A deep learning algorithm for classification of oral lichen planus lesions from photographic images: A retrospective study

Detection of Tooth Numbering, Frenulum, Gingival Hyperplasia and Gingival Inflammation on Dental Photographs Using Convolutional Neural Network Algorithms: An Initial Study

“…As a result of this study, they had reported that AI systems have a performance comparable to the specialist physician in determining the relevant pathology, and that the system performs signi cantly better than an average medical student [26]. Similarly, Keser et al (2021), in their study, had tried to determine the oral lichen planus lesions related using inception v3 architecture (an AI system) on 65 intraoral photographs of healthy induviduals and 72 patients with oral lichen planus [43]. They also had reported that the AI system used in this study was 100% successful in detecting the presence/absence of the lesion, that is, in its classi cation [43].…”

“…In the literature, detection of lesions such as squamous cell carcinoma [26], lichen planus [43] using AI systems in intraoral photographs; detection of dental applications such as dental prostheses, restorations and ssure sealants [29,44,45]; in addition, there are many studies on the detection of conditions such as dental caries [28, 46], white spot [18], and anomalies such as microdontia, rotation, and supernumerary [47]. All of these studies about AI, which has attracted great interest in dentistry in recent years, support the usability of these systems for intraoral photographs and dental cameras in the dental eld in the coming years.…”

“…Similarly, Keser et al (2021), in their study, had tried to determine the oral lichen planus lesions related using inception v3 architecture (an AI system) on 65 intraoral photographs of healthy induviduals and 72 patients with oral lichen planus [43]. They also had reported that the AI system used in this study was 100% successful in detecting the presence/absence of the lesion, that is, in its classi cation [43]. 2021) had found that metallic colored restorations were recognized by the ai-systems with a higher accuracy rate, as a result of their study on prosthesis and restoration detection using AI systems (This study was carried out on 1904 dental photographs) [29].…”

Decoding AI Curricular Integration: Navigating Course Quality in Brazilian Dental Master's Programs

“…The implications of AI integration in dental education are profound. AI empowers dental professionals with real-time patient information, enhancing the personalization and efficiency of treatments (Agrawal & Nikhade, 2022;Chau et al, 2022;Engels et al, 2022;Keser et al, 2023;Khanagar et al, 2021;Lee et al, 2022;Mine et al, 2022;Sakai et al, 2023). Dental graduates who are well-versed in AI concepts will be better equipped to harness its potential for improving patient care (Schwendicke et al, 2020;Schwendicke et al, 2023) AI should be used to support clinical decision-making, but it should not replace the clinical judgment of the professional or student (Agrawal & Nikhade, 2022;Schwendicke et al, 2023).…”

Bionic Artificial Neural Networks in Medical Image Analysis

“…Classification [ 15 ] uses machine learning algorithms to classify or predict a diagnosis or outcome based on the features extracted from the image data. Detection [ 16 ] locates and identifies specific structures or