Defect Shape Research Articles

Treatment of Articular Cartilage Defects: A Descriptive Analysis of Clinical Characteristics and Global Trends Reported from 2001 to 2020.

To evaluate the clinical characteristics and global trends in the surgical treatment of articular cartilage defects. Studies in English published between January 1, 2001 and December 31, 2020 were retrieved from MEDLINE, WOS, INSPEC, SCIELO, KJD, and RSCI on the "Web of Science." Patient data were extracted, including age, sex, defect location and laterality, duration of follow-up and symptoms, and body mass index (BMI). Data were further stratified according to the surgical method, lesion location, procedural type and geographical area, and time period. A comparative analysis was performed. Overall, 443 studies involving 26,854 patients (mean age, 35.25 years; men, 60.5%) were included. The mean lesion size and patient BMI were 3.51 cm2 and 25.61 kg/m2, respectively. Cartilage defects at the knees, talus, and hips affected 20,850 (77.64%), 3,983 (14.83%), and 1,425 (5.31%) patients, respectively. The numbers of patients who underwent autologous chondrocyte implantation, arthroscopic debridement/chondroplasty, osteochondral allograft (OCA), osteochondral autologous transplantation, and microfracture were 7,114 (26.49%), 5,056 (18.83%), 3,942 (14.68%), 3,766 (14.02%), and 2,835 (10.56%), respectively. European patients were the most numerous and youngest. North American patients had the largest defects. The number of patients increased from 305 in 2001 to 3,017 in 2020. In the last 5 years, the frequency of OCAs showed a greatly increasing trend. Clinical characteristics and global trends in the surgical treatment of articular cartilage defects were revealed. The choice of operation should be based on the patient characteristics and defect location, size, and shape, as well as the patient's preference.

Myelomeningocele Defects Reconstruction With Various Types of Keystone Flaps: An Algorithmic Approach Based on Defect Shape.

The keystone flap has become an established option that has been utilized recently to close significant myelomeningocele defects. Although the keystone flaps can be adapted to repair myelomeningocele defects of different sizes, the shape of the defect is also an essential factor to consider. In this study, the authors proposed an algorithmic approach for reconstructing significant meningomyelocele defects utilizing different types of keystone flaps based on the shape of the resulting defect. From September 2016 to March 2022, the authors retrospectively reviewed the medical records of 28 patients with meningomyelocele who underwent repair with the keystone flaps. The mean age was 28 days (3 days to 4.5 months). Twenty-one meningomyelocele defects were in the lumbosacral (75%) and 7 in the thoracolumbar regions (25%). The defect sizes ranged from 3.5×5cm (area, 13.4cm 2 ) to 9×7cm (area, 49.5cm 2 ), with an average of 26.2cm 2 . Ten meningomyelocele defects were a vertically oriented oval shape, 16 defects were a transversely oriented oval shape, and 2 defects were circular. In addition, unilateral keystone flap was used in 7.1% of the patients (2 patients), bilateral keystone flap in 28.6% (8 patients), and rotation advancement keystone flaps in 64.3% (18 patients). According to these findings, an algorithm was created based on the defect shape. This algorithmic approach is used as a decision-making guide to select the correct type of keystone flaps to repair myelomeningocele according to the defect shape.

Study on creep-fatigue interaction mechanism and life prediction of aero-engine turbine blade

Aiming at the creep-fatigue interaction damage failure problem of turbine blades in aeronautical engineering, firstly, based on the modified Kachanov-Rabotnov-Chaboche (MKRC) damage mechanics theory, the creep-fatigue life prediction model of turbine blade material was constructed with the experimental verification completion of nickel-based superalloy DZ125. Meanwhile, the creep-fatigue interaction behavior was investigated with the mechanism revelation. Then, considering the shape, size and microscopic defect difference effect from the material to the structure, the creep-fatigue life prediction model of turbine blade structure is proposed by introducing the comprehensive correction factor with experimental verification. Finally, the research results show that there are a large number of interwoven tear edges, micro-cracks and micro-pores in the fracture morphology, and creep and fatigue interact with each other in the form of effective stress. Simultaneously, the creep-fatigue life prediction model has a high life prediction ability with an error of 3%, which can provide a theoretical reference for the damage tolerance design of the turbine blade.

