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How open architecture CAD/CAM can benefit your practice

With exocad’s ChairsideCAD, clinicians have the freedom to choose the best hardware and software for same-day dentistry regardless of the product or system they prefer. The technology has been selected as a Cellerant Best of Class Technology Award recipient in 2019 and 2020. (Photo: exocad)

Dental Tribune USA

Tue. 13 April 2021

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ChairsideCAD, available from exocad, is the choice of software for leading manufacturers of dental CAD/CAM systems, according to the company, and it has been selected as a Cellerant Best of Class Technology Award recipient in 2019 and 2020.

“We are entering a new era in dentistry — one that will change how we diagnose, treat and manage our patients and practices,” said Dr. Lou Shuman, CEO of Cellerant and founder of the Best of Class Technology Awards. “This was a breakthrough year in product and services technologies. The panel spent hundreds of hours in close discussion reviewing and analyzing the corporate landscape. Pay close attention to our winners as they are truly leading the way to provide you what is best in today’s contemporary practice.”

With exocad’s ChairsideCAD, clinicians have the freedom to choose the best hardware and software for same-day dentistry regardless of the product or system they prefer. This is a groundbreaking evolution, according to the company, since historically the industry has been limited to specific workflows and specific hardware/software working together and adhering to a more closed architecture.

Derived from exocad DentalCAD, a signature software solution within the dental laboratory marketplace for more than a decade, ChairsideCAD includes dentalshare, a powerful collaboration tool for clinicians and labs, according to the company.

“We are honored to receive this prestigious industry award from our clinical audience. With exocad ChairsideCAD, clinicians can access labs, design and production centers with freedom of choice,” said Larry Bodony, president of exocad America. “With this open and flexible workflow, clinicians can maximize their return on investment as well as valuable chair time.”

“Our goal is to help the doctor make the best decisions for their office, which, in the end, benefits the patients that we all serve,” said John Flucke, DDS, Best of Class panel member. “I’m honored to be able to help my peers with the decision-making process and helping them wade through the plethora of high-tech products that can change offices and lives for the better.”

Bodony said, “Having received the Best of Class Award from the Cellerant Company and recognition from their exclusive panel of experts is truly an honor.”

(Source: exocad)

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TIRUCHIRAPPALLI, India: The integration of artificial intelligence (AI) into medical imaging can support diagnosis and reduce clinical workload. A new study has tested the performance of two newer AI image recognition models—called transformers—in the automatic detection of common dental conditions on panoramic radiographs. The results highlight their potential to support dentists with faster and more reliable assessments.

Undertaken by researchers in India, the study sought to determine whether software can reliably sort a panoramic radiograph into a condition category—caries, gingivitis, calculus and hypodontia—based on the overall radiographic pattern. The authors tested two transformer models that process an image differently and compared their diagnostic performance and speed. Their goal was to address limitations of traditional diagnostic methods, including subjectivity, variability between clinicians and difficulty detecting early or subtle lesions.

They trained, validated and tested the models using a dataset of over 5,000 annotated panoramic images sourced from multiple clinical repositories. Their results showed that the best-performing model achieved slightly higher diagnostic performance, reaching around 96% accuracy. The second model delivered comparable accuracy but ran more efficiently—a real-world consideration. Here, accuracy refers to how often the model assigned the correct category to the whole radiograph. Both models were able to classify most radiographs correctly, but performance differed by condition.

How does this relate to AI products already used in clinics?

Tools such as Pearl Second Opinion, VideaHealth Detect AI and Align X-ray Insights support decision-making in that they typically highlight regions of interest for specific findings on radiographs. In contrast, the present study evaluated whether the AI models tested could be used for automated categorisation of radiographs as a whole, not as regions.

Overall, the study concluded that transformer-based systems offer a promising tool for automated diagnosis and have the potential to enhance early detection, reduce diagnostic errors and streamline workflows. Future work will focus on testing with larger and more diverse datasets and refining models to ensure reliability before routine clinical deployment.

The study, titled “A self attention based deep learning framework for accurate and efficient dental disease detection in OPG radiographs”, was published online on 21 January 2026 in Scientific Reports.