Machine Learning for 3D Printed Multi-Materials Tissue-Mimicking Anatomical Models

Urology is one of the fields that are always at the frontline of bringing scientific advancements into clinical practice, including 3D printing (3DP). This study aims to discuss and presents the current role of 3D-printed phantoms and devices for organ-specified applications in urology. The discussion started with a literature search regarding the two mentionedtopics within PubMed, Embase, Scopus, and EBSCOhost databases. 3D-printed urological organ phantoms are reported for providing residents new insight regarding anatomical characteristics of organs, either normal or diseased, in a tangible manner. Furthermore, 3D-printed organ phantoms also helped urologists to prepare a pre-surgical planning strategy with detailed anatomical models of the diseased organs. In some centers, 3DP technology also contributed to developing specified devicesfor disease management. To date, urologists have been benefitted by 3D-printed phantoms and devices in the education and disease management of organs of in the genitourinary system, including kidney, bladder, prostate, ureter, urethra, penis, and adrenal. It is safe to say that 3DP technology can bring remarkable changes to daily urological practices.

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Removing the Constraints of Disability

Advances in Human and Social Aspects of Technology - Technological Breakthroughs and Future Business Opportunities in Education, Health, and Outer Space ◽

10.4018/978-1-7998-6772-2.ch013 ◽

2021 ◽

pp. 205-219

Author(s):

Wadad Kathy Tannous ◽

Laney McGrew

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Neural Networks ◽

Assistive Technologies ◽

Vision Impairment ◽

Cultural Participation ◽

Smart Glasses ◽

Control Computer ◽

3D Printed ◽

Over Time

One billion people globally live with disabilities that are physical, sensory, psychiatric, neurological, cognitive, or intellectual. Their disabilities are dynamic and can be temporary or permanent, singular or plural, from birth or developed, and can change over time. People with disabilities face barriers to economic, social, political, and cultural participation. Assistive technology, artificial intelligence, and broader technology can amplify their inclusion, participation, and independence. This chapter will highlight emerging and evolving technologies, rooted in machine learning and neural networks, which assist across different disabilities and seek to improve the user's sense of ability and independence. These include Seeing AI app, OXSIGHT, OrCam, Envision smart glasses, and Dot Watch for vision impairment; Ava app and cognitive hearing aid for hearing impairment; Liftware self-stabilising utensils for limited hand mobility; Eyegaze and Tobii – assistive technologies that allow users to control computer and smartphone screens with their eyes; and 3D printed prosthetics.

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Virtual Prototyping: Computational Device Placements within Detailed Human Heart Models

Applied Sciences ◽

10.3390/app10010175 ◽

2019 ◽

Vol 10 (1) ◽

pp. 175

Author(s):

Alex J. Deakyne ◽

Tinen L. Iles ◽

Alexander R. Mattson ◽

Paul A. Iaizzo

Keyword(s):

Anatomical Study ◽

3D Models ◽

Anatomical Models ◽

Cardiac Tissues ◽

Patient Demographics ◽

Wide Range ◽

Specific Device ◽

Mri Scans ◽

3D Printed ◽

Magnetic Resonance Imaging Mri

Data relative to anatomical measurements, spatial relationships, and device–tissue interaction are invaluable to medical device designers. However, obtaining these datasets from a wide range of anatomical specimens can be difficult and time consuming, forcing designers to make decisions on the requisite shapes and sizes of a device from a restricted number of specimens. The Visible Heart® Laboratories have a unique library of over 500 perfusion-fixed human cardiac specimens from organ donors whose hearts (and or lungs) were not deemed viable for transplantation. These hearts encompass a wide variety of pathologies, patient demographics, surgical repairs, and/or interventional procedures. Further, these specimens are an important resource for anatomical study, and their utility may be augmented via generation of 3D computational anatomical models, i.e., from obtained post-fixation magnetic resonance imaging (MRI) scans. In order to optimize device designs and procedural developments, computer generated models of medical devices and delivery tools can be computationally positioned within any of the generated anatomical models. The resulting co-registered 3D models can be 3D printed and analyzed to better understand relative interfaces between a specific device and cardiac tissues within a large number of diverse cardiac specimens that would be otherwise unattainable.

