What if a denture fabrication product could offer many clinical benefits without any change in clinical time or technique, such as these below? (Figure 1)

Duplicates and maintains border roll clinically established by either functional or compound bordering of the impression.

Produces a uniform thickness of the base that follows the palatal contours in the final prosthetic.

Aids in communicating and evaluating expected fit to the patient at the beginning of treatment rather than at the delivery of the dentures.

Illustrates outcome and provides an avenue to offer treatment options prior to patient disappointment.

Provides a final fit for baseplate records. Fewer missed registrations due to looseness of base as undercuts are maintained in baseplate.

Fewer postdelivery adjustments as comfort issues are addressed at baseplate and try-in stage.

Any adjustments made are final as the base becomes part of the completed denture. 

In order for us to justify implementing a technique or product to offer, we must establish the benefits for ourselves and our dental professional clients.

In 1988, I was introduced to a technique and product offering that eliminated several issues concerning the records and try-in appointments associated with removable prosthetics/dentures, which added no extra chair time.

Vacuum forming and the implementation of microwave-cured resins such as NATURE-CRYL® MC from GC America allowed me to refine the original technique in laboratory labor manufacturing time, uniformity of base thickness, and speed of cure to accommodate daily use and recommendation.

 Box and pour a definitive impression using WonderFormer from Dental Creations and Coecal™ Type III Dental Stone from GC America 

 After the stone sets, remove it from the former and trim the model to provide a land area and minimal model thickness that does not intrude or destroy the vestibular area.

 Next, vacuum form a .080 sheet of tray/baseplate polystyrene from Keystone Industries over model. Postforming thickness will present itself between 1.5 mm and 1.8 mm, which offers an ideal completed base thickness and rigidity in acrylic. The benefit of vacuum forming rather than waxing is a uniformity of base that follows the contour of the palate 

 Trim the forming material to the land area leaving the vestibular molded area intact.

 Invest the model in the lower half of a microwave flask, covering all but the polystyrene. Once hard, paint the land area of the investment with COE-SEP™ tinfoil substitute from GC America and invest the upper half of the flask 

 Once the investment has set, remove the bolts and place the flask vertically in a dedicated microwave oven and irradiate for 1 minute at 500 watts. This creates steam that should release the flask halves 

. Then remove the polystyrene from the model. If it is not soft enough to remove without damaging the model, flame brush the polystyrene with a blazer torch or similar and then peel off.

 Coat both halves of the flask with COE-SEP or similar. Then, dry with warm air to speed up the process.

 Mix NATURE-CRYL MC® from GC America to the correct powder to liquid ratio and allow it to reach recommended pack state (dough-like, not sticky or snappy). To avoid waste, I suggest halving the amounts recommended for a standard sized complete denture.

 Trial pack the mold, trim the flash, and if possible, tighten the bolts while the flask is under a pressure of 1500 psi 

. This maintains final closure pressure without release. The best compress for the microwave technique is a style that has a plunger press cap. This allows for cinching the nuts unobstructed.

The Completed Base

 Next, cure vertically for 3 minutes at 500 watts, remove it from the oven, and allow it to bench cool for 25 minutes. Submerge it in lukewarm water and then let it cool for an additional 20 minutes.

 Deflask, polish and finish the base acrylic 

 Adapt a wax rim following professional standard dimensions. Sticky wax works nicely to fasten wax against the acrylic 

 Construct the mounting model by blocking out the major undercuts with PVS putty and pouring a stone base. This allows for articulation and easy repeated placement and removal of base from the articulator 

 The base and rim are sent back for a records appointment and subsequent tooth try-ins 

A technique for waxing and processing on a completed base will be covered in my next article.

Articles

Vacuum forming and microwave-cured resin allowed one lab to refine the denture fabrication process without additional chair time.

How to Achieve the Completed Denture Base

Dental labs and milling centers providing digital fabrication of dentures.

While offering an integrated material system with digital denture tooth and bonding solutions, as well as try-in/base materials for process-reliable fabrication, these denture teeth can always be used during highly efficient fabrication of full dentures with digital fabrication systems (milling and 3D-printing technology).

Described as the denture tooth for high productivity and a natural appearance, VITA VIONIC VIGO is a completely new generation of teeth made of SE polymer with a design perfected for the digital fabrication of dentures. This new generation can be fixed precisely and reliably in the denture base without any reworking, enabling an efficient completion; used for highly efficient digital fabrication of full dentures with all common open CAM and 3D-printing systems.

The VITA VIONIC Solutions portfolio includes 8 upper anterior tooth molds, 4 lower anterior tooth molds, 4 upper and lower posterior tooth molds each, 1 bonding system, and material blanks for wax try-ins and plastic bases. It allows for the highly efficient fabrication of full dentures with digital fabrication systems (milling and 3D-printing technology) in only 5 steps and offers an integrated material system with digital denture tooth and bonding solutions, as well as try-in and base materials for process-reliable fabrication.

VITA VIONIC VIGO® digital denture teeth from VITA are made of SE polymer (silica-endowed polymer matrix) with a design perfected for the digital fabrication of dentures. 

