3 d printing technology in Biomaterials
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3D Printing is vast wide field. Here u can get more detail by check the link given below. https://zshort.io/3dprinting Stay tune for regular information.
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Inside3DPrinting_JonathanButcher
(1) 3D bioprinting offers promising techniques for tissue engineering by allowing the rapid fabrication of living tissues and organs. (2) However, challenges remain including developing cell-friendly inks, controlling tissue properties, and translating techniques to clinical applications. (3) The document outlines research at Cornell University using 3D printing to engineer tissues like heart valves, cartilage, and intervertebral discs with goals of developing customized living implants.
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3D printing has various applications in orthopedics including pre-operative planning, creating implants and prosthetics, and patient-specific instrumentation. The document discusses several studies that demonstrate benefits of 3D printing such as improved screw placement accuracy, reduced radiation exposure and operation time, and aiding complex surgical planning. Applications discussed include using 3D printed models and guides for fractures of the acromion, clavicle, humerus, elbow, wrist, and acetabulum.
3D Bioprinting
It has been expleined in these slides that how 3D bioprinters work and some of them have been introdused. Also some examples of use 3D bioprinter in reality are introduced.
Finally feature of 3D bioprinters in human life has been explained.
3D Bioprinting
its about 3D printing and scanning of internal organ , biomolecules and tissues
It is an emerging field in tissue engineering, surgery and transplant of organs
3d Bioprinting
layer-by-layer precise positioning of biological materials, biochemicals and living cells, with spatial control of the placement of functional components (extracellular matrix, cells and pre-organized micro vessels) to fabricate 3D structures.
Role of 3D printing & 3D model in Complex Total Hip Replacement
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Dr. Kalaivanan Kanniyan
for queries - drkkbriyan@gmail.com / drkkbriyan@outlook.com
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Inside3DPrinting_JonathanButcher
(1) 3D bioprinting offers promising techniques for tissue engineering by allowing the rapid fabrication of living tissues and organs. (2) However, challenges remain including developing cell-friendly inks, controlling tissue properties, and translating techniques to clinical applications. (3) The document outlines research at Cornell University using 3D printing to engineer tissues like heart valves, cartilage, and intervertebral discs with goals of developing customized living implants.
Futuristic 3D printing applications
3D printing technology has evovled over years and showing signs of successful innovations in Bio 3D printing.
Bioprinting and 3D printing for educational centres
Do you know the benefits for educational centers and universities of integrating 3D printing and bioprinting technologies in their activity? contact us: info@regemat3D.com
3d printing in orthopedics
3D printing has various applications in orthopedics including pre-operative planning, creating implants and prosthetics, and patient-specific instrumentation. The document discusses several studies that demonstrate benefits of 3D printing such as improved screw placement accuracy, reduced radiation exposure and operation time, and aiding complex surgical planning. Applications discussed include using 3D printed models and guides for fractures of the acromion, clavicle, humerus, elbow, wrist, and acetabulum.
3D Bioprinting
It has been expleined in these slides that how 3D bioprinters work and some of them have been introdused. Also some examples of use 3D bioprinter in reality are introduced.
Finally feature of 3D bioprinters in human life has been explained.
3D Bioprinting
its about 3D printing and scanning of internal organ , biomolecules and tissues
It is an emerging field in tissue engineering, surgery and transplant of organs
3d Bioprinting
layer-by-layer precise positioning of biological materials, biochemicals and living cells, with spatial control of the placement of functional components (extracellular matrix, cells and pre-organized micro vessels) to fabricate 3D structures.
Role of 3D printing & 3D model in Complex Total Hip Replacement
Role of 3D printing & 3D model in Complex Total Hip Replacement
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for queries - drkkbriyan@gmail.com / drkkbriyan@outlook.com
Asian Joint Reconstruction Institute
AJRI
chennai
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complex hip replacement , knee replacment, knee navigation
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Medical image process is that the most difficult and rising field currently now a day. Process of MRI pictures is one amongst the part of this field. This paper describes the projected strategy to find & extraction of tumour from patient's MRI scan pictures of the brain. This technique incorporates with some noise removal functions, segmentation and morphological operations that area unit the fundamental ideas of image process. Detection and extraction of tumor from MRI scan pictures of the brain is finished by victimization MATLAB software package Satish Chandra. B | Smt K. Satyavathi | Dr. Krishnanaik Vankdoth"Implementation of Brain Tumor Extraction Application from MRI Image" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd15701.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/15701/implementation-of-brain-tumor-extraction-application-from-mri-image/satish-chandra-b
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vascular structure is modelled as a vessel segment graph and the true vessels are identified by using supervised
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I reviewed 3 papers at 'SNU TF Study Group' in Korea.
