If you are confused with the terms “alpha and beta prototypes”, do not feel bad. Many of you are on that boat. Also, these terms have different meanings for different industries (e.g. software vs hardware). Since my field is medical device design, I am talking about hardware prototypes here.

The product development process takes place in stages. First, you have an idea. You then develop that idea into a concept (or many different concepts) of how it might be as a product.

Once you get the feeling that you think it will work, you set about turning it into a working model. Your work model may be an intermediate stage to prove the feasibility of your concept (you have to build many to make Kinks work), or it could be your actual feasibility model. A possible prototype is intended to prove that you can turn your concept into something that works normally as you intended. It gives you confidence that your idea is sound and should be able to be made using technology and readily available components (another question is whether your idea is commercially viable).

Achieving a feasible design is your first major milestone. The next milestone is the alpha prototype. Product development between feasibility and the alpha prototype involves much of the effort. You look for standard parts that you can use in your design and design custom parts if you can not store them. Most problem solving, testing and repetition takes place here. The goal is to build a design / prototype of your product that will use the components of the product that you will eventually sell to consumers. This is an alpha prototype.

The important thing to understand is that Alpha is still an estimate of how your product will look. Now you can improve the design with the aim of reaching the final look that you make and sell. The alpha prototype is close to you, but changes and improvements still need to be made. You will probably find suppliers who can provide an equivalent of what you used for your alpha prototype but with better volume reduction for commercial sizes. You want to include those parts in the design and make sure that the new parts do not introduce unexpected consequences. You will need to make touches to specific areas of the design. You can find parts that have better performance features than you can find for Alpha Build.

A beta prototype will be built incorporating final changes and improvements. Beta is as close as possible to commercial production. Why is it not a commercial product? This is because it is not built using actual manufacturing methods and tools required for commercial volume production. In most cases, beta prototypes are built by hand one by one. They are close to the commercial product that can be put through the final test. If the beta prototypes work reliably beyond the rest of the test requirements, you are ready to transfer your design to manufacturing. Five paragraphs ago the dream you started came true.

Proving the feasibility of medical devices is the first important milestone in bringing medical device to market.

Much of the design and development of the device will not take place until feasibility is proven. Read on to understand what feasibility means and what needs to happen before you can prove feasibility. Promotes the feasibility of medical devices

The development of medical devices begins with taking an idea for a new or improved device and turning it into a basic design concept. Once you have a concept, you develop it to a level that allows you to create a working model. You get used to testing that model and finding out if it does what you expect it to do. That model – your Proof of Concept – does not have to prove that you have a path to commercial production. You set it up with a possible pattern.

The purpose of a feasibility prototype is to determine if you can build a vision device using existing components and technologies, or if you need to make some customization and / or innovation. It gives an indication of how big the development effort is and how much resources you need to commit to. Developing a prototype that makes it possible to develop a proof-of-concept model (depending on the complexity of the product, it is sometimes more economical and efficient to abandon proof-of-concept activities in favor of going straight into the possibility). Your concept will inform you of a possible prototype if it is technically possible and can be produced in a financially understandable way.

The feasible design is similar to the work model you have built to prove your concept. While the Proof-of-Concept is a very simple device, designed to prove the principles, a feasible prototype is the Proof-of-Concept model, which reveals what the final commercialized product will look like. In addition to demonstrating the main functionality of the device, the feasibility prototype also includes user interface elements. You can think of it as a product in its first, most rigorous incarnation.

One of the important parts of the project is the feasible phase. In order for FDA to clear the device so that you can market it, the design of the device must be done in the prescribed manner and fully documented. Know that it is design control. The thing about feasibility is that you are free to experiment and try different approaches to achieve your design goals without the burden of documenting each step. Once you have proven the feasibility and turned to actual design and development, you must have a design control system and adhere to its guidelines. This is actually a good thing because as much as possible – the devices are designed in accordance with the principles of sound engineering, and any potential for device failure and harm to users or patients is fully considered.

Design control adds a significant administrative burden and cost, however, you do not need to implement it until you are sure that your device can function to a minimum (commercial feasibility is another question, and the prototype should be developed to prove that the device can be manufactured at a cost that supports the business model).

Proving the feasibility of medical devices dictates the stage for subsequent detailed development. This is the first important milestone in your journey, and it is often crucial to open fund boxes.