5Ws* VITA VIONIC VIGO

Much has been written about the early methods to treat oral pain and how Baltimore was home to the first dental school in the United States, some 180 years ago. Less publicized is the history of how dental laboratories came about and how far the role of lab technicians and laboratory technological advances have come over the years. As addressed in this month’s cover story, artificial intelligence (AI) evolved and is poised to influence the dental lab industry. Here are 5 milestones that paved the way for today’s dental lab industry and for a future that will surely be impacted more by AI and machine learning.

Dr William H. Stowe opened a dental laboratory in Boston and was later joined by his cousin, Frank F. Eddy. The laboratory (later known as Stowe and Eddy) was generally acknowledged to be the first commercial dental laboratory in America that was separate from a private dental practice. According to the National Board for Certification (NBC) in Dental Laboratory Technology,Stowe was particularly noted for his prosthetic skills, and eventually found that he had little time for his own dental practice after accepting laboratory work from all the dentists who sought his services. His laboratory service proved to be so successful that it indicated the potential need for a dental laboratory capable of serving the profession at large, as opposed to Stowe and Eddy, which had to limit the number of dentists it could assist.

By the mid-1900s, 2 national organizations were claiming to represent the dental laboratory industry, but there was no single unified organization. A group of dental laboratory owners from throughout the country met in Chicago in 1950 and agreed to form a national, federated association, similar in organization to the American Dental Association, with state-level association components. The meeting marked the beginning of the National Association of Dental Laboratories (NADL), which represents nearly 1000 commercial laboratories nationwide today.

Early on, the NADL concentrated on the areas of recognition and education. In 1954, the NADL Education Committee began developing a certification program, which would set skill standards. The following year, the NADL Executive Council elected 7 people to the newly formed NBC, which adopted policies and approved examinations for the certification program. The first Certified Dental Technician (CDT) tests were administered in October 1958, and the first CDT certificates were awarded in March 1959. Today, there are approximately 5500 technicians with active CDT certificates.

Much of the lab industry was faced with a rough transition from analog to digital production, which transformed processes, materials, and workflow. Just as technology and advancements were improving efficiencies, a number of obstacles came about, including a recession in 2008, a decline of accredited dental laboratory programs, overseas manufacturing, regulatory forces, and laboratory consolidation. But despite these hurdles, lab technicians continued to thrive, innovate, and impact a population more aware than ever of the importance of good oral health.

Although many large dental labs are doing their part to help deliver great oral care services, 1 well-known lab celebrated half a century of operations when Glidewell commemorated its 50th year of operations with an employee celebration on its campus in Irvine, California, last year. The lab’s success has come in large part by taking advantage of new technologies along the way. Today, the dental lab industry is set to take advantage of AI technology in much the same way as most industries.

This month's The List takes a look at milestones that helped pave the way for today’s dental lab industry and for a future that will surely be impacted more by AI and machine learning.

5 Milestones That Helped Lead to the Path of Automation

Just a few short years ago, 3D printers were considered a luxury by some and a down-the-road technology to others. But now 3D printing technology is having a huge impact on many industries and the dental lab industry is no exception. Additive manufacturing is the latest piece of the digital dentistry workflow that has become a smart business choice for dental labs. It combines high quality with low costs and greatly streamlined workflows. The 3D printer market has been expanding rapidly, bringing this technology within reach for more businesses.

There appears to be an endless array of things that labs can accomplish with this technology, so if you haven’t yet gotten into 3D printing, it may be time to do so. Here’s a look at 16 3D printing solutions currently available.

This month's Product Roundup for Dental Lab Products takes a look at 16 3D printing solutions currently available.

3D Printers Are Achieving Great Things 

Zirlux Transitions are for all dental laboratories looking to produce high-quality, durable restorations that resemble natural teeth, helping to achieve lifelike esthetics without compromising strength.

Zirlux Transitions zirconia is indicated for both anterior and posterior use. This next generation transitional layered zirconia provides dental laboratories with a highly esthetic and strong solution for producing final restorations.

Zirlux Transitions feature Smart Incisal™ Technology, which provides higher chroma in the cervical area and decreases chroma toward the incisal area. This delivers the perfect balance between strength and translucency—1200 MPa and 48% translucency. Available in all 16 classic VITA shades plus OM2 bleach shade. Zirlux Transitions discs are manufactured in USA.

In addition to producing a bluish-gray effect, Smart Incisal technology is patented and enables the material to produce a higher chroma, or brighter shade, in the cervical area (the surface above the junction of the crown of the tooth and the root of the tooth), and decreasing chroma, or darker shade, toward the incisal area (the surface of the tooth that faces the tongue).

Zirlux® Transitions from Zahn Dental feature transitional layered zirconia with Smart Incisal Technology designed to achieve high-quality esthetic restorations.

5Ws* Zirlux Transitions

With Google, Siri, and Alexa there to answer basically every question, it’s easy to assume that artificial intelligence (AI) and machine learning are new inventions. However, AI actually traces back to the mythological Greek Talos, a giant bronze automaton created to defend Crete from invaders.

 Talos was a fictional creation, but over time, automatons and robots became real devices. Now in 2021, this technology doesn’t just perform tasks—it learns.

It can be a bit unnerving when Amazon pushes ads for a product we might have just mentioned or thought about a few hours before. But the AI behind that ability is something that dental labs can leverage to their benefit.

