Role Of AI In Dentistry
healthcaretechoutlook

Role Of AI In Dentistry

Healthcare Tech Outlook | Friday, April 29, 2022

AI techniques can be utilized to advance the dental industry in many ways since diagnoses and treatment planning can be more precise.

Fremont, CA: Artificial intelligence, or AI, is a computer program's ability to learn specific patterns by teaching it to perform actions that mimic human learning and problem-solving abilities. Artificial neural networks (ANNs) and convolutional neural networks are two examples of AI in dentistry (CNNs). ANNs are used to identify patterns in data and then educate the machine to recognize them. 1 CNNs are used to analyze visual images and diagnose problems with them. These AI tools have the potential to improve dental care in a variety of ways.

Artificial intelligence can detect tooth decay and periodontal disease

Dental caries is usually discovered through a clinical examination of the teeth and a review of dental radiographs. While radiograph analysis provides initial objective assessments, tactile sense is typically used to detect tooth morphology, restoration margins, interproximal contacts, incipient decay, and recurrent decay. Diagnostic abilities can also differ depending on the dental provider's level of experience. In these situations, AI approaches can be beneficial because they have been found to give a more efficient diagnostic process when combined with clinical assessment. Using picture detection, classification, and segmentation, AI can improve dental quality.

Artificial intelligence can detect oral cancer

It's worth noting that neural networks are being used to evaluate photos of oral cancer lesions for early detection and diagnosis during dental hygiene appointments. Early detection is critical for oral cancer survival, hence the doctor must perform an oral cancer screening at each recall visit. If premalignant or potentially malignant lesions are detected early enough, malignant alterations may be avoided entirely or, at the very least, the odds of therapeutic success are increased. Mobile applications for image capture of oral lesions for remote diagnosis have been developed to aid in early diagnosis. 6 Similarly, deep learning will be employed for the system to discern between photographs with and without indicators of oral cancer, which is being integrated into these applications.

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