For decades, silicone has been used for both short- and long-term implantable applications, due to its high level of biocompatibility. As the patient begins to realize the potential of long-term silicone-based drug-eluting implants to improve outcomes, today, even more exceptional permeability is crucial for next-generation combination products.

However, these combination products are very challenging to design, manufacture and shepherd through the control process. In this article, we will examine the potential pitfalls and develop a strategy to ensure a successful product launch.

Growth of combination products

Long-lasting implantable combination products (which last longer than 29 days in the body) developed with one or more active pharmaceutical ingredients (APIs) are currently being used to improve lives. For example, some such products may eliminate the need for daily administration of contraceptives or APIs to prevent retroviral infections. Since silicone-based intraturine combination products have more than one API, the combination product can provide the exact dosage and hormone of antiviral drugs simultaneously.

Cancer-tumor reduction is another area of ​​immense promise for long-term drug-eluting combination products that can be implanted. Small combination products that can deliver the exact amount of cytostatics used in chemotherapy can be placed exactly where needed, significantly reducing the cost of care while significantly reducing side effects.

Therefore, iHealthcareAnalyst Inc. predicts that by 2025 the global implantable combination product market will reach $ 30 billion at a CAGR of 5.8%. No surprise though. An important driver is the incidence of chronic diseases; Others are the growing popularity of minimal invasive surgeries and wearable combination products.

Better patient results can ultimately be a great driver, while combination products can simultaneously increase treatment efficiency and reduce costs — all important health care is a sweet spot. Furthermore, the combination products allow pharmaceutical companies to use FDA-approved formulations in novel ways, to expand drug use and to extend patient life.

Go-to-market challenges

The design, manufacturing and approval processes for combination products can be very challenging but very rewarding when successful. Some of the great challenges faced by product manufacturers are related to resolving the technical and scalability issues associated with their manufacture before the start of clinical trials.

To address these issues, it is important to understand the relationships and interactions between drug, device, silicone and manufacturing processes among all parties involved in terms of technical feasibility, regulatory compliance and functionality and effectiveness in treatment. These variables affect each other. The design and manufacturing process It is very important to find the right balance before freezing. Silicon extraction, molding and other manufacturing methods use heat to cure silicon. If the API is heat sensitive, silicone selection and curing method are critical factors. Newer, lower-temperature curing silicones or alternative curing technologies may provide better solutions, e.g.

Substantial skill is required to create the desired release rate for a specific solution of the specified silicon type. The silicon matrix has a polymer structure. During the development process, the silicon matrix matched the molecular structure of the API, ensuring optimal illusion over the desired time frame. Success requires expertise on how Silicon and API interact.

Finally, scale-up is often overlooked for the overall success of a product. During the development phase, a small scale possible process can lead to inefficient or prohibitively expensive full-scale manufacturing. Understanding both lab scale development and commercial production to a very large extent is important for partner market potential.

In general partnership, the combination product owner will focus on development drug development, clinical strategy and coordination, control submissions, features and design control. The combination of development and production partner focuses on the effective preparation of production and planning for scalability from bench scale to commercial production, including certification in line with the planned control route for production.

4 Important Questions

Here are some questions to ask yourself when choosing manufacturing and supply chain partners for your combination product development:

What experience do you have with the government regulatory review process for combination products? Combination product creators who are unfamiliar with the dual control route of combination products can benefit from this experience by being able to select a manufacturer and assist in developing the project plan. Underestimating the time required for product development and testing (e.g., illusion rate study), conducting a series of clinical trials, and guiding combination production through multiple control reviews easily and quickly derailed such a project.

Can you recommend the best process for the drug drug-dispensing system we are designing? First, an experienced production partner must have extensive processing experience to recommend the optimal production process for a combination product. Second, the partner must have experience in joint engineering and have a DfM (Design for Manufacturing) mentality. Third, the partner must be able to contribute to early development decisions, provide the necessary design and processing input related to the compatibility, form and performance of the combination product so that scalability and commercialization are fully considered.