GRA-Net: Global receptive attention network for surface defect detection

Surface defect detection is a key link in the production process of industrial products. However, it is still challenging to realize automatic detection due to the low contrast between the defect and background, the random spatial positions and shapes of defects, and the imbalance between positive and negative samples. Although convolutional neural network (CNN) has been successfully applied to defect detection, current CNN-based methods are still unable to cope with the above challenges because of the insufficient global representation ability, small receptive field, and difficulty in capturing tiny targets. To address these problems, this paper proposes a global receptive attention network (GRA-Net) for surface defect detection. This deep learning system introduces a backbone network combining CNN and transformers for feature extraction, so defects with low contrast can be identified. Then, a multi-level receptive field module is designed to achieve the overall perception for complex and changeable surface defects. Furthermore, a feature interaction attention module is developed to maximize the utilization of the comparatively few defect pixels. Finally, the defect boundary is refined by an adaptive boundary refinement block. Semantic segmentation experiments are conducted based on three datasets. The results show that the mean intersection-over-union and pixel accuracy of GRA-Net are 4–7 % and 2–5 % higher than the existing models, respectively.

New experimental investigation of 3D defects propagation in mortar under tension loading

Among the construction materials, the mortar is the weakest component, especially against tensile loading and cracking. We propose in this paper an experimental study of the propagation of 3D internal defects in the mortar for different dosages and their effects on the tensile strength of the mortar. We start by deter- mining the tensile strength of a non-cracked specimen for five different dosages. After that, as our first contribution, we calculate the tensile strength and the critical Stress Intensity Factor SIF for the different dosages. Then, as our second contribution, we investigate the effect of the defects shape on the stability limit. Finally, we consolidate, through a comparative study, the obtained experimental results with the numerical ones.

The contribution of image processing in the evaluation of guided bone regeneration

One of the most ambitious goals of modern bone surgery is to predict the shape of the bone defect, monitor the progress of bone regeneration, and assess the quantity and quality of newly formed bone. Calcium phosphate biomaterials such as hydroxyapatite and tricalcium phosphate are commonly used as bone substitutes in maxillofacial and dental surgery. The objective of this work is to use cone-beam computed tomography (CBCT) and image processing to assess the spatial (architectural) layout, rate of bone generation and osseointegration of implanted commercial granules (PAH 40%, β-TCP 60%, size 0.5 to 1 mm) in the bone defect generated after tooth extraction. CBCT measurements were performed at 48 hours and 12 months. The analysis of 3D images and the application of appropriate morphological mathematical operations allowed us to evaluate the volume of the cavity to be filled, the volume occupied by the granules and the volume of porosity generated by the random stacking of the granules. The result shows that the bone generation rate reaches a value of 89% after one year of implantation. This study shows that by using 3D image processing techniques CBCT, the same results as classical anatomical and histological studies can be obtained.

Experimental study of the influence of pre-embedded defect shape and size on the performance of adhesively bonded single lap joints using Al-6061 adherends

Experimental study of the influence of pre-embedded defect shape and size on the performance of adhesively bonded single lap joints using Al-6061 adherends

The influence of defect shape on the cracking behavior of brittle materials

To understand the influence of defect shape on the cracking behaviors of brittle materials, three types of gypsum specimens containing different shape defects are designed, and three-point bending tests have been conducted. The results show that the shape of defects can affect the initiation and occurrence of tensile cracks, as the load continuous to increase, the tensile crack usually propagates along the direction of applied compression. The defect shape can also influence the strength of the specimen, and both tensile and shear cracks occur when the load is close to or greater than 50% of the peak strength. Defects with sharp corners are easy to induce the initiation and occurrence of tensile crack, whereas, for the defects with smooth edges, if the defect area is not large enough, it is difficult to breed tensile cracks. Additionally, defect shape has no effect on the fracture toughness, whereas the defect shape can influence the unstable toughness, and when the defect shape is triangular, the distribution of the unstable toughness value varies randomly, and when the defect shape is trapezoidal, the unstable toughness decreases with the increase of defect area. These results are expected to improve the understanding of brittle fracture and can be used to analyze the durability of concrete structures and the stability of rock slopes.

