such asthe low-weight-to-high-strength rati and, low density; (iii) various tribological and mechanical properties with the consideration of different input parameters; (iv) outlook and perspective.

Dissimilar Materials Welding with a Standoff-Free Vaporizing Foil Actuator between TRIP 1180 Steel Sheets and AA5052 Alloy.

This paper mainly demonstrates an advanced type of the vaporizing foil actuator welding (VFAW) process between GPa-grade steel (TRIP1180) and aluminum alloy (AA5052-H32) without applying standoff. To secure a flying distance during the VFAW process, the preformed target sheet shaped like a circular indentation has been utilized. It is necessary to optimize process parameters integrated with geometrical design of the preform since the welding strength can be decreased beyond the optimum input energy in the standoff-free VFAW process. The welded surface was evaluated by SEM-EDS, XRD, EBDS, and TEM to analyze the welding mechanism and composition at the welding interface. The diffusion zone including the AlFe3 phase was observed at the welded interface which has high grain density due to the high-speed impact by increasing the welding strength, which leads to the perfect welding between the dissimilar materials.

Detailed Inspection of γ-ray, Fast and Thermal Neutrons Shielding Competence of Calcium Oxide or Strontium Oxide Comprising Bismuth Borate Glasses.

For both the B2O3-Bi2O3-CaO and B2O3-Bi2O3-SrO glass systems, γ-ray and neutron attenuation qualities were evaluated. Utilizing the Phy-X/PSD program, within the 0.015-15 MeV energy range, linear attenuation coefficients (µ) and mass attenuation coefficients (µ/ρ) were calculated, and the attained µ/ρ quantities match well with respective simulation results computed by MCNPX, Geant4, and Penelope codes. Instead of B2O3/CaO or B2O3/SrO, the Bi2O3 addition causes improved γ-ray shielding competence, i.e., rise in effective atomic number (Zeff) and a fall in half-value layer (HVL), tenth-value layer (TVL), and mean free path (MFP). Exposure buildup factors (EBFs) and energy absorption buildup factors (EABFs) were derived using a geometric progression (G-P) fitting approach at 1-40 mfp penetration depths (PDs), within the 0.015-15 MeV range. Computed radiation protection efficiency (RPE) values confirm their excellent capacity for lower energy photons shielding. Comparably greater density (7.59 g/cm3), larger µ, µ/ρ, Zeff, equivalent atomic number (Zeq), and RPE, with the lowest HVL, TVL, MFP, EBFs, and EABFs derived for 30B2O3-60Bi2O3-10SrO (mol%) glass suggest it as an excellent γ-ray attenuator. Additionally, 30B2O3-60Bi2O3-10SrO (mol%) glass holds a commensurably bigger macroscopic removal cross-section for fast neutrons (ΣR) (=0.1199 cm-1), obtained by applying Phy-X/PSD for fast neutrons shielding, owing to the presence of larger wt% of 'Bi' (80.6813 wt%) and moderate 'B' (2.0869 wt%) elements in it. 70B2O3-5Bi2O3-25CaO (mol%) sample (B: 17.5887 wt%, Bi: 24.2855 wt%, Ca: 11.6436 wt%, and O: 46.4821 wt%) shows high potentiality for thermal or slow neutrons and intermediate energy neutrons capture or absorption due to comprised high wt% of 'B' element in it.

A novel approach for evaluating bone mineral density of hips based on Sobel gradient-based map of radiographs utilizing convolutional neural network.

Osteoporosis, which is a common disorder associated with low bone mineral density (BMD), is one of the primary reasons for hip fracture. It not only limits mobility, but also makes the patient suffer from pain. Unlike traditional methods, which require both expensive equipment and long scanning times, this study aims to develop a novel technique employing a convolutional neural network (CNN) directly on radiographs of the hips to evaluate BMD. To construct the dataset, X-ray photographs of lower limbs and dual-energy X-ray absorptiometry (DXA) results of the hips of patients were collected. The core of this research is a deep learning-based model that was trained using the pre-processed X-rays images of 510 hips as the input data and the BMD values obtained from DXA as the standard reference. To improve performance quality, the radiographs of the hips were processed with a Sobel algorithm to extract the gradient magnitude maps, and an ensemble artificial neural network which analyses the outputs of CNN models corresponding to three Singh sites and biological parameters was utilized. The superior performance of the proposed method was confirmed by the high correlation coefficient of 0.8075 (p<0.0001) of the BMD measured by DXA in a total of 150 testing cases, with only 0.12 s required for applying the computing configuration to a single X-ray image.

Decentralized convolutional neural network for evaluating spinal deformity with spinopelvic parameters.

Low back pain which is caused by the abnormal spinal alignment is one of the most common musculoskeletal symptom and, consequently, is the reason for not only reduction of productivity but also personal suffering. In clinical diagnosis for this disease, estimating adult spinal deformity is required as an indispensable procedure in highlighting abnormal values to output timely warnings and providing precise geometry dimensions for therapeutic therapies. This paper presents an automated method for precisely measuring spinopelvic parameters using a decentralized convolutional neural network as an efficient replacement for current manual process which not only requires experienced surgeons but also shows limitation in ability to process large numbers of images to accommodate the explosion of big data technologies. The proposed method is based on gradually narrowing the regions of interest (ROIs) for feature extraction and leads the model to mainly focus on the necessary geometry characteristics represented as keypoints. According to keypoints obtained, parameters representing the spinal deformity are calculated, which consistency with manual measurement was validated by 40 test cases and, potentially, provided 1.45o mean absolute values of deviation for PTA as the minimum and 3.51o in case of LSA as maximum.

Intelligent analysis of coronal alignment in lower limbs based on radiographic image with convolutional neural network.

One of the first tasks in osteotomy and arthroplasty is to identify the lower limb varus and valgus deformity status. The measurement of a set of angles to determine this status is generally performed manually with the measurement accuracy depending heavily on the experience of the person performing the measurements. This study proposes a method for calculating the required angles in lower limb radiographic (X-ray) images supported by the convolutional neural network. To achieved high accuracy in the measuring process, not only is a decentralized deep learning algorithm, including two orders for the radiographic, utilized, but also a training dataset is built based on the geometric knowledge related to the deformity correction principles. The developed algorithm performance is compared with standard references consisting of manually measured values provided by doctors in 80 radiographic images exhibiting an impressively low deviation of less than 1.5° in 82.3% of the cases.