What is the range of knowledge of your materials? Look for a manufacturer with strong base development and processing experience and long-term relationships with material manufacturers on a global basis. Understanding the processing properties of materials and how they can be affected and controlled is crucial to the successful validation and launch of the final product. Technical cooperation between components and materials manufacturers simplifies the process of adapting existing materials or developing new ones as needed.What is the full range of your abilities? Depending on the specific needs of your project, it may help to choose a productive partner who is capable of providing a range of services in addition to the product of the combination. To ensure a thin supply chain, it can be beneficial to choose a manufacturing partner that can deliver a fully integrated product including all aspects of post-manufacturing processes, packaging, labeling and steri

Sample Preparation in the design and testing of a medical device serves as an investigative process to evaluate quality measures and measurements and performance. Safety and quality are key factors in the medical device manufacturing industry. Strict legal requirements exist at all stages of the device life cycle, including configuration and testing. OEMs (Original Equipment Manufacturer) and companies that can design and test medical devices or components play an important role in the medical device industry. However, it is important when looking for companies that can design and test medical devices or components that reflect their quality management practices and practices in all aspects of product life.

Design validation and validation

All manufacturers of any Phase II and III medical devices, as well as most Phase I devices, must, by law, establish and maintain device-design procedures to ensure that the designated design requirements are met. Manufacturers are required to develop and maintain such procedures to ensure that those design requirements per device are not only appropriate but also to address the intended use of the device, that is, to meet the needs of each user and patient. Procedures must include mechanisms to resolve any incomplete, complex, or regulatory issues. Procedures should also allow for adequate design compliance testing and reference acceptance process that is essential for device performance. property assurance procedures and assurance property must also be met to ensure that the device will operate as described and that the device complies with the needs of the user and its intended use.

Testing and Testing Again

All medical devices must be tested for their final product status and condition before being marketed. This is done not only to ensure the quality and safety of the product but also to ensure the success of the product in competitive markets. This includes testing and testing of parts or sub-components of the device.

To ensure safety, medical device testing should go beyond design by testing equipment used in telephone production. Test objectives should ensure the safety of each device’s device first, but, in addition to that, make sure that the steps used in manufacturing the device did not endanger the usability of the device. The factors to be considered are all the necessary materials and sterilizers that make up the production process. There are many cosmetics, additives, fungicides, processing tools, cleaning products, and the like that could endanger the device or interact with it in contact with it. When looking for a company to produce your product make sure that the sampling and testing processes take into account these factors.

Preparing a Sample Final Product

The sample preparation of your design should be tested on the final product form. That way, the test can be designed to include all that is involved in making the phone. Building materials must be safe for patients and the device must comply with the procedures and materials used in the manufacturing process. In the sample preparation of multiple devices, the test material should be prepared to mimic the clinical conditions expected to determine the possible effects of chemical exposure.

In short, preparing a sample of medical devices is one of the most important steps in the control process to successfully bring your device to market. Device or component design and testing continue to establish performance parameters to ensure product performance as it is designed and that the end result is the production of high-quality medical devices.

Data quality is an important factor for the success or failure of a machine learning system; In fact, data quality is more important than machine learning algorithm. There are two main factors that affect data quality: dataset and model. The dataset is sent to the model to learn. Machine learning is not possible outside of this dataset and size and variation define how easily a model can learn from it. Therefore data scientists play an important role in terms of algorithm scaling.

AI may fail (unreliable) because the data is not representative or it is not suitable for the task at hand. Therefore, it is important to ensure that the data quality needed to make Medical AI more reliable and that the algorithms are strong enough and adequate for the purpose. In short, determining the security and effectiveness of AI is based on verification of data quality and verification of its compatibility with the algorithm model. Furthermore, as AI has the potential to change over time, verification and validation processes should not be used as a one-time premarket activity, but rather throughout the life cycle of the system, from initial design and clinical compliance to its post-market use until decommissioning. Continuous assurance of the safety of the AI-based device and its life cycle performance helps regulators, physicians and patients gain confidence in machine learning AI.

There are many factors that contribute to data quality, including the completeness, accuracy and accuracy of the data; Quote; Bias; And consistency in data labeling (e.g., different labels may mean the same thing but the algorithm treats them differently).

Dataset citations contain variables and biases that apply to humans so that the AI ​​solution can detect it.

Any bias in the dataset will affect the performance of the machine learning system. There are many resources, including population, frequency frequency and instrumental bias.