Interpretable artificial intelligence for classification of alveolar bone defect in patients with cleft lip and palate

Cleft lip and/or palate (CLP) is the most common congenital craniofacial anomaly and requires bone grafting of the alveolar cleft. This study aimed to develop a novel classification algorithm to assess the severity of alveolar bone defects in patients with CLP using three-dimensional (3D) surface models and to demonstrate through an interpretable artificial intelligence (AI)-based algorithm the decisions provided by the classifier. Cone-beam computed tomography scans of 194 patients with CLP were used to train and test the performance of an automatic classification of the severity of alveolar bone defect. The shape, height, and width of the alveolar bone defect were assessed in automatically segmented maxillary 3D surface models to determine the ground truth classification index of its severity. The novel classifier algorithm renders the 3D surface models from different viewpoints and captures 2D image snapshots fed into a 2D Convolutional Neural Network. An interpretable AI algorithm was developed that uses features from each view and aggregated via Attention Layers to explain the classification. The precision, recall and F-1 score were 0.823, 0.816, and 0.817, respectively, with agreement ranging from 97.4 to 100% on the severity index within 1 group difference. The new classifier and interpretable AI algorithm presented satisfactory accuracy to classify the severity of alveolar bone defect morphology using 3D surface models of patients with CLP and graphically displaying the features that were considered during the deep learning model's classification decision.

Three-dimensional digital technology-assisted precise tumor resection and reconstruction of the femoral trochanter and postoperative functional recovery: a retrospective study

BackgroundThe trochanter of the femur is a common site for bone tumors. However, locating the specific boundary of bone tumor infiltration and determining the surgical method can be challenging. The objective of this study was to review the diagnosis, treatment, and surgical outcomes of patients with tumors or tumor-like changes in the femoral trochanter after computer-assisted precise tumor resection and hip-preserving reconstruction of the trochanter. MethodsFrom January 2005 to September 2020, 11 patients with trochanteric tumors (aged: 18–53 years; six males and five females) were treated in Guangzhou First People's Hospital. The cases included aneurysmal bone cyst (n = 1), giant cell tumor of bone (n = 2), fibrous histiocytoma of bone (n = 1), endochondroma (n = 1), and fibrous dysplasia of bone (n = 6). For patients with trochanteric tumors, computed tomography and magnetic resonance imaging scanning were performed before operation to obtain two-dimensional image data of the lesion. A three-dimensional digital model of bilateral lower limbs was reconstructed by computer technology, the boundary of tumor growth was determined by computer simulation, the process of tumor resection and reconstruction was simulated, and the personalized guide template was designed. During the operation, the personalized guide plate guided the precise resection of the tumor, and the allogeneic bone was trimmed to match the shape of the bone defect. ResultsAll 11 patients underwent accurate resection of the tumor or tumor-like lesion and reconstruction of the hip. In eight cases, the lesion was confined to the trochanter, which was fixed with large segment allogeneic bone, autologous iliac bone, and proximal femoral anatomic plate. In three cases, allogeneic bone, autologous iliac bone, and femoral reconstruction nail were used to fix the tumor under the trochanter. Postoperative X-ray examination showed that the repair and reconstruction of the bone defect was effective, and callus bridging between the allogenic bone and autogenous bone was observed 6 months after operation. All patients recovered their walking function 3–6 months after operation. The duration of the follow-up period ranged from 6 months to 6 years. A patient experienced recurrence of endochondroma; pathological examination revealed chondrocytic sarcoma. The remaining 10 patients were treated with segmental resection and reconstruction. The operation time ranged 2.5–4.5 h (average: 3.2 h). Intraoperative blood loss ranged from 300 to 500 ml (average: 368 ml). The local recurrence rate was 9.1%, and the overall survival rate was 100%. The average Musculoskeletal Tumor Society score was 27 (excellent and good for eight and three patients, respectively). ConclusionsThree-dimensional computer skeleton modeling and simulation-assisted resection and reconstruction of femoral trochanteric tumor is a new surgical technique, which might markedly improve the surgical effect, shorten the surgical time, increase the overall survival rate of patients with tumors, reduce the local recurrence rate, assist in the digitization and programming of femoral trochanteric tumor surgery, and improve surgical accuracy.