Machine learning is a function of AI that gives systems the ability to learn and improve from experience. Its algorithms use statistics to find patterns in large amounts of data.2 These algorithms are responsible for most of AI’s more well-known uses, including streaming platforms such as Netflix, YouTube, and Spotify; search engines like Google; and social media feeds such as Facebook and Twitter.

 According to Lisa Aguirre, a product manager of Dental Solutions at Roland DGA, DGSHAPE Americas, this technology can assist labs in distinctive processes and dimensions, including expanding production and other capabilities.

“By utilizing digital technology, such as Roland DG’s DGSHAPE DWX-52DCi with automatic disc changer, labs can take advantage of 24-hour unattended milling,” Aguirre says. “With DGSHAPE’s exclusive DWINDEX monitoring software, laboratory owners and technicians can monitor production on any mobile device or from the location of their choice. In addition, with DWINDEX software, lab owners can monitor the status of ongoing jobs throughout the workday, either remotely or from the comfort of their desk.”

Machine learning and AI can help throughout the business, starting with how a case enters the lab to the hours and expertise spent on it, to case planning, says Lou Azzara, CEO of Dental Services Group.

“The first 8 hours are critical to the success of the case and the relationship of practice and lab,” he says. “Getting that right is of utmost importance. Consider the expertise and talent of a lab tech who has seen thousands, potentially tens of thousands, of case scenarios. With their experience, they can navigate and see beyond the information that is presented. But if you incorporate AI, now you can start to do that at a scale that is beyond the ability of a single person or group of people and at a resolution that is unquestioned.”

Tolerance, the analysis of determining how close something is to its market or by how much it will deviate, is usually about 99.9% perfect with AI and machine learning, Azzara explains. This kind of deep understanding allows labs to establish a sort of institutional memory—building trends and predictability,

Machine learning can also assist with communication among stakeholders. As there is often a lot of back-and-forth communication among dental practices and laboratories, AI can make it easy to share accurate information.

“AI allows easy access of recorded metrics for proper planning and can give us insights before and as cases are planned. The value of this information for the lab-clinician relationship is immense,” Azzara says. “AI analysis on clinical scans for prep clearance, marginal integrity, debonding probability, and ‘restoration success grade’ would allow the lab to communicate issues with real measurements attached, creating a more efficient system and reducing unnecessary costs that could be passed on to practices.”

Similarly, office managers and staff spend a great deal of time trying to understand past deficiencies and improving processes, Azzara notes. AI can be used in the front office setting to predict what’s around the corner and improve the patient experience. But AI’s potential isn’t limited to only these aspects. Human resources management, operational management, sales, communication, and lab and/or practice relationship can be positively impacted by AI, leading to a more effective experience for patients, Azzara says.

AI and machine learning are constantly processing information, and the technology can essentially learn all of our habits and eventually predict our decisions. This is something Jack Marrano, CDT, Absolute Dental Services’ director of Signature Prosthetics, refers to it as “intelligent design.” This can prove to be a great asset to the lab industry, he says.

“The vision for it should be huge,” Marrano says. “My cell phone knows everything about me. My Alexa at home knows everything about me because they’re constantly learning. Anytime we enter a data point, it learns. That’s what we’re doing when we’re designing. Each time I design a crown from the initial proposal, I generally will do the same things.”

Machine learning appears to be the next phase in the workflow evolution. As the industry embraces digital workflows, machine learning takes those efforts to a new level. Ophir Tanz, CEO of Pearl, a dental AI software company, considers the next level a second part of the digital revolution, which is like adding a cognitive layer to digital dentistry.

“The way that I think about it is that you had a real revolution at one point in digital dentistry, where things started to become digitized and nonanalog,” he says. “And that has been great in many ways for the category. Now you have a huge amount of digitized dental data that [are] historical and being generated every day. It’s difficult to make use of [those] data in a real way because there’s so much of it.”

Tanz is also a board member of the Dental AI Council (DAIC), an organization that studies and promotes AI in the dental field. “One way to think of AI and machine learning and dentistry is the adding of a cognitive layer to the digital dentistry realm,” he says. “So, it’s kind of like the second part of that revolution. Now you’re able to do things that, historically, would have required some creativity or intelligence, but you’re able to do them in a more automatic fashion.”

AI and machine learning can not only help dental labs automate but can also allow for greater customization within workflows, Tanz says. Using AI gives lab technicians the ability to create designs from the ground up without using a template.

“Areas where we see activity with machine learning in the laboratory environment involve things like smile design and crown design—which is a really interesting one because you’re no longer beholden to just some set of templates that you’re customizing,” he says. “You can really use AI to design a crown restoration from the ground up with the ideal contacts, occlusion, shape, and anatomy.”

Sure, AI and machine learning have been around for a few years. But how do companies tailor it to the dental lab? And can machines learn dental anatomy as well as an experienced technician can?

These questions are an inspiration to Sergei Azernikov, PhD, vice president of AI and Machine Learning at Glidewell Dental. His team wasn’t achieving the level of automation it wanted.

“So, we started looking into new ways to do it, and a machine learning initiative started taking off. One of our first questions was, ‘Can we learn dental anatomy using machine learning?’ Can a neural network recognize teeth, for example? We asked ourselves, ‘Can we train a machine to recognize individual teeth?’” Azernikov says.