3 papers tried to solve segmentation problems in medical images with Deep Learning.
Deep Learning 을 이용하여 의료 영상에서 Segmentation 문제를 풀고자 한 3가지 논문을 리뷰하였습니다. :)
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A NOVEL AUTOMATED IDENTIFICATION OF INTIMA MEDIA THICKNESS IN
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Image processing is an active research area in which medical image processing is a highly challenging field. Medical imaging
techniques are used to image the inner portions of the human body for medical diagnosis. Brain tumor is a serious life altering
disease condition. Image segmentation plays a significant role in image processing as it helps in the extraction of suspicious regions
from the medical images. In this paper we have proposed segmentation of brain MRI image using K-means clustering algorithm
followed by morphological filtering which avoids the misclustered regions that can inevitably be formed after segmentation of the brain MRI image for detection of tumor location.
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This document describes a new algorithm for fully automatic brain tumor segmentation using 3D convolutional neural networks. The algorithm uses 3D convolutional filters to preserve spatial information, and a high-bias CNN architecture to increase effective data size and reduce model variance. On a dataset of 274 brain MR images, the algorithm achieved a median Dice score of 89% for whole tumor segmentation, significantly outperforming past methods. This demonstrates the effectiveness of generalizing low-bias high-variance methods like CNNs to learn from medium-sized datasets.Segmentation of cysts in kidney and 3 d volume calculation from ct images
Statistics based optimization, Plackett–Burman design (PBD) and response surface methodology
(RSM) were employed to screen and optimize the media components for the production of
clavulanic acid from Streptomyces clavuligerus MTCC 1142, using solid state fermentation. jackfruit
seed powder was used as both the solid support and carbon source for the growth of Streptomyces
clavuligerus MTCC 1142. Based on the positive influence of the Pareto chart obtained from PBD on
clavulanic acid production, five media components – yeast extract, beef extract, sucrose, malt extract
and ferric chloride were screened. Central composite design (CCD) was employed using these five
media components- yeast extract 2.5%, beef extract 0.5%, sucrose 2.5%, malt extract 0.25% and ferric
chloride nutritional factors at three levels, for further optimization, and the second order polynomial
equation was derived, based on the experimental data. Response surface methodology showed that
the concentrations of yeast extract 2.5%, beef extract 0.5%, sucrose 2.5%, malt extract 0.25% and ferric
chloride 2.5% were the optimal levels for maximal clavulanic acid production (19.37 mg /gds) which were validated through experiments.
Applications of 3 d printing in biomedical engineering
Medical applications of 3D printing are expanding rapidly and may revolutionize healthcare. Current uses include creating customized prosthetics and implants, anatomical models for surgery planning, and complex drug dosage forms through various printing techniques like selective laser sintering and inkjet printing. Researchers are working to develop organ printing through layer-by-layer deposition of living cells and biomaterials. While significant advances have been made, the most transformative applications like full organ printing will require more time and addressing remaining scientific and regulatory challenges.
3D healthcare
3D printing has significant potential to impact healthcare by enabling the creation of customized medical devices, implants, and tissue scaffolds. It allows for new designs that cannot be created through traditional manufacturing. The technology is also being used to print microfluidic chips, customized food, and the first approved 3D printed drug. While standards are still being developed, 3D printing promises to transform healthcare through personalized solutions, on-demand manufacturing, and open collaboration between various stakeholders in the industry and research community.
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3D printing has been widely adopted in surgical practice for a variety of applications. It has been used to print patient-specific anatomical models for pre-operative planning and education, as well as implants, prosthetics and other medical devices customized for individual patients. Surgeons have also utilized 3D printed surgical guides and instruments. This technology allows for simulated surgeries using customized models and helps optimize pre-operative planning and equipment selection.
