The AI-powered dental algorithm, Video Caries Assist, reduced missed cavities by 43%.
The FDA has granted 510(k) clearance to a dental artificial intelligence (AI) system that helps detect cavities on dental X-rays.
The imaging algorithm from VideaHealth, called the Videa Caries Assist, uses data from the company's so-called Videa Factory that includes more than 100 million data points from dental service organizations, insurance companies, clearing houses, and universities to capture patient diversity and create an impartial algorithm.
In a clinical trial, missed cavities were reduced by 43 percent among dentists using the AI system, in addition to a 15 percent reduction in erroneous detections of lesions. A companion study completed with Heartland Dental echoed these results, showing an increase of 46 percent in lesions detected as well as a 13% improvement in diagnostic accuracy among dentists utilizing the platform.
“Our biggest priority as a team is ensuring that our solution is effective across diverse patient populations and helps dentists deliver the most accurate diagnoses," VideaHealth CEO Florian Hillen said in a statement. "This paves the way for more appropriate dental treatment recommendations and the opportunity for dentists to foster deeper patient engagement.”
VideaHealth expects that use of the imaging algorithm will not only improve diagnostic performance but also pass on cost-savings to patients by reducing unnecessary interventions.
VideaHealth's platform is just the latest to utilize AI to improve dental imaging analysis. The FDA previously gave the go-ahead to Overjet's Dental Assist program which helps measure bone loss seen on X-rays, and also recently cleared Pearl's Second Opinion software which helps detect cavities, inflammation, and tartar and monitors fillings, bridges, and other interventions.
Can Portable Dual-Energy X-Ray be a Viable Alternative to CT in the ICU?
September 13th 2024The use of a portable dual-energy X-ray detector in the ICU at one community hospital reportedly facilitated a 37.5 percent decrease in chest CT exams in comparison to the previous three months, according to research presented at the American Society of Emergency Radiology (ASER) meeting in Washington, D.C.
Can Radiomics and Autoencoders Enhance Real-Time Ultrasound Detection of Breast Cancer?
September 10th 2024Developed with breast ultrasound data from nearly 1,200 women, a model with mixed radiomic and autoencoder features had a 90 percent AUC for diagnosing breast cancer, according to new research.
What a Prospective CT Study Reveals About Adjunctive AI for Triage of Intracranial Hemorrhages
September 4th 2024Adjunctive AI showed no difference in accuracy than unassisted radiologists for intracranial hemorrhage (ICH) detection and had a slightly longer mean report turnaround time for ICH-positive cases, according to newly published prospective research.