Once the team at Glidewell had AlexNet, its AI system set up—in a move reminiscent of a scene out of a science fiction film—it challenged an experienced, seasoned technician to a friendly competition against the machine. The results weren’t surprising.

“We trained the AlexNet network, which was a standard architecture, to identify teeth,” Azernikov says. “Even though that person was very experienced, he did make some mistakes, but the machine was accurate 100% of the time. This gave us confidence that the AI model can learn dental anatomy, and as such, it can be applied to many different applications. That’s how we convinced the company to invest in AI.”

That was 5 years ago, and machine learning has since been deployed in various aspects through the company. According to Azernikov, Glidewell first implemented this technology to track cases as they come in. The company began to take images of every case that came through their doors in an effort to recognize the boxes that dentists send in, types of impressions, and more, all of which used to be done manually.

When it comes to design, they’ve developed a system that generates crown proposals with little or no touches, Azernikov continued, and now the team is focused on doing the same for bridges.

“On the manufacturing side, when a design is ready, we have our robotic systems that go further,” he explains. “They take the blank and put it into [the] machine. Once it’s milled, we have an automatic [quality assurance] system that checks scans of the crown and compares it to the initial design to make sure that what we milled is actually what we designed. Then when it goes out, we also check whether it’s the right crown that specifically goes to that patient because for digital cases, we don’t have a model to test the crown on.”

When dental labs are looked at from a traditional standpoint, it’s easy to see how machine learning helps the overall workflow. According to Azernikov, Glidewell’s handles more than 10,000 cases per day. That kind of volume is similar to that of a mass-production manufacturing facility.

“But the big difference is that every case is unique,” he adds. “Even 1 person doesn’t have 2 identical teeth. So, it’s really a classic case of what’s called ‘mass customization’ or personalization at a huge scale.

“Our systems must be flexible enough to cope with all this variability, but at the same time, it needs to be able to consistently generate high-quality outcomes for our customers and their patients. Those are the things that are difficult because you can be very consistent if you do the same thing. If you make the same car, it’s easier, because all the parts are the same. But here, every part is different. So, how do we stay consistent across the board with a low price and high quality? That’s challenging. And that’s exactly why patient-specific products are the perfect case for machine learning. We just started, frankly, scratching the surface of where we can use it.”

Once the latest and greatest device is on the market, it’s hard not to want the newest model. We’ve all upgraded our cell phones, only for a fresher, sleeker model with better features to debut days or weeks later—it’s frustrating, and not to mention expensive. But one of the upsides of machine learning in its current state is that it’s not cost-prohibitive. The majority of the computer functions happen on the cloud. This is good news for dental labs looking to add AI and machine learning to their processes without the exorbitant expense of brand-new technology.

“The vast majority of folks out there aren’t going to engage directly in trying to build these types of capabilities,” Tanz says. “That is, at least in our experience, no hardware required whatsoever. So, no change to your existing infrastructure and no actual physical equipment costs whatsoever. The software tends to be incredibly lightweight. The reason for that is that all of this analysis and all these calculations are done in the cloud. So, what happens is—and this is the case for videography and oral scans—you capture whatever the asset may be, it gets sent out to the cloud,” he continued. “It gets processed there on very, very powerful machines that are a scaling infrastructure, and the results are sent back. Really, it’s the upload time, and then whatever text data is received back, which is obviously minuscule, and that tends to be the entirety of the integration. It’s as lightweight as anything you’ll find and typically fairly simple to engage with now.”

Despite how capable these machines are, it still requires a bit of investment from the lab—mainly time and commitment. As powerful as these tools are, they can’t do all of the work for you, Azzara says.

“First and foremost, they need to adopt the mindset and dedication to make the investment, in a financial sense, but also in a time investment,” he says. “You must dedicate a plan to incorporate the tools and help your organization get through the learning curve, which can feel like a slow start to progress, but you’ll find the impact will start to gain traction. The development is largely based on having a culture that is innovative in thought, dedicated [to] the day-to-day, but also building a pathway to a transformational future. The system’s components are centered around the capturing, sending, and receiving of digital impressions, and with that, we need a streamlined workflow that keeps these files in their native format as much as possible.”

Just like other technology, AI and machine learning are not perfect systems. One of the challenges these machines face is interoperability, or the capacity for different information systems, devices, and applications to exchange data in a coordinated manner.

“You have different setups in different infrastructures, in different environments,” Tanz explains. “Those companies don’t have the legacy plugins to their systems in order to pull out data, or they don’t want to share it, or whatever the case may be. Oftentimes, what you’re dealing with in terms of hindrances or bottlenecks, are just legacy systems, a fragmented environment, and needing to make things work in the reality of those environments. That’s something that people often overlook when you have an industry that’s been around for a while with lots of different software players and lots of old software and competing philosophical views about what should be open and what’s proprietary.”

One of the tech’s other biggest obstacles is the growing adoption of chairside systems, Azerniknov says. As those systems become easier to use and more mature, other doctors will feel confident enough in using the technology.