8-13-2013LPPPresentation
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3D bio-printing is a process that uses 3D printing technologies to create cell patterns in a confined space while preserving cell function and viability. It works by layering liquid mixtures of cells and nutrients to structure tissue-like constructs for medical and tissue engineering uses. Some key developments include the first artificial bladder and trachea built by bio-printing in 2006 and 2011. The process generally involves pre-bio-printing to create models, bio-printing to structure cells using patient scans, and post-bio-printing to create stable structures. Applications include using bio-printed tissues for transplants, drug research, and eventually fully functional human organs.
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This document describes a study that used texture feature analysis and a bidirectional associative memory (BAM) type artificial neural network to segment normal and tumor tissues from brain CT images. Gray level co-occurrence matrix features were extracted from 80 CT images of normal, benign and malignant tumors. The most discriminative features were selected using t-tests and used to train the BAM network classifier to segment tissues in the images. The proposed method provided accurate segmentation of normal and tumor regions, especially small tumors, in an efficient and fast manner with less computational time compared to other methods.
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This document describes a study that evaluated the accuracy of using computer-assisted virtual surgical planning and guides for reconstructing zygomatic bone defects with vascularized iliac crest bone grafts. CT scans of patients' faces and bone grafts were used to create 3D models and virtual surgical plans. Surgical guides were fabricated to transfer the plans intraoperatively. Postoperative CT scans found the mean difference in bone graft shape and position between actual and planned was 0.71mm and 3.53mm respectively, indicating good accuracy of the computer-assisted method.
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This document summarizes research on skin bioprinting. It discusses the current state of the field, challenges, and potential applications. Some key points made include:
- Skin cells have been successfully bioprinted and cultured, but bioprinted skin constructs currently lack functionality to be used as skin implants due to the absence of features like skin pigmentation and vascularization.
- The field of skin bioprinting is still in its infancy, with more work needed on materials, printing processes, and maturation processes.
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Medical image process is that the most difficult and rising field currently now a day. Process of MRI pictures is one amongst the part of this field. This paper describes the projected strategy to find & extraction of tumour from patient's MRI scan pictures of the brain. This technique incorporates with some noise removal functions, segmentation and morphological operations that area unit the fundamental ideas of image process. Detection and extraction of tumor from MRI scan pictures of the brain is finished by victimization MATLAB software package Satish Chandra. B | Smt K. Satyavathi | Dr. Krishnanaik Vankdoth"Implementation of Brain Tumor Extraction Application from MRI Image" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd15701.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/15701/implementation-of-brain-tumor-extraction-application-from-mri-image/satish-chandra-b
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This study evaluated the feasibility of using 3D printing to create models from neuroimaging data to aid in medical education and patient communication. The researchers were able to take CT scan data, isolate the brain tissue and vasculature, convert it to 3D printable files, and produce prototypes using a 3D printer. Their next steps are to print the tissues together using different materials. Once optimized, they plan to assess if 3D printed models improve training for medical students and clinicians in neuropathology.
Retinal Vessels Segmentation Using Supervised Classifiers for Identification ...
The risk of cardio vascular diseases can be identified by measuring the retinal blood vessel. The
identification of wrong blood vessel may result in wrong clinical diagnosis. This proposed system addresses the
problem of identifying the true vessel by vascular structure segmentation. In this proposed model the segmented
vascular structure is modelled as a vessel segment graph and the true vessels are identified by using supervised
classifier approach. This paper proposes a post processing step in diagnose cardiovascular diseases which can
be identified by tracking a true vessel from the optimal forest in the graph given a set on constraints.
Segmentation problems in medical images
I reviewed 3 papers at 'SNU TF Study Group' in Korea.
3 papers tried to solve segmentation problems in medical images with Deep Learning.
Deep Learning 을 이용하여 의료 영상에서 Segmentation 문제를 풀고자 한 3가지 논문을 리뷰하였습니다. :)
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Automated reconstruction and diagnosis of brain MRI images is one of the most challenging problems in medical imaging. Accurate segmentation of MRI images is a key step in contouring during radiotherapy analysis. Computed tomography (CT) and Magnetic resonance (MR) imaging are the most widely used radiographic techniques in diagnosis and treatment planning. Segmentation techniques used for the brain Magnetic Resonance Imaging (MRI) is one of the methods used by the radiographer to detect any abnormality specifically in brain. The method also identifies important regions in brain such as white matter (WM), gray matter (GM) and cerebrospinal fluid spaces (CSF). These regions are significant for physician or radiographer to analyze and diagnose the disease. We propose a novel clustering algorithm, improved LOGISMOS-B to classify tissue regions based on probabilistic tissue classification, generalized gradient vector flows with cost and distance function. The LOGISMOS graph segmentation framework. Expand the framework to allow regionally-aware graph construction and segmentation.
