Dentists must know the precise location of the mandibular canal, which runs along both sides of the lower jaw and houses the alveolar nerve, to plan a dental implant procedure and determine the implant size and position.
Medical professionals use computer tomography (CT) models and X-rays to identify and diagnose the lower jaw’s anatomically complex structures. The mandibular canals are typically defined physically by dentists and radiologists from X-rays or CT scans, which makes the process difficult and time-consuming. Because of this, automating the process could do its job and simplify placing dental implants.
Researchers created a novel model that precisely and automatically displays the exact location of mandibular canals to address this issue. Deep neural networks are trained and used as the model’s foundation. The dataset used by the researchers to train the algorithm consisted of 3-D cone beam CT (CBCT) scans.
The model uses a fully convolutional design to maximize speed and data efficiency. According to the study’s findings, this kind of deep learning algorithm can precisely localize the mandibular canals. It outperforms statistical shape models, the most effective automated technique for locating mandibular canals up until now. The model is as accurate as a human expert in straightforward cases—when the patient has no special conditions, such as osteoporosis. It describes the vast majority of dental customers.
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