Experimental investigation for multi-defect detection of industrial pipeline based on passive infrared thermal imaging analysis: A case study

Corrosion of pipe walls is a major hidden hazard for the safety of chemical enterprises; therefore, detection of the depth of pipe wall corrosion defects is extremely important. In this study, passive thermal imaging technology was used to detect and evaluate corrosion defects of different shapes in pipe walls and of different depths for the same shape. Three-dimensional finite element models of defects with different depths were established using COMSOL, and the thermal response mechanisms of defects with different depths and the effects of defect depth and shape on heat transfer were analyzed. The thermal response mechanism of defects with different shapes and depths (1–3 mm) was also analyzed experimentally and compared with that of defect-free parts to analyze the safety of the pipeline and verify the results of the numerical simulation analysis. The results indicated that the level of fluid temperature determined the accuracy of the test, and at the same temperature, the defects at a depth of 3 mm exhibited multiple peaks and a large peak area. However, defects with a depth of 2 mm exhibited the opposite behavior, and the heating rate of 3-mm deep defects during the temperature change was 1.08 times higher than that for 2 mm-deep defects. This study provides a reference for detecting leaks in chemical pipelines.

Estimation of burst pressure of pipelines with interacting corrosion clusters based on machine learning models

Pipeline corrosion defects mostly appear in a colony such that they interact to reduce the failure pressure, which is not defined by features of a single corrosion defect. The huge amount of corrosion defects captured by in-line inspection tools including the variability of defect profile in pipelines and the dependence of the reliability assessment on such data pose significant research challenges in performance assurance. This highlights the need for computationally efficient modelling schemes to estimate the burst pressure of pipelines affected by both longitudinal and circumferential interacting corrosion defects. In the present paper, a novel approach is proposed for this purpose by combining supervised machine learning methods with 25 numerical models of corroded pipelines, validated with experimental results available from literature. Additionally, six improved composite defect shapes are proposed, resulting in 150 models to examine the non-linear behaviour of interacting corrosion defects by capturing the real the defect profiles captured by the In-line Inspection tools. The predicted failure pressures from the developed numerical models produced an absolute mean deviation of not exceeding 2.03% and 2.2% from the experimental burst pressure and the modified Mixed Type Interaction approach respectively, better than published results from the literature. Notably, the predicted failure pressures based on real pipeline data, infused with the generated artificial neural networks and non-linear regression models provide a total mean deviation of 3.1% and 7.3% respectively, thereby providing a path for effective maintenance planning.

Defect Classification for Additive Manufacturing with Machine Learning.

Additive manufacturing offers significant design freedom and the ability to selectively influence material properties. However, conventional processes like laser powder bed fusion for metals may result in internal defects, such as pores, which profoundly affect the mechanical characteristics of the components. The extent of this influence varies depending on the specific defect type, its size, and morphology. Furthermore, a single component may exhibit various defect types due to the manufacturing process. To investigate these occurrences with regard to other target variables, this study presents a random forest tree model capable of classifying defects in binary images derived from micrographs. Our approach demonstrates a classification accuracy of approximately 95% when distinguishing between keyhole and lack of fusion defects, as well as process pores. In contrast, unsupervised models yielded prediction accuracies below 60%. The model's accuracy in differentiating between lack of fusion and keyhole defects varies based on the manufacturing process's parameters, primarily due to the irregular shapes of keyhole defects. We provide the model alongside this paper, which can be utilized on a standard computer without the need for in situ monitoring systems during the additive manufacturing process.

Dual-scale point cloud completion network based on high-frequency feature fusion

For many vision tasks and intelligent robotics applications, it is common that the scanned 3D point cloud is not complete, so inferring from the residual defect shape to the intact shape becomes an essential task. Previous 3D completion neural network models generally use voxel-based or point-based methods to learn and process 3D data. For the voxel-based models, the computational cost and memory increase exponentially with the improvement of input resolution, and fine-grained features cannot be guaranteed in the completed point cloud due to limited computational resources. Point-based models suffer from the lack of precision in feature acquisition and crude reconstruction of complicated structures, making it extremely hard to accomplish elaborated semantic shapes. Combining advantages of voxel-based and point-based feature extraction through the high-frequency feature fusion module, this paper proposes a dual-scale point cloud completion network called DSNet, which performs global feature analysis at the voxel scale, and local feature analysis at the point cloud scale. The fused features are then integrated into the decoding and generation process, so as to complete the point cloud completion task from coarse to fine. Experimental results, at both quantitative and qualitative perspectives, in several prevailing datasets demonstrate that our approach surpasses state-of-the-art point cloud completion networks and has a good generalization performance. Code is available at https://github.com/engqing/DSNet.

