How can AI support dentists and improve long-term patient outcomes? Learn about the Artificial Intelligence Applications in Dentistry use cases in dentistry and see how top dental practitioners use V7 to build AI solutions shaping the future of dental care.
Radiography, intraoral scans, and facial scans often present dental practitioners with a quantity of overwhelming and unstructured data. AI-driven dental imaging software can help make sense of the data quickly and efficiently.
Machine learning algorithms also proved to outperform dentists in diagnosing tooth decay or predicting whether a tooth should be extracted, retained, or have restorative treatment.
In this article, we’ll take a detailed look at some of the most promising applications for AI in dentistry.
1. Dental decay and periodontal disease detection:
Just as “two heads are better than one,” enlisting the help of additional (computer vision) eyes can improve dentists’ ability to identify and treat issues.
And sometimes that extra help is more valuable than you might expect. Let’s take a look at two examples: dental decay and periodontal disease.
IT is not to say that dentists are bad at their jobs. AI is just really good at analyzing and processing large amounts of complex data.
It saves the dentist time, increases diagnosis accuracy, and leaves more resources available for patient care.
2. Oral cancer detection:
While losing a tooth can be traumatic, it pales in comparison to the effects of oral cancer.
Visible oral lesions called Oral Potentially Malignant Disorders (OPMDs) are a strong sign of cancer and can be detected in routine oral examinations by a general dentist. The problem is that this kind of examination does not occur frequently enough during dental checkups.
If only there were an efficient, cost-effective way to automate the detection of malignant or potentially malignant lesions.
V7 users are working hard on annotating dental data and building models to detect oral cancer.
3. Detection and diagnosis of dental caries:
We met dental caries back in our discussion of tooth decay, but here we’ll look at it more in-depth. After all, “looking at” cavities is one of the most frequent and crucial jobs a dentist has.
As with oral cancer, early detection of dental caries is critical to preventing irrevocable harm. When cavities are treated early, it significantly reduces the cost of treatment, time of restoration, and risk of tooth loss.
Recently, however, computer-aided detection and diagnosis systems (CAD) are making their way toward becoming a standard part of dental clinics. These systems can read dental X-rays and cone-beam computed tomography (CBCT) images for signs of oral pathology.
CAD systems are particularly good at using pictures of dental X-rays to spot dental caries that occur between teeth, which are often quite difficult to see with the naked eye. Further, computer vision-powered systems can estimate the depth of lesions and use this information to detect and classify caries.
So how well did our AI dental assistant do? Quite well!
Based on the results of all the test images, the CNN managed to detect caries with 92.5% accuracy in standardized, single-tooth photographs.
If you’ve ever had a root canal, then you’ve come face to face (literally) with endodontics.
Luckily, AI has applications that help dentists detect and treat these dreaded pathologies even more effectively.
Endodontists typically use radiographic images to examine, measure, and evaluate the condition of the tooth down in the gums (ie. the root).
AI models can also look at these images and determine the structure, measurements, tissue viability, and even potential success of treatment for those out-of-sight portions of the tooth.
Deep learning algorithms can then detect, locate, and classify different aspects of tooth root anatomy and possible pathologies. This is useful for locating specific tooth structures or identifying particular kinds of fissures and lesions in or around the tooth.
5. AI-assisted orthodontic treatments planning:
So far we’ve seen that AI computer vision and machine learning can be of great help in diagnosing and treating issues in and around the teeth. But—
Can it help with moving teeth into the right place?
Indeed it can.
Orthodontics requires a significant amount of planning, and AI can be used to optimize analytics for predicting things like the size of interrupted teeth or the potential need for extraction.
Orthodontists also need to determine the best path for teeth to move in. AI algorithms can take a starting point and target endpoint and calculate the best way for a tooth or group of teeth to reach their optimal destination.
This saves dental practitioners a lot of time and increases the efficiency of tooth movement. As the old carpenter’s adage goes: “Measure twice, braces once.”
In addition to moving teeth, dental professionals can also use computer vision to diagnose bone pathologies in the mouth. For example, it’s already being widely used to diagnose and classify osteoarthritis in the temporomandibular joint.
So from dealing with a bit of pain to get your teeth straight to relieving an arthritic jaw, AI is proving to be a promising friend to dentists and orthodontists alike.
6. AI in Dentistry: Limitations (and how to overcome them):
While all that we have covered has great potential for transforming dentistry, there are currently some limitations to the widespread application of AI in the dental industry.
The most significant is access to quality training data consisting of properly labeled dental datasets.
AI algorithms can only perform accurately if they’re given relevant data to learn from. This means using annotations such as bounding boxes or polygons to label objects of interest and highlight relevant areas.
In general, publicly available datasets are a great resource for AI training.
However, the trouble with dental AI isn’t an unwillingness or inability to compile data sets or access those currently available. Rather, the data needed for training an artificial intelligence in dentistry needs to be anonymized, or collection requires patients’ consent.
Annotating medical data requires a HIPAA and FDA-compliant image annotation platform, and V7 offers just this.