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This document presents a method for liver segmentation using a 2D U-Net model. The method first preprocesses CT scan images using techniques like windowing, masking, normalization, and morphological operations. It then trains a 2D U-Net model on slices containing liver regions only, in order to focus modeling on liver segmentation. To improve inference performance on full volumes which may contain non-liver slices, the method uses histogram analysis to roughly select a range of slices likely to contain liver, such as the central 40% of slices. Evaluation shows this rough selection approach improves segmentation accuracy compared to applying the model to full volumes directly.
A novel automated identification of intima media thickness in ultrasound
A NOVEL AUTOMATED IDENTIFICATION OF INTIMA MEDIA THICKNESS IN
ULTRASOUND ARTERY IMAGES USING GREY AND WHITE MATTER
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Image processing is an active research area in which medical image processing is a highly challenging field. Medical imaging
techniques are used to image the inner portions of the human body for medical diagnosis. Brain tumor is a serious life altering
disease condition. Image segmentation plays a significant role in image processing as it helps in the extraction of suspicious regions
from the medical images. In this paper we have proposed segmentation of brain MRI image using K-means clustering algorithm
followed by morphological filtering which avoids the misclustered regions that can inevitably be formed after segmentation of the brain MRI image for detection of tumor location.
A New Algorithm for Fully Automatic Brain Tumor Segmentation with 3-D Convolu...
A New Algorithm for Fully Automatic Brain Tumor Segmentation with 3-D Convolu...Christopher Mehdi Elamri
This document describes a new algorithm for fully automatic brain tumor segmentation using 3D convolutional neural networks. The algorithm uses 3D convolutional filters to preserve spatial information, and a high-bias CNN architecture to increase effective data size and reduce model variance. On a dataset of 274 brain MR images, the algorithm achieved a median Dice score of 89% for whole tumor segmentation, significantly outperforming past methods. This demonstrates the effectiveness of generalizing low-bias high-variance methods like CNNs to learn from medium-sized datasets.Segmentation of cysts in kidney and 3 d volume calculation from ct images
Statistics based optimization, Plackett–Burman design (PBD) and response surface methodology
(RSM) were employed to screen and optimize the media components for the production of
clavulanic acid from Streptomyces clavuligerus MTCC 1142, using solid state fermentation. jackfruit
seed powder was used as both the solid support and carbon source for the growth of Streptomyces
clavuligerus MTCC 1142. Based on the positive influence of the Pareto chart obtained from PBD on
clavulanic acid production, five media components – yeast extract, beef extract, sucrose, malt extract
and ferric chloride were screened. Central composite design (CCD) was employed using these five
media components- yeast extract 2.5%, beef extract 0.5%, sucrose 2.5%, malt extract 0.25% and ferric
chloride nutritional factors at three levels, for further optimization, and the second order polynomial
equation was derived, based on the experimental data. Response surface methodology showed that
the concentrations of yeast extract 2.5%, beef extract 0.5%, sucrose 2.5%, malt extract 0.25% and ferric
chloride 2.5% were the optimal levels for maximal clavulanic acid production (19.37 mg /gds) which were validated through experiments.
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Bioprinting and bionks a new paradigm for 3 d organ development
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Implementation of Brain Tumor Extraction Application from MRI Image
Lumbar disk 3D modeling from limited number of MRI axial slices
Lumbar disk 3D modeling from limited number of MRI axial slices
Retinal Vessels Segmentation Using Supervised Classifiers for Identification ...
Retinal Vessels Segmentation Using Supervised Classifiers for Identification ...
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Segmentation and Classification of Brain MRI Images Using Improved Logismos-B...
A novel automated identification of intima media thickness in ultrasound
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Similar to 3 d printing technology in Biomaterials
Applications of 3 d printing in biomedical engineering
Medical applications of 3D printing are expanding rapidly and may revolutionize healthcare. Current uses include creating customized prosthetics and implants, anatomical models for surgery planning, and complex drug dosage forms through various printing techniques like selective laser sintering and inkjet printing. Researchers are working to develop organ printing through layer-by-layer deposition of living cells and biomaterials. While significant advances have been made, the most transformative applications like full organ printing will require more time and addressing remaining scientific and regulatory challenges.