“Among other things, doctors will feel more confident to produce restorations while the patient is in the chair. In order to stay competitive, dental labs will have to invest heavily in technology to improve the level of service and reduce costs,” he says. “For example, we were recently able to provide 3-day turnaround to our customers, due to technological advances in manufacturing processes.”

Another issue could be relying on the computers too much. It’s one thing if a computer were allowed to design restorations all on its own, but human technicians will still want to be involved, according to Marrano, especially when proposals are involved. 

“If you have 10 technicians and you line them up, each one will tell you that their crown is better than the technician sitting next to them,” he says. “There may be truth to it, but a lot of it is, ‘This is how we do it,’ and nobody is better than that than the technicians. I’ve spent a lot of time consulting in many labs, and when you walk in, every lab is the same, but every lab is different,” Marrano continues. “They all have their own way that they do restorations or their own standard of what they believe is ideal. So, I don’t think it could be something that is generated by a computer. If the computer system could generate the most perfect automated proposal, every technician out there would change it because the design was not theirs.”

Marrano believes that AI must learn from individual technicians to understand a particular lab’s process and standards. Otherwise, it’s self-defeating.

“If they just put something up there, everybody’s going to say, ‘Oh yeah, but I could do it better,’ and then they’re going to end up changing it, which kind of defeats the purpose,” Marrano says. “If I started with a standard molar and gave it to 10 different designers, you would get 10 different variations of that same molar design. And I can tell you, there was probably nothing wrong with the proposal in the first place, but they have to tinker with it.”

Aguirre believes that AI’s benefits outweigh its disadvantages. Dental digital technology and AI go hand-in-hand.

“Digital technology has progressed significantly over the last few years, providing more efficient workflows, unattended milling, and the ability to monitor lab jobs and milling machine functions offsite,” she says.

Although machine learning has taken hold in some labs, it hasn’t yet seen broader adoption. The notion of what machine learning can do may not be clear to some, which could explain this reluctance.

“One of the key reasons that we helped found the DAIC is because this technology is so fundamentally misunderstood, and this isn’t only for dental labs,” Tanz says. “It’s really across every industry. And that’s of no fault of anyone in particular. [Machine learning] is a term that has been used and abused by Hollywood forever. Often, when you think about AI, the first thing that comes to mind is killer robots…we’ve been trained to interpret this term in a certain way. There is very little understanding about what this technology actually is, what it can do, what it can’t do, and what it will be able to do in the future.”

In the effort to prove 3 things, the DAIC engages in independent academic research to

Better understand how and where AI will most significantly impact the dental industry.

Validate the technology’s performance and capabilities.

Answer fundamental questions related to AI’s role in the oral health care ecosystem of tomorrow.

The first question deals with maintaining levels of consistency and proficiency among practitioners and laboratory technicians, Tanz explains.

“We just released a study where 136 practitioners looked at the same FMX [full mouth x-ray] set. What you find is diagnoses across the board and treatment plans between  $300 and $36,000 and everywhere in between. So, there’s a real consistency problem with the kind of care that is getting issued for the same patient,” he says. “If the same patient goes to 10 different dentists, they are very likely to get 10 different diagnoses and treatment plans. That’s a problem. We can do better with AI.”

Tanz says the next question deals with the efficacy of AI and how to validate it academically.

“What are case-driven examples of how AI is going to be implemented across these various categories of the industry?” he asks. “Let’s really think critically and talk to leaders in those categories about the kind of impact that’s going to have.”

Another issue is that labs may expect an AI solution to take care of every aspect of the lab on its own in the short-term, which is simply not the case, Azzara says.

“In the short-term, they expect the AI software and tools to ‘show up and work,’ and the misconception is that it is not going to take additional work,” he says. “However, long-term underestimating of the power can seismically influence the way we operate.”

A solution to reining in expectations is understanding how AI is trained, Azzara adds.

“We are working within an industry that has, historically, been a hands-on problem-solving group,” he says. “Digital has been in dentistry for a while, but it has mostly been placed into the hands of newly hired talent with a great knowledge of technology, but less knowledge of dentistry. Therefore, the disconnect from the experienced, hands-on employees who know the ins and outs of dental is often removed from the more technologically inclined, but inexperienced digital group.”

Aguirre says that there may also be a misconception about what is possible with AI. Many make the mistake of assuming that adopting or using AI workflows are more complicated than they are when some are fairly easy to use.

“On the contrary, today’s digital dental technology provides efficient, easy-to-use solutions that expand both laboratory capabilities and overall production capacity,” she says. “The DGSHAPE DWX-52DCi’s 6-disc changer provides 24-hour unattended production and remote monitoring with its exclusive DWXINDEX monitoring software.”

In the bigger picture, AI and machine learning can be a difficult concept to picture, even with all the technology that consumes our daily lives. In many ways, it still feels so far away.

“I think most people don’t give it a thought,” Marrano says. “It’s one of those things you hear Elon Musk talking about, something that is way above most of our heads. It’s something that we don’t really, in the laboratory, interface with as of right now. I definitely see it being a part of laboratories in the future, but right now, I don’t think it crosses technicians’ minds.”

Another reason for the slow adoption of AI is the attitude toward new technology throughout the industry—it’s essential for some, a perk for another, and unnecessary for others.