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Machine-Learning-Based Muscle Control of a 3D-Printed Bionic Arm

Sensors ◽

10.3390/s20113144 ◽

2020 ◽

Vol 20 (11) ◽

pp. 3144 ◽

Cited By ~ 1

Author(s):

Sherif Said ◽

Ilyes Boulkaibet ◽

Murtaza Sheikh ◽

Abdullah S. Karar ◽

Samer Alkork ◽

...

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Support Vector Machine ◽

Experimental Test ◽

Support Vector Machine Classifier ◽

Low Cost ◽

Support Vector ◽

Wide Range ◽

3D Printed ◽

Semg Signals

In this paper, a customizable wearable 3D-printed bionic arm is designed, fabricated, and optimized for a right arm amputee. An experimental test has been conducted for the user, where control of the artificial bionic hand is accomplished successfully using surface electromyography (sEMG) signals acquired by a multi-channel wearable armband. The 3D-printed bionic arm was designed for the low cost of 295 USD, and was lightweight at 428 g. To facilitate a generic control of the bionic arm, sEMG data were collected for a set of gestures (fist, spread fingers, wave-in, wave-out) from a wide range of participants. The collected data were processed and features related to the gestures were extracted for the purpose of training a classifier. In this study, several classifiers based on neural networks, support vector machine, and decision trees were constructed, trained, and statistically compared. The support vector machine classifier was found to exhibit an 89.93% success rate. Real-time testing of the bionic arm with the optimum classifier is demonstrated.

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Custom Patient-Specific Three-Dimensional Printed Mitral Valve Models for Pre-Operative Patient Education Enhance Patient Satisfaction and Understanding

Journal of Medical Devices ◽

10.1115/1.4043737 ◽

2019 ◽

Vol 13 (3) ◽

Author(s):

Kay S. Hung ◽

Michael J. Paulsen ◽

Hanjay Wang ◽

Camille Hironaka ◽

Y. Joseph Woo

Keyword(s):

Patient Education ◽

Mitral Valve ◽

Three Dimensional ◽

3D Models ◽

Patient Specific ◽

Imaging Data ◽

Anatomical Models ◽

Mitral Valves ◽

Flexible Polymer ◽

3D Printed

In recent years, advances in medical imaging and three-dimensional (3D) additive manufacturing techniques have increased the use of 3D-printed anatomical models for surgical planning, device design and testing, customization of prostheses, and medical education. Using 3D-printing technology, we generated patient-specific models of mitral valves from their pre-operative cardiac imaging data and utilized these custom models to educate patients about their anatomy, disease, and treatment. Clinical 3D transthoracic and transesophageal echocardiography images were acquired from patients referred for mitral valve repair surgery and segmented using 3D modeling software. Patient-specific mitral valves were 3D-printed using a flexible polymer material to mimic the precise geometry and tissue texture of the relevant anatomy. 3D models were presented to patients at their pre-operative clinic visit and patient education was performed using either the 3D model or the standard anatomic illustrations. Afterward, patients completed questionnaires assessing knowledge and satisfaction. Responses were calculated based on a 1–5 Likert scale and analyzed using a nonparametric Mann–Whitney test. Twelve patients were presented with a patient-specific 3D-printed mitral valve model in addition to standard education materials and twelve patients were presented with only standard educational materials. The mean survey scores were 64.2 (±1.7) and 60.1 (±5.9), respectively (p = 0.008). The use of patient-specific anatomical models positively impacts patient education and satisfaction, and is a feasible method to open new opportunities in precision medicine.

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Geometric Accuracy Prediction for Additive Manufacturing Through Machine Learning of Triangular Mesh Data

Volume 1: Additive Manufacturing; Manufacturing Equipment and Systems; Bio and Sustainable Manufacturing ◽

10.1115/msec2019-3050 ◽

2019 ◽

Cited By ~ 4

Author(s):

Nathan Decker ◽

Qiang Huang

Keyword(s):

Machine Learning ◽

Additive Manufacturing ◽

Three Dimensional ◽

Triangular Mesh ◽

Geometric Accuracy ◽

New Approach ◽

Machine Learning Model ◽

3D Printed ◽

3D Shapes ◽

Complex Parts

Abstract While additive manufacturing has seen tremendous growth in recent years, a number of challenges remain, including the presence of substantial geometric differences between a three dimensional (3D) printed part, and the shape that was intended. There are a number of approaches for addressing this issue, including statistical models that seek to account for errors caused by the geometry of the object being printed. Currently, these models are largely unable to account for errors generated in freeform 3D shapes. This paper proposes a new approach using machine learning with a set of predictors based on the geometric properties of the triangular mesh file used for printing. A direct advantage of this method is the simplicity with which it can describe important properties of a 3D shape and allow for predictive modeling of dimensional inaccuracies for complex parts. To evaluate the efficacy of this approach, a sample dataset of 3D printed objects and their corresponding deviations was generated. This dataset was used to train a random forest machine learning model and generate predictions of deviation for a new object. These predicted deviations were found to compare favorably to the actual deviations, demonstrating the potential of this approach for applications in error prediction and compensation.

