Developments and Performance of Artificial Intelligence Models Designed for Application in Endodontics: A Systematic Review

Abstract

Technological advancements in health sciences have led to enormous developments in artificial intelligence (AI) models designed for application in health sectors. This article aimed at reporting on the application and performances of AI models that have been designed for application in endodontics. Renowned online databases, primarily PubMed, Scopus, Web of Science, Embase, and Cochrane and secondarily Google Scholar and the Saudi Digital Library, were accessed for articles relevant to the research question that were published from 1 January 2000 to 30 November 2022. In the last 5 years, there has been a significant increase in the number of articles reporting on AI models applied for endodontics. AI models have been developed for determining working length, vertical root fractures, root canal failures, root morphology, and thrust force and torque in canal preparation; detecting pulpal diseases; detecting and diagnosing periapical lesions; predicting postoperative pain, curative effect after treatment, and case difficulty; and segmenting pulp cavities. Most of the included studies (n = 21) were developed using convolutional neural networks. Among the included studies. datasets that were used were mostly cone-beam computed tomography images, followed by periapical radiographs and panoramic radiographs. Thirty-seven original research articles that fulfilled the eligibility criteria were critically assessed in accordance with QUADAS-2 guidelines, which revealed a low risk of bias in the patient selection domain in most of the studies (risk of bias: 90%; applicability: 70%). The certainty of the evidence was assessed using the GRADE approach. These models can be used as supplementary tools in clinical practice in order to expedite the clinical decision-making process and enhance the treatment modality and clinical operation.

Impact of covid-19 on education, psychological wellness and life style of dental students in Saudi Arabia

“…The switch to online and those that were originally not designed for online delivery resulted in increased psychological distress among students [ 2 ]. Majority of the respondents were females (79.4%, 55.6%, 65%, 54.1%) in previous studies [ 2 , 16 , 17 ], whereas, in our present study males (56.2%) were in majority than females (43.8%). Majority (25.7 %) stayed at home with family, similar to the present study (34.3%).…”

“…Those stayed with families reported high levels of anxiety than others in these studies, which contradicts the present study, in which anxiety scores were high among those students who stayed with their friends. Studies in Saudi Arabia [ 17 ] reported depression, anxiety, and fear among dental interns. Accommodation, marital status and gender did not influence the psychological health of the participants which contradicts the present study in which females reported increased levels of depression, anxiety and stress than males and accommodation of students with their friends caused more anxiety than others.…”

Artificial intelligence for radiographic imaging detection of caries lesions: a systematic review

“…Since the advent of DL, it has been suggested for various applications in the field of oral and dental health, such as tooth classification, detection and segmentation [ 9 , 10 ], endodontic treatment and diagnosis [ 11 ], periodontal problem tooth [ 12 ] and oral lesion pathology detection [ 13 ].…”

Evaluating a Periapical Lesion Detection CNN on a Clinically Representative CBCT Dataset—A Validation Study

“…Recently, dental medicine has also started to benefit from such deep learning techniques [ 8 ]. Specifically, these techniques have been applied to panoramic radiographs and cone-beam computed tomography (CBCT) images with the aim of assisting clinicians in detecting and analyzing dental conditions and diseases in the maxillofacial region [ 9 , 10 , 11 ]. Examples include the detection of maxillary sinus mucosa [ 12 ], pharyngeal airway space [ 13 ], calcifications of the cervical carotid artery [ 14 ], jaw cysts [ 15 , 16 ], supernumerary mesio-buccal root canals on maxillary molars [ 17 ], vertical root fractures [ 18 ], and periapical lesions (PALs).…”