3D-printed LEGO®-inspired titanium scaffolds for patient-specific regenerative medicine

Despite the recent advances in 3D-printing, it is often difficult to fabricate implants that optimally fit a defect size or shape. There are some approaches to resolve this issue, such as patient-specific implant/scaffold designs based on CT images of the patients, however, this process is labor-intensive and costly. Especially in developing countries, affordable treatment options are required, while still not excluding these patient groups from potential material and manufacturing advances. Here, a selective laser melting (SLM) 3D-printing strategy was used to fabricate a hierarchical, LEGO®-inspired Assemblable Titanium Scaffold (ATS) system, which can be manually assembled in any shape or size with ease. A surgeon can quickly create a scaffold that would fit to the defect right before the implantation during the surgery. Additionally, the direct inclusion of micro- and macroporous structures via 3D-printing, as well as a double acid-etched surface treatment (ST) in the ATS, ensure biocompatibility, sufficient nutrient flow, cell migration and enhanced osteogenesis. Three different structures were designed (non-porous:NP, semi-porous:SP, ultra-porous:UP), 3D-printed with the SLM technique and then surface treated for the ST groups. After analyzing characteristics of the ATS such as printing quality, surface roughness and interconnected porosity, mechanical testing and finite element analysis (FEA) demonstrated that individual and stacked ATS have sufficient mechanical properties to withstand loading in a physiological system. All ATS showed high cell viability, and the SP and UP groups demonstrated enhanced cell proliferation rates compared to the NP group. Furthermore, we also verified that cells were well-attached and spread on the porous structures and successful cell migration between the ATS units was seen in the case of assemblies. The UP and SP groups exhibited higher calcium deposition and RT-qPCR proved higher osteogenic gene expression compared to NP group. Finally, we demonstrate a number of possible medical applications that reveal the potential of the ATS through assembly.

Treatment of pelvic giant cell tumor by wide resection with patient-specific bone-cutting guide and reconstruction with 3D-printed personalized implant

BackgroundThis study reports our experience in the treatment of aggressive pelvic GCT through wide resection assisted with patient-specific bone-cutting guides (PSBCGs) and subsequent reconstruction with 3D-printed personalized implants (3DPIs), aiming to present the operative technique of this method and evaluate its clinical efficacy.MethodsWe retrospectively analyzed seven patients who underwent wide resection of pelvic GCT followed by reconstruction with 3DPIs from August 2019 to February 2021. There were two males and five females, with a mean age of 43 years. PSBCGs and 3DPIs were prepared using 3D-printing technology. The operational outcomes, local recurrence, radiological results, and any associated complications of this technique were assessed. And the functional outcomes were assessed according to the Musculoskeletal Tumor Society (MSTS) 93 functional score.ResultsThe mean follow-up time was 35.3 months (range 28–45 months). There was no intraoperative complication. Negative surgical margins were achieved in all patients. Postoperative pelvic radiographs showed that 3DPIs matched the shape and size of the bone defect. The anterior–posterior, inlet, and outlet pelvic radiograph demonstrated precise reconstruction consistent with the surgical planning. In addition, tomosynthesis‐Shimadzu metal artifact reduction technology (T-SMART) showed good osseointegration at an average of three months after surgery (range 2–4 months). There was no local recurrence or tumor metastasis. The average MSTS score was 24.4 (range 23–27) at the last follow-up. Delayed wound healing was observed in one patient, and the wounds healed after debridement. Prosthesis-related complications were not detected during the follow-up, such as aseptic loosening or structure failure.ConclusionsThe treatment of aggressive pelvic GCTs through wide resection assisted with PSBCGs and subsequent reconstruction with 3DPIs is a feasible method, which provides good clinical results and reasonable functional outcomes.