“The dental industry is…pretty conservative,” Azernikov says. “And if you look at the first step, in order to do machine learning, you need the digital workflow, and that’s a key. Even though intraoral scanning has been available for decades now, still I would say only 20, 30 percent of doctors have it. Most doctors still work with analog impressions. To me, the doctors are still very slow in adopting those technologies.”

What will it take for machine learning to take off? Don’t be surprised if the newest generation of dentists and lab technicians are the ones to embrace and drive the future of dental AI and machine learning, Azernikov says.

“It’s much more safe and faster and more accurate, and it’s a generational thing,” he says. “As the new generation comes in, they’re more open to new technologies. They were born into a digital world, so it’s easier for them to adopt this.”

An important piece of the puzzle has been cloud computing. According to Azernikov, before Glidewell even considered machine learning, they migrated their infrastructure to the Cloud via Amazon Web Services (AWS). Up until that point, their data were spread across several locations and hard drives.

“It was very difficult to collect it, to make sense out of it, [and] to analyze it was practically impossible,” he says. “Once we accumulated everything in one place, now we have more than 10 million cases, basically, stored on AWS, and it’s very easy for us to crunch it, to go through it, to collect the cases, to train our models, all using the data. That’s a very important step for anyone who wants to get into that space. You cannot have the legacy infrastructure and jump directly from that to the AI era.”

But, as Azernikov points out, humans still play a massive role. He suggests starting with simply having the right mentality when approaching AI; don’t see it as a threat or view it as taking away your job, but view it as a powerful tool to help you be more productive.

One of the obstacles here is finding or training the right team to implement these processes, Azernikov notes. It can be challenging to compete with the tech sector in retaining engineering talent.

“It is extremely difficult in today’s environment because the Amazons and Googles are going after these engineers as well,” he says. “And you need them if you want to develop or even just deploy AI technologies. You still need to understand enough to effectively use it.”

Marrano is hopeful for an even more accessible future for AI and machine learning. He says there should be individual logins for designers; that way, the machine starts learning the hundreds of thousands of movements that a technician makes.

“If I sit down at one of the designer’s computers right now, I can see how many movements they did to design the crown,” he says. And, more than likely, if I watched that one designer for a time period of, let’s say over an hour, they [will] continue to make the same adjustments that they did before. There’s no reason for it…my iPhone can do it. It’s very, very simple. They just need to implement it. That’s what’s going to revolutionize the industry right now.

“Single-unit design is gone,” he continues. “The future is AI design.”

Skashkevich A. Stanford researcher examines earliest concepts of artificial intelligence, robots in ancient myths. Stanford University. February 28, 2019. Accessed January 14, 2021. 

https://news.stanford.edu/2019/02/28/ancient-myths-reveal-early-fantasies-artificial-life/#:~:text=The%20myth%20describes%20Talos%20as,boulders%20at%20approaching%20enemy%20ships

Hao K. What is machine learning? MIT Technology Review. November 17, 2018. Accessed January 14, 2021. 

https://www.technologyreview.com/2018/11/17/103781/what-is-machine-learning-we-drew-you-another-flowchart/

Mayo G. What is tolerance analysis? Shield AI. April 16, 2019. Accessed January 14, 2021. 

https://www.shield.ai/content/2019/4/16/what-tolerance-analysis

Interoperability in healthcare. Healthcare Information and Management Systems Society, Inc. Accessed January 14, 2021. 

https://www.himss.org/resources/interoperability-healthcare

Our purpose. Dental AI Council. Accessed January 14, 2021. 

How artificial intelligence technology has evolved, how it is poised to influence the dental lab industry, and what is needed for consistent adaptation.

The Imitation Game

February 2021 New Products

A look at some of the recently announced products from the dental laboratory industry.

DLP February 2021 New Products

Information provided by Ivoclar Vivadent.

Dental laboratories hoping to maximize production and take the guesswork out of the workflow.

The PrograPrint’s accuracy and reliability make it particularly adept when you need to print precision models, splints, surgical guides, wax inlays and onlays, and crowns and bridges for pressing or casting.

From CAD to postcuring, this system consists of components engineered to work together to maximize production throughout the workflow. The PR5 printer is the cornerstone of the system, with a self-calibrating DLP light engine designed to ensure a high degree of precision. Its self-contained ProArt Print Wax, Splint, and Model cartridges allow for virtually contactless handling of the resin materials, which reportedly eliminates contamination and waste. The PR5 features built-in material recognition technology that automatically alerts users of the level of print material in the cartridge and even warns users if the wrong material has been inserted for the chosen application.

The PR5 features a 125 x 78 mm build platform with no deformation of printed components at the edges. The 4-million-pixel resolution with 49-μm pixel size and 50- to 100-μm layer thickness make it suitable for a variety of applications. The build platform of finished printed parts loads into the PrograPrint Clean’s dual stainless steel stations and is submerged in cleaning solution, and a self-reversing magnetic spinner at the bottom of each tank keeps the solution in motion. The PrograPrint Cure features high-intensity light for fast curing and it allows for programmable preset curing times based on users’ material preferences.

A 3D printer system that was designed by technicians for technicians to meet their needs.

5Ws* PrograPrint 3D Printer System

The software platform supports a great degree of customization, allowing labs to set different data access permissions for different users and to customize the style and copy in invoices and other communications. New features include the ability to label remakes, including whether it was due to clinician or technician error, as well as bar code scanning for instant access to specific job files, and many other customizable features.

