Application of Deep Learning Algorithms in Segmentation of Mandibular Nerve Canal in Orthopantomogram (Panoramic) Radiographs: A State-Of-Art Systematic Review

Abstract

Objectives: To assess the current landscape and efficacy of artificial intelligence (AI) and deep learning (DL) algorithms in detecting and segmenting mandibular canals in orthopantomogram (panoramic) radiographs. Methods: Research on the detection and segmentation of the mandibular canal for developing AI models was conducted by searching five major electronic databases. The PICO question was, "Are 2D radiographic images suitable for utilizing deep learning algorithms to identify the infra-alveolar nerve?" The included studies adapted customized assessment criteria based on QUADAS-2 for quality assessments. Results: 255 records were identified during the initial electronic search. After a thorough evaluation, six studies specifically addressing the detection and segmentation of mandibular canals were selected for inclusion. Various outcome metrics were reported. The dice coefficient varies between 0.78 and 0.97 between models. Also, sensitivity (recall) varies from 0.83 to 0.99, indicating high performance in various DL models. Conclusion: The AI models discussed in the included studies vary in performance. Additionally, the outcome metrics reported were not consistent, making it difficult to compare all the deep learning (DL) models comprehensively. The impressive performance of these DL models should be evaluated using external datasets to compare their effectiveness and train them to achieve better results.