Having a system that is inadvertently biased on one subset of the patient population results in poor model performance when confronted with another subset and eventually leads to health care inequalities. When working with quality data, there may be instances of intentional bias (also known as positive bias), such as a dataset designed for people over the age of 70 to view age-related health issues.

When considering the application of a dataset for a machine learning application, it is important to understand the claims it makes. It depends on whether the data can be reproduced, and whether any citations are reliable, or whether a proper balance has been achieved in the representative population classes. For example, a dataset may contain chest X-rays from men aged 18-30 in a particular country, half of whom have pneumonia. This dataset cannot be said to indicate pneumonia in females. Since this subgroup may not be listed in the dataset variables and may not be explicitly represented in the sample size, it cannot be said that it represents young males in a particular ethnic group.

Trained in AI model dataset. It learns trained variables and annotations on the dataset. In healthcare, most neural networks are trained in the dataset, evaluated for accuracy and then used for inference (e.g., by implementing the model on new images).

It is important to understand what the model can reliably identify (e.g., model arguments). Neural networks can generalize a bit, allowing them to learn things that are slightly different from their training dataset. For example, a carefully trained model on male chest X-rays may work well on the female population or even with different X-ray devices. The only way to verify this is to display the trained model with the new test dataset. Depending on the model’s performance, AI can accurately diagnose pneumonia in both male and female patients and demonstrate that it can be normalized on various X-ray machines. There may be minor differences in performance between datasets, but they may still be much more accurate than human.

In summary, AI learns the variables, bias, and annotations of a dataset, with the assumption that it can identify an important feature. After training, an algorithm is tested, which indicates that this feature can be detected with a certain level of accuracy. To test a claim that AI can detect a specific item, it needs to be tested in a dataset that specifies this feature as fair. If it performs satisfactorily on this dataset, the model can claim to be able to detect this feature in future datasets that share the same variable as the test dataset.

The following example shows an example of a poor quality dataset and its incorrect relationship with the algorithm model causing failure in the output.

A custom learning taxonomy [1] analyzed photographs to distinguish between wolves and huskies. Instead of identifying the distinctive features between the two dog breeds, the system found that photos of huskies had snow in the background, but no photos of wolves. The system’s conclusions are not relevant to its training data but are used in real-world scenarios because external and inappropriate variables (i.e., backgrounds) are included in the learning dataset. [2] This is an example of how the AI ​​system in a dataset can detect random patterns or correlations and assign a false or inconsistent causal or meaningful relationship.

As we make new advances in AI technology, regulators may consider multiple approaches to address the safety and impact of AI in health care, as well as how international standards and other best practices are currently being used to support medical software regulation, as well as differences and gaps that need to be addressed for AI solutions. AI needs to generate real-world clinical evidence throughout its life cycle and has the potential for additional clinical evidence to support adaptive systems.

Over the past ten years, regulatory guidance and international standards for software have emerged where they are included as an independent medical device or physical device. It provided requirements and guidelines for software manufacturers to show that they comply with medical device regulations and to place their products on the market.

However, AI introduces a new risk which has not been addressed under current standards portfolio and software guidance. Various approaches are required to ensure the security and performance of AI solutions placed on the market. As these new policies are being defined, the current control landscape for software should be considered a good starting point.

In Europe, there are several general requirements that apply to software such as Medical Device Regulation (MDR) and In vitro diagnostic regulation (IVDR). These include:

General responsibilities of manufacturers such as risk management, clinical performance evaluation, quality management, technical documentation, specialized device identification, postmarket surveillance and corrective measures;

Equipment design, environment interaction, analysis and measuring functions, design and manufacturing requirements including active and connected equipment; And

Information provided with the device, such as labeling and instructions for use.

In addition, EU regulations have specific requirements for software. These include the need for electronic programmable systems and the prevention of negative interactions between the software and the IT environment.

U.S. In, the FDA recently published a discussion paper on the proposed regulatory framework for amendments to the AI ​​/ machine learning-based SaMD. It is based on the practices of current FDA premarket programs, including the 510 (k), de novo, and premarket approval (PMA) routes. It uses the FDA Benefit-Risk Framework, Risk Management Principles in Software Modification Guidelines, and the Total Product Life Cycle (TPLC) approach from the FDA Digital Health Pre-Sert Program, in addition to the risk classification principles from IMDRF.

Elsewhere, other countries have begun developing and publishing regulatory guidelines. In China, the National Medical Products Administration (NMPA)  has developed a guideline for assistive decision-making medical device software using in-depth learning methods. Japanese and South Korean regulatory bodies have also published guidelines for AI in health care.

For 1 million global new amputees per year (which is one amputation every 30 seconds), the loss of an organ means they must adapt to the new world. Take, for example, India, one of the most populous countries in the world. Only 3% of its buildings are suitable for access, while the country counts over half a million amputees. As part of this new adaptation process, amputees suffer further injuries. Experts estimate that amputees are 200 times less likely than healthy individuals; And seek comparable medical assistance with institutionalized seniors.

With an aging population, trauma leading to an increase in vascular disease and dissection, those numbers are likely to increase worldwide. Some countries, such as the US, have almost doubled the number of their amputees by 2050.

Advances in prosthetics can help improve that world for amputees, even if the world around them does not necessarily improve. And these developments are happening fast. Who better to ask than prosthetic wearers who develop robotic prostheses?

Egyptian mummies to cyberpunk brain-controlled prosthetics

Back in 2011, archaeologists discovered the oldest prosthetic device; Wooden toe buried with Egyptian mummies 3,000 years ago. Aesthetically it packs a steampunk look, while this artificial toe is far from being a cosmetic item. After examining the replicas, the researchers found that they were actually practical devices that could help with gait. Although materials have changed over the millennium, prosthetics have only evolved in recent decades with the advent of robotic prostheses.

In a Seminal 2008 paper, the researchers described how monkeys were able to control a mechanical hand with their brain activity. Controlling the prosthetic limb by means of electrodes implanted in their brains, which also allowed them to feed the fetus themselves; It was the first with brain-controlled prostheses. The developments in subsequent years since 2011 have been as close to human capacity as the modular prosthetic limb; And by 2020, patients with mind-controlled arm prostheses will be able to experience palpation thanks to the new implant system.

With developments like that, the future of this technology seemed to depend on battery life and robotic progress. The narrative has shifted to focus on how much artificial intelligence (A.I.) we can develop for prostheses.

If you think the field of prosthetics with cyberpunk brain controls got all the science fiction, hold on because there is more. Enter the age of the smart prosthesis as a pair of engineers A.I. With prostheses.

The age of smart prosthetics

The basis for including A.I. In robotic prostheses, the algorithm describes the nerve signals from the patient’s muscles, allowing the prosthesis to be more precisely controlled. All externally powered (motor-driven) upper limb prostheses have become a cornerstone of R&D, but are difficult to apply to lower limb prostheses. However, a study published in March 2020 by the University of Michigan team in Science Translational Medicine records a new method for incorporating technology into more types of prostheses.

Their new technology, based on the regenerative peripheral nerve interface (RPNI), surgeons use a small muscle and wrap it around the end of a dissected nerve to produce stretched signals. The team of mathematicians apply machine learning algorithms to convert the signals into fine movements in the prosthetic.

3D Printing Medical Devices has been the talk of the town for the past few years, with the cost of printers plummeting and becoming a consumer good. Aside from its use for prototyping components, 3D printing medical devices are new. Recently, however, there have been some developments with technology that make it a viable process for mass production of components. Several weeks ago, I posted about the Franhofer Institute’s ability to print multiple plastics, ceramics, glass and metals in a single piece.

An article in the MIT Technology Review highlights High Packard’s new jet fusion technology for high-speed printing of high-strength plastic components. Printing speed and part strength are the main limiting factors with 3D printing of product-quality components. If HP technology is really competing with the speed and part performance characteristics of injection molding, it is the perfect game-changer. 3D printing medical devices allow the construction of parts that have very complex geometry, parts that cannot be produced economically by any traditional method. With injection molding, good part design prevents the presence of undercuts on the part of the practice. With 3D printing medical devices, it is no longer a barrier. Not having to worry about undercuts on the part means that designers are free to create the geometry of any interior parts they can imagine – capillary structures like veins and other anatomical geometries are all possible. The parts that are impossible to achieve can be provided from here to the front. The real advantage of 3D printing medical devices is the same, and if HP technology reports as quickly and consistently as possible, it will make significant progress that will revolutionize the design of medical devices