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Machine learning generative models for automatic design of multi-material 3D printed composite solids

Extreme Mechanics Letters ◽

10.1016/j.eml.2020.100992 ◽

2020 ◽

Vol 41 ◽

pp. 100992

Author(s):

Tianju Xue ◽

Thomas J. Wallin ◽

Yigit Menguc ◽

Sigrid Adriaenssens ◽

Maurizio Chiaramonte

Keyword(s):

Machine Learning ◽

Generative Models ◽

Automatic Design ◽

3D Printed

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From medical imaging data to 3D printed anatomical models

PLoS ONE ◽

10.1371/journal.pone.0178540 ◽

2017 ◽

Vol 12 (5) ◽

pp. e0178540 ◽

Cited By ~ 79

Author(s):

Thore M. Bücking ◽

Emma R. Hill ◽

James L. Robertson ◽

Efthymios Maneas ◽

Andrew A. Plumb ◽

...

Keyword(s):

Medical Imaging ◽

Imaging Data ◽

Anatomical Models ◽

3D Printed ◽

Medical Imaging Data

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Beneath the Skin: Emulating human physiology using a novel bitmap-based “voxel” 3D-printing workflow.

10.26686/wgtn.17151335 ◽

2021 ◽

Author(s):

◽

Ana Morris

Keyword(s):

Medical Education ◽

3D Printing ◽

Biomedical Imaging ◽

Human Anatomy ◽

Patient Specific ◽

Imaging Data ◽

Anatomical Models ◽

Medical Communication ◽

Medical Models ◽

3D Printed

<p>Novel technologies that produce medical models which are synthetic equivalents to human tissue may forever change the way human anatomy and medicine are explored. Medical modelling using a bitmap-based additive manufacturing workflow offers exciting opportunities for medical education, informed consent practices, skills acquisition, pre-operative planning and surgical simulation. Moving medical data from the 2D-world to tactile, highly detailed 3D-printed anatomical models may significantly change how we comprehend the body; revamping everything – from medical education to clinical practice. Research Problem The existing workflow for producing patient-specific anatomical models from biomedical imaging data involves image thresholding and iso-surface extraction techniques that result in surface meshes (also known as objects or parts). This process restricts shape specification to one colour and density, limiting material blending and resulting in anatomically inequivalent medical models. So, how can the use of 3D-printing go beyond static anatomical replication? Imagine pulling back the layers of tissue to reveal the complexity of a procedure, allowing a family to understand and discuss their diagnosis. Overcoming the disadvantages of static medical models could be a breakthrough in the areas of medical communication and simulation. Currently, patient specific models are either rigid or mesh-based and, therefore, are not equivalents of physiology. Research Aim The aim of this research is to create tangible and visually compelling patient-specific prototypes of human anatomy, offering an insight into the capabilities of new bitmap-based 3D-printing technology. It proposes that full colour, multi-property, voxel-based 3D-printing can emulate physiology, creating a new format of visual and physical medical communication. Data Collection and Procedure For this study, biomedical imaging data was converted into multi-property 3D-printed synthetic anatomy by bypassing the conversion steps of traditional segmentation. Bitmap-based 3D-printing allows for the precise control over every 14-micron material droplet or “voxel”. Control over each voxel involves a process of sending bitmap images to a high-resolution and multi-property 3D-printer. Bitmap-based 3D-printed synthetic medical models – which mimicked the colour and density of human anatomy – were successfully produced. Findings This research presented a novel and streamlined bitmap-based medical modelling workflow with the potential to save manufacturing time and labour cost. Moreover, this workflow produced highly accurate models with graduated densities, translucency, colour and flexion – overcoming complexities that arise due to our body’s opaqueness. The presented workflow may serve as an incentive for others to investigate bitmap-based 3D-printing workflows for different manufacturing applications.</p>

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