Digital Dentistry. Группа авторов

sample size, which can help researchers to work with small samples and avoid overfitting. Overfitting occurs when the intrinsic data of a specific sample are prioritized; thus, the performance of the model for the images not belonging to the sample ends up being much lower than the performance related to the sample used for training.

Several authors have reported high precision using NNs for object detection in the classification of dental elements [17], periodontal disease [18], dental caries [19], apical lesions [20], cystic lesions and tumors [21, 22], dental fracture diagnoses [23], and sinusitis in the maxillary sinus [24], among other factors, with the use of deep CNNs applied to panoramic radiographs and digital periapical radiographs. However, although methods for object detection via deep CNNs are rapidly progressing, object detection can still be challenging, as it is dependent on a large database and computational processing [25, 26].

Object detection software using AI already exists. Second Opinion^®, for example (Hello Pearl, Los Angeles, USA), is an AI‐driven assistant for diagnostics and treatment planning. The software offers a computer vision platform that can instantly detect dozens of common pathologies (Figures 1.5–1.8).

      In addition to object detection in radiographic analysis, NNs have been used for automatic identification. Examples include comparison between antemortem and postmortem panoramic radiographs (human postmortem identification) [25], diagnosis of osteoporosis on panoramic radiographs [26–28], and malocclusion diagnosis [29, 30].

      Another important possibility with the use of CNNs is the automation of identification and selection of structures in CBCT DICOM images. With this method, automated mandibular canal detection [31] and automated mandible segmentation [32], an approach to dental implant planning [33], are possible.

Figure 1.5 Screen capture of the software Hello Pearl (Los Angeles, USA) showing automatic detection of a maxillary area with bone loss.

Figure 1.6 Screen capture of the software Hello Pearl (Los Angeles, USA) showing automatic detection of a mandibular area with bone loss.

Figure 1.7 Screen capture of the software Hello Pearl (Los Angeles, USA) showing automatic detection of marginal discrepancy of a metallic restoration.

Figure 1.8 Screen capture of the software Hello Pearl (Los Angeles, USA) showing automatic detection of calculus.

References

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