Anytime cases come in or go out of the lab, or lab operations data are needed.

Designed to promote operational efficiency, this software platform allows labs to customize how cases are tracked when they enter the lab through every step including delivery to the client. Having all case and client information in a single, intuitive platform allows Lab Manager to manage customers, jobs, products, invoices, payments and technicians while quickly and simply creating important documents such as delivery notes, invoices, statements, financial reports, and job and technician tracking reports.

This UK-based company is bringing its popular dental lab management platform to labs throughout the United States. Information provided by Transactor Systems Ltd

5Ws* Transactor Lab Manager V8.2 

dental revolution has already firmly cemented its place at the center of fixed restoration production, but the adoption of digital denture workflows and products has progressed at a slower pace.

Fixed and removable production can reap benefits from digital technologies and the materials they process. Still, the shift to digital production of removable dental appliances is happening at a more measured pace.

This pace is due to many factors, but a big one is the learning curve and the full scope of actually creating a denture or other removable appliance. A single crown is fairly straightforward but shifting from designing it in wax to designing it on a computer screen is a leap, yet not always a gigantic one. However, removable appliance cases may involve multiple teeth and gingival tissues, and doing this digitally involves a bigger analog to digital transition, but that transition is well underway, according to Gene Peterson, CDT, director of Technical Development and Learning for Sterngold Dental.

“Through the last 9 years, there’s been a lot of advancements. Some people have said it’s been a revolution. Some people say it’s still evolving. I think it’s kind of both. I think it’s a revolution that’s evolving,” he says.

Today digital dentures are a reliable and viable option for many situations, Peterson says. The latest materials and systems can produce appliances that provide great fit, function, and esthetics to meet the needs of patients. These digital denture solutions offer far greater production efficiency and can drastically reduce the number of dentist appointments for the patient. Still, the move toward digital denture production remains a slow shift for the industry.

Peterson says removables technicians looking to shift to digital workflows often struggle to translate their skills and experience from the benchtop to the desktop. Although the essential steps of creating a denture remain the same, transitioning from the tactile analog process to the virtual digital process is the biggest hurdle for many clinicians.

“The idea is to find that harmony where you can bridge that gap easily and see the symbiosis between the 2, so they come together,” Peterson says. “You have to be able to say, ‘OK, this is what I did in the old world, and this is what I do in the new world, and now I do it with a mouse instead of a handpiece, and I’m good to go.’”

Peterson admits that digital dentures may not be the best match for every lab, every technician, and every practice. It’s a technology that should be embraced, and the technician should start thinking digitally. It’s been an excellent tool for the technicians, labs, and dentists who are truly engaging with the technology.

A variety of digital denture systems and materials are currently available. Some systems feature milled or 3D-printed denture bases with the denture teeth placed and bonded using longstanding analog techniques. Other systems offer a monolithic denture by milling the appliance from a special material composed of both gingiva and teeth.

Peterson noted that some systems require a large upfront investment in equipment and materials from the dental lab, whereas others can be leveraged with a minimal initial investment. Early printed denture bases have been criticized for lacking in characterization, but digitally produced dentures can all be customized to provide whatever level of esthetics are designed for that case.

Although the workflow used to get to the finish line is vastly different between analog and digital denture workflows (and even among the workflows of other digital denture systems), as with any case, success with digital dentures depends on having good information about the case at the start.

Peterson says creating a perfect digital denture requires accurate clinical information, including detailed impressions, an accurate bite, the midline incisal edge, proper lip support, the plane of occlusion, the tooth size, and the central height. As long as those measurements are accurate, digital denture design applications should provide a guided process to fabricate a digital denture that will be a perfect fit for the patient.

The highly accurate information captured in a digital impression is a great starting point, but the clinician should provide accurate measurements for all the details of the denture to turn a denture case into something akin to a crown appointment when the patient comes in once for prep, scanning, and design, and then returns for seating the final restoration. With accurate starting measurements, Peterson believes digital dentures can be the best and most efficient option for many patients, and clinicians can be sure of their measurements when they measure at least twice to make sure their numbers are repeatable.

“When they’re accurate, it’s going to go right in the mouth,” he says. “You’re going to have that 2-appointment denture, and that’s a key to digital dentures.”

Optimal Efficiency With Workflow Flexibility

Sterngold recently partnered with Merz Dental which provides the Baltic Denture System (BDS), a versatile digital denture solution that standardizes parts of the design and fabrication to make the production process as simple and efficient as possible. The solution includes 

Creator® PLUS, a customized software application that guides technicians through the design steps. The designs are then milled from special 

Load® discs, which come preformed with denture teeth set up in an optimized lingualized occlusion. This allows the system to go directly from design, to milling, to finishing without the need for multiple try-ins or a lengthy setup process.

“It’s a milled premium denture,” Peterson says. “It’s a monolithic final denture that looks beautiful.

“For a premium denture, you’re getting a lot for your buck,” he continues. “In fact, in its simplicity, it will exceed your expectations because what you’re getting and what you’re able to do with this software and the final product is something that nobody else is really doing at this price point.”