3D healthcare
3D printing has significant potential to impact healthcare by enabling the creation of customized medical devices, implants, and tissue scaffolds. It allows for new designs that cannot be created through traditional manufacturing. The technology is also being used to print microfluidic chips, customized food, and the first approved 3D printed drug. While standards are still being developed, 3D printing promises to transform healthcare through personalized solutions, on-demand manufacturing, and open collaboration between various stakeholders in the industry and research community.
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- The final 3D model exported from Mimics in STL file format can be used for applications like surgical simulation and implant design where an accurate knee model is needed
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This document summarizes recent advances, innovations, and challenges in the field of tissue engineering and regenerative medicine. Three-dimensional printing and bioprinting technologies have shown promise in fabricating tissue scaffolds and engineered tissues, but challenges remain around printing resolution and speed. Four-dimensional bioprinting allows shape-changing structures in response to stimuli and could be useful for in vivo studies. While engineered tissues have replicated some organ functions in vitro, fully functional vascularized organs remain a long-term goal. Regulatory frameworks need revisions to adequately assess new regenerative therapies and balance innovation with safety. Overall, tissue engineering and regenerative medicine have improved medical research and applications, but continued technology development and regulatory support are needed to fully realize3dbio printing
3D bio-printing involves using 3D printers to print biological materials and living cells in a layer-by-layer process to create living tissues and organs. It aims to address the shortage of organs available for transplant by creating organs customized for each patient using their own cells to prevent rejection. While the concept is simple, bio-printing is challenging due to the need to print multiple cell types and keep the cells alive. Researchers are working to modify bio-printers to deposit cells and nurturing gels accurately according to 3D models of organs in order to eventually print transplantable tissues and organs.
3dbio printing
3D bio-printing involves using 3D printers to print biological materials and living cells in a layer-by-layer process to create living tissues and organs. It aims to address the shortage of organs available for transplant by creating organs customized for each patient using their own cells to prevent rejection. While the concept is simple, realizing it is challenging due to the need to print multiple cell types and keep the cells alive during printing. Researchers are working to modify 3D printers to accommodate biological materials and are experimenting with printing tissues like liver and skin as well as structures like human hearts and faces.
3D printing
This document provides an overview of 3D printing presented by Pratyush Shukla. It discusses what 3D printing is, general principles including modeling, printing and finishing, and various 3D printing methods such as stereolithography, fused deposition modeling, and binder jetting technology. Applications discussed include organ printing and challenges are addressed. Advantages of 3D printing include faster production, better quality, cost effectiveness and design freedom. New developments presented include printable electronics and multi-material multi-nozzle 3D printing.
Applications of Medical 3D Printing | Duane Boise
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3D_Bio_Printing seminar Slide
it is a seminar slide that i prepared on the topic 3d bioprinting. it may be a help to whom taking seminar on that topic. It is not covered its full area only the basics of bio printing ..
Estimation of 3d Visualization for Medical Machinary Images
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Estimation of 3d Visualization for Medical Machinary Images
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
BIOMEDICAL PRESENTATION FOR CAREER GROWTH.pptx
Biomedical engineering uses technology like X-rays and ECGs to help detect medical issues and improve patient care. Some recent technologies include artificial intelligence, virtual reality, 3D printing, tissue engineering, machine learning, and prosthetic devices. Prosthetic devices replace missing body parts to restore lost function and stability. Tissue engineering aims to assemble functional tissues to repair or replace damaged organs using cells and scaffolds. Virtual reality, machine learning, and 3D printing also have various healthcare applications such as medical training, diagnostics, and creating customized implants.
Stem cell therapy and organoid and 3D bioprinting
This document discusses two emerging techniques in cell therapy: organoids and 3D bioprinting. Organoids are 3D organ models derived from stem cells that mimic native tissue structures and physiology. They allow study of biological processes and have applications in transplantation, research, disease modeling, and drug testing. 3D bioprinting precisely creates living tissues and organs by combining cells, growth factors, and biomaterials in a layer-by-layer process. It has potential applications to produce tissues like skin, bone and blood vessels for transplantation and regenerative medicine. Both organoids and 3D bioprinting are establishing as critical tools in biological research with significant implications for clinical applications.