Effect of internal defects on tensile strength in SLM additively-manufactured aluminum alloys by simulation

This research investigates the effect of internal defects on the tensile strength of Selective Laser Melting (SLM) additively-manufactured aluminum alloy (AlSi10Mg) test parts used for civil aircraft light weight design. A Finite Element Analysis (FEA) model containing internal defects was established by combining test data and the stress concentration factor comparison method. The effect of variation in the number, location and shape of defects on the finite element results was analyzed. Its results show that it is reasonable to use spherical defect modeling. The finite element modeling and analysis methods are also applied to the study of the effect of internal defects on tensile strength in additive manufacturing of other metallic materials. According to the FEA results of single defects at different scales, the formula for calculating the weakening degree of tensile strength applicable to the defective area of less than 15% was established. The result of the procedure is reliable and conservative. This research results can guide the selection of process parameters for the additive manufacturing of aluminum alloys. Further, the research results can promote the application of metal additive manufacturing in designing light-weight civil aircraft structures.

Inceptor facilitates acrosomal vesicle formation in spermatids and is required for male fertility.

Spermatogenesis is a crucial biological process that enables the production of functional sperm, allowing for successful reproduction. Proper germ cell differentiation and maturation require tight regulation of hormonal signals, cellular signaling pathways, and cell biological processes. The acrosome is a lysosome-related organelle at the anterior of the sperm head that contains enzymes and receptors essential for egg-sperm recognition and fusion. Even though several factors crucial for acrosome biogenesis have been discovered, the precise molecular mechanism of pro-acrosomal vesicle formation and fusion is not yet known. In this study, we investigated the role of the insulin inhibitory receptor (inceptor) in acrosome formation. Inceptor is a single-pass transmembrane protein with similarities to mannose-6-phosphate receptors (M6PR). Inceptor knockout male mice are infertile due to malformations in the acrosome and defects in the nuclear shape of spermatozoa. We show that inceptor is expressed in early spermatids and mainly localizes to vesicles between the Golgi apparatus and acrosome. Here we show that inceptor is an essential factor in the intracellular transport of trans-Golgi network-derived vesicles which deliver acrosomal cargo in maturing spermatids. The absence of inceptor results in vesicle-fusion defects, acrosomal malformation, and male infertility. These findings support our hypothesis of inceptor as a universal lysosomal or lysosome-related organelle sorting receptor expressed in several secretory tissues.

A role for the Gram-negative outer membrane in bacterial shape determination

The cell envelope of Gram-negative bacteria consists of three distinct layers: the cytoplasmic membrane, a cell wall made of peptidoglycan (PG), and an asymmetric outer membrane (OM) composed of phospholipid in the inner leaflet and lipopolysaccharide (LPS) glycolipid in the outer leaflet. The PG layer has long been thought to be the major structural component of the envelope protecting cells from osmotic lysis and providing them with their characteristic shape. In recent years, the OM has also been shown to be a load-bearing layer of the cell surface that fortifies cells against internal turgor pressure. However, whether the OM also plays a role in morphogenesis has remained unclear. Here, we report that changes in LPS synthesis or modification predicted to strengthen the OM can suppress the growth and shape defects of Escherichia coli mutants with reduced activity in a conserved PG synthesis machine called the Rod complex (elongasome) that is responsible for cell elongation and shape determination. Evidence is presented that OM fortification in the shape mutants restores the ability of MreB cytoskeletal filaments to properly orient the synthesis of new cell wall material by the Rod complex. Our results are therefore consistent with a role for the OM in the propagation of rod shape during growth in addition to its well-known function as a diffusion barrier promoting the intrinsic antibiotic resistance of Gram-negative bacteria.

A new chevron electromagnetic acoustic transducer design for generating shear horizontal guided wave

Guided wave electromagnetic acoustic transducers (EMATs) have a significant role in non-destructive testing and structural health monitoring. It can generate horizontally polarized shear waves propagating axially or circumferentially to characterize the shape and orientation of pipeline defects. This work proposes a new Chevron pattern-like coil structure design with a periodic permanent magnet (PPM) EMAT configuration. This specific design of the EMAT coil can generate a bi-directional shear horizontal wave (SH-wave) and reduce the side lobes. An optimal Chevron angle of coil wires assists in generating orthogonal propagatingwaveforms. These phenomena lead to the constructive interference of propagating waves and develop a resulting wave along the horizontal direction. A 3D FEM modeling and simulation have been carried out and validated with experimental results. The proposed EMAT results are compared with the conventional racetrack PPM EMAT model, which shows a significant improvement over conventional EMATs. A prototype of this proposed EMAT has been developed. It can be used to inspect surface defects in applications such as fuel transportation pipelines.