Peterson has experience working with multiple digital denture solutions and working for companies behind the scenes. He likes the BDS because it offers both simplicity and versatility. The solution can be used for denture sets or single arches and can even be used for immediate dentures. Because it offers clinics the benefit of faster-finished dentures at a lower cost, labs can jump into the solution in multiple ways.

Labs with a compatible mill are ready to do everything on-site and can invest in the full solution to create their digital dentures completely in-house. Those without a mill or looking to start smaller can design in-house and send files to Sterngold for milling. Alternately, labs can take on any of the steps in-house and outsource the rest of the process. In the end, all the paths lead to digital dentures being delivered to the prescribing clinic.

Load pucks are manufactured from highly esthetic, high-impact PMMA, which can be characterized and finished like any standard denture. The preset teeth are affixed using a process that creates a true chemical interface between acrylic used for the gingiva and the material used for the teeth. This process creates a monolithic denture with distinct teeth and gingiva, and virtually eliminates the risk of teeth debonding and needing replacement.

“You’re not going to have pop-outs,” says Peterson. “You’re not going to have fracture failures because there’s no mechanical bond to break.”

Like any digital solution, the software is a key part of the digital denture process, and this is the area where things have truly advanced in recent years, according to Peterson. The major dental CAD/CAM applications from companies such as exocad and 3Shape now offer removables modules that technicians can use with multiple digital denture materials and workflows, but some systems, such as BDS, offer their own software platforms tailored to the specific approach to digital dentures. Peterson describes the 

Creator PLUS application as intuitive and automated with a step-by-step approach that walks the technician through each part of the process.

“The software journey has been what’s really made the difference,” Peterson says.

Although digital denture software might be easier to use, it still requires specialized knowledge and training for the digital technician to follow the software prompts and recognize what is accurate and what needs to be adjusted. Like any system, the quality of the output is directly related to the quality of the input, so even though the system can make suggestions and automate steps, the technician using the controls still needs to know enough to recognize when the computer is heading off course.

The way Peterson sees things, digital dentures are fully ready for primetime today, and the only things holding them back are trust in the process from the clinicians and buy-in and training from the technicians. He adds that solving the first challenge is going to be easier than the second.

Getting clinicians on board with digital dentures should be achievable. When done correctly, the quality of digital dentures can meet the demands of most dental practices. Convincing dentists to commit to milled digital dentures rather than printed dentures, for the time being, is part of achieving this goal. Although Peterson says printing will be the industry standard when printers and materials improve enough to surpass the quality of milled dentures, he thinks milled restorations are the best option today.

With quality no longer a barrier, clinicians really only need to be convinced that they can get equal quality dentures in far less time by embracing a digital process. In fact, Peterson believes digital dentures could be just the thing to get clinicians excited about providing dentures for their patients.

“It’s my passion to get doctors to the point where they’re comfortable and consistent making the records, and they enjoy making dentures again,” Peterson says. “If you can get some basic consistency down in your record making, you’re going to have a lot of enjoyment out of making dentures, and it can be a hugely profitable aspect of your business.”

Unfortunately, increasing the number of digital denture technicians will be a bigger challenge. Peterson says the barriers on the lab side are more challenging because they are institutional. There is a declining number of schools and educational opportunities for new technicians, and many established removables technicians are not interested in learning a new approach to their craft.

According to Peterson, a key to breaking down this barrier is setting technicians up for success when they start learning the digital denture processes. They need to not only understand the software workflows but also how to identify when inaccurate data are causing problems with the denture design. Digital denture technicians need to be ready to coach the doctors and drive home how accurate information at the start leads to efficient production and dentures that fit right the first time. With an automated digital workflow, the problems that might arise will rarely originate at the lab.

“With digital dentures, they can’t blame a technician anymore because [they are] just putting in information that they give [them],” Peterson adds, once again noting the importance of accurate scans and measurements from the dentist.

Labs often face a barrier to entry into new digital platforms due to the cost. Peterson says this led to innovations like BDS where a lab can embrace it 1 piece at a time and build up enough business to generate a quick return on investment by automating production step.

When it comes to the need for more digital denture technicians, Peterson says it may take time. He does not expect the established generation of denture technicians to shift to digital in large numbers. They’re largely comfortable with the way they work and don’t have immense pressure forcing them to make a switch.

However, the next generation of lab technicians will be trained in a digital environment and might learn digital techniques before or in lieu of analog workflows. Even though they will enter a much more automated industry than previous generations, they will still need to learn the same key details about the engineering, chemistry, and design of dental restorations, whether they work at a bench or a keyboard.

Peterson says he’s excited about how far digital dentures have come and looks forward to being a part of the continued development of the removables side of digital dentistry. The tools and materials required to make dentures will evolve, but the core knowledge at the heart of designing a removable dental restoration will remain the same.

“I’ve been in this for 10 years, and when I first got into doing this, people would say, ‘You’re going to make my grandma’s dentures with a computer? Are you kidding me?’” Peterson says. “The format is different, but the details remain the same. Technicians, per se, aren’t going to go away. They’re just going to be doing different things.”

The latest digital removables materials, processes, and workflows such as the Baltic Denture System available via Sterngold are making denture production more efficient and simpler.

Dental Lab Products February 2021