3D bioprinting
hashim salim
hashsalim@gmail.com
Whether due to illness or injury, organ failure is a worldwide problem and its only treatment is organ transplantation or tissue replacement. Although it’s the only solution in these cases, organs demand greatly surpasses the supply. Organs are usually obtained from people who recently have died (up to 24 hours past the cessation of heartbeat) or from people who are clinically brain dead and their body functions are maintained artificially, nevertheless living organ donation is becoming more frequent [1]. The increase of the organ demand has been raising ethical concerns, since this can result in offers or incentives for donation, profit on donated human organs or even exploitation of the disadvantaged. In the developed world most countries have a legal system that oversee organ transplantation, however in poorer countries a black market has been arising, enabling those who can afford to buy organs, exploiting those who are desperate enough to sell them
Bone Cancer Detection using AlexNet and VGG16
The document presents a methodology for detecting bone cancer using deep learning models AlexNet and VGG16. CT scan images are preprocessed using smoothing, averaging and Canny edge detection filters. Features are extracted from the images and classified using AlexNet and VGG16 convolutional neural networks. The results show that VGG16 achieves higher accuracy compared to AlexNet in classifying images as non-cancerous (99.995% vs 88.34%) and stage 1 cancer (99.998% vs 72.28%). Both models achieve 100% accuracy in classifying stage 2 cancer images. VGG16 performs better than AlexNet for bone cancer detection and classification.
3 d printing of pharmaceuticals by nishu
The document discusses 3D printing of pharmaceuticals. It begins with definitions of 3D printing and describes the basic 3D printing process of designing an object digitally, exporting the file, and fabricating the object through successive layers of material. Current trends and an example of a 3D printed drug tablet are mentioned. Advantages include reduced production time and costs. Applications discussed include organ and tissue engineering, medical research and education, surgical planning, and drug delivery through 3D printed devices. The future of 3D printing in India is promising with a projected growth of 20% and establishment of new facilities.
3 d bioprinting
3D bioprinting is a technique that uses 3D printing technologies to precisely position biological materials, cells, and biochemicals in layers to fabricate 3D structures and tissues. The process involves imaging tissues to create digital models, selecting appropriate biomaterials, cell sources, and a bioprinting method (inkjet, microextrusion, or laser). Applications include producing skin, blood vessels, and other tissues for implantation and drug testing. However, fully functional 3D printed organs are still in development due to challenges with vascularization and matching native tissue complexity.
Similar to 3 d printing technology in Biomaterials (20)
Applications of 3 d printing in biomedical engineering
Applications of 3 d printing in biomedical engineering
Platform for Tissue Engineering and 3D Printing at La Paz University Hospital...
Platform for Tissue Engineering and 3D Printing at La Paz University Hospital...
Advances and Innovations and Impediments in Tissue Engineering and Regenerati...
Advances and Innovations and Impediments in Tissue Engineering and Regenerati...
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- 2. RECENT ADVANCES IN 3D PRINTING OF BIOMATERIALS 3D Printing vows to deliver complex biomedical gadgets as indicated by PC configuration utilizing patient-explicit anatomical information. Since its underlying use as pre-careful representation models and tooling molds, 3D Printing has gradually developed to make stand-out gadgets, inserts, frameworks for tissue designing, symptomatic stages, and medication conveyance frameworks.
- 3. RECENT ADVANCES IN 3D PRINTING OF BIOMATERIALS Powered by the new blast openly interest and admittance to reasonable printers, there is restored interest to join undifferentiated cells with custom 3D frameworks for customized regenerative medication. Before 3D Printing can be utilized regularly for the recovery of complex tissues (for example bone, ligament, muscles, vessels, nerves in the craniomaxillofacial complex), and complex organs with many-sided 3D microarchitecture (for example liver, lymphoid organs), a few mechanical limits should be tended to.
- 4. RECENT ADVANCES IN 3D PRINTING OF BIOMATERIALS In this audit, the significant materials and innovation propels inside the most recent five years for every one of the basic 3D Printing advancements (Three Dimensional Printing, Fused Deposition Modeling, Selective Laser Sintering, Stereolithography, and 3D Plotting/Direct-Write/Bioprinting) are portrayed. For more detail click the link below to get more knowledge. https://zshort.io/3dprinting