AI & ML for real time pathology detection in dentistry

In dentistry, what was once thought impracticable is now possible with the help of technology. All this has been possible through the introduction of artificial intelligence and machine learning in healthcare [1]. Recent advancements in computing infrastructure, machine learning, and digitized data collecting have allowed AI applications to spread into fields previously regarded to be domains of human expertise. These improvements in the field of dentistry have promised increased quality of life and improved decision-making [2].

X-rays are the most important part of developing an ideal treatment plan for the patient. The real-time pathology detection software integrated into the radiology system helps dentists identify various dental abnormalities in patients' radiographs. Since dental radiology is a regular part of a patient’s experience, the impact of AI through this detection software can be greatly appreciated by the dentist and the patients [3].

Since the fine interpretation of the X-rays is the first step in providing quality patient care, the adoption of this AI technology and machine learning will help you to deliver the necessary care.

One of the fast-growing companies that provide artificial intelligence solutions in dentistry is Pearl. Pearl’s AI through detection software offers a “Second opinion” platform which is the leading product today. The second opinion by Pearl is a computer vision platform that can detect various potential conditions in a radiograph. It can detect pathologies like incipient caries or early signs of radiolucency, which are difficult to spot on routine checks. This radiologic detection system is trained to detect bone loss, calculus, crowns, fillings, root canals, caries, periapical radiolucencies, widened periodontal ligaments, and many more.

It was noted that Pearl is using machine learning to analyze radiographs, helping insurance companies to identify if the same X-ray was used for more than one patient and if the recommended procedure was necessary. Pre-processors for generative adversarial networks improve images so that radiologic analysis can be carried out even when x-rays are deteriorated, overexposed or underexposed, or have digital artifacts [4].

Computer vision segmentation models distinguish individual teeth and supporting structures, which can give localizing information on the type and severity of detected problems. To determine the size of anatomical structures and the extent of deterioration, detection and segmentation capabilities work together in the Second opinion platform [4].

The integration of such software options into the clinical workflow and receiving the advantages of dental AI support help in creating a healthy environment where patients get the optimum care. These technologies make it easier for the dentist to interpret the X-rays, which helps in better communication and education of the patient.

References:

1. Machoy ME, Szyszka-Sommerfeld L, Vegh A, Gedrange T, Woźniak K. The ways of using machine learning in dentistry. Adv Clin Exp Med. 2020 Mar;29(3):375-384. doi: 10.17219/acem/115083. PMID: 32207586.

2. Nguyen TT, Larrivée N, Lee A, Bilaniuk O, Durand R. Use of Artificial Intelligence in Dentistry: Current Clinical Trends and Research Advances. J Can Dent Assoc. 2021 May;87:l7. PMID: 34343070.

3. Fatima A, Shafi I, Afzal H, Díez IT, Lourdes DRM, Breñosa J, Espinosa JCM, Ashraf I. Advancements in Dentistry with Artificial Intelligence: Current Clinical Applications and Future Perspectives. Healthcare (Basel). 2022 Oct 31;10(11):2188. doi: 10.3390/healthcare10112188. PMID: 36360529; PMCID: PMC9690084.

4. Artificial Intelligence in dentistry: Pearl’s Dental AI solutions Amol Nirgudkar

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