Understanding deep learning — challenges and prospects

Abstract

The developments in Artificial Intelligence have been on the rise since its advent. The advancements in this field have been the innovative research area across a wide range of industries, making its incorporation in dentistry inevitable. Artificial Intelligence techniques are making serious progress in the diagnostic and treatment planning aspects of dental clinical practice. This will ultimately help in the elimination of subjectivity and human error that are often part of radiographic interpretations, and willimprove the overall efficiency of the process. The various types of Artificial Intelligence algorithms that exist today make the understanding of their application quite complex. The current narrative review was planned to make comprehension of Artificial Intelligence algorithms relatively straightforward. The focus was planned to be kept on the current developments and prospects of Artificial Intelligence in dentistry, especially Deep Learning and Convolutional Neural Networks in diagnostic imaging. 
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An Artificial Intelligence model for implant segmentation on periapical radiographs

“…6 DL is a subset of AI that consist of neural networks that allow these models to train independently and learn various features with limited need for manual human input. 7 Various diagnostic DL models exist that aid dentists in teeth segmentation and numbering as well pathology detection on different types of radiographs used in clinical practice. 5 Further research into the detection of all possible findings on dental radiographs will help in improving the efficiency of diagnoses and treatment planning in everyday clinical settings.…”

“…8 These tasks have been carried out on a range of radiographs, including PA, Orthopantomograms (OPGs) and Cone Beam Computed Tomography (CBCT) images. 5,7,9 However, mere classification of dental implants has very limited application in helping dentists identify unknown implants placed outside of their practice in order to provide compatible restorations. 5,7 Additionally, all existing studies that perform implant classification as well detection use bounding boxes to localise them on radiographic images.…”

“…5,7,9 However, mere classification of dental implants has very limited application in helping dentists identify unknown implants placed outside of their practice in order to provide compatible restorations. 5,7 Additionally, all existing studies that perform implant classification as well detection use bounding boxes to localise them on radiographic images. 5,7 This technique of implant detection leads to incorporation of the background around implants, leading to a wider region of interest.…”

“…5,7 Additionally, all existing studies that perform implant classification as well detection use bounding boxes to localise them on radiographic images. 5,7 This technique of implant detection leads to incorporation of the background around implants, leading to a wider region of interest. 7 A more advanced technique that allows precise delineation of the region of interest is segmentation.…”

“…5,7 This technique of implant detection leads to incorporation of the background around implants, leading to a wider region of interest. 7 A more advanced technique that allows precise delineation of the region of interest is segmentation. [5][6][7] Therefore, for better localisation of implants via segmentation is better suited that identifies each pixel associated with the implant fixture for a more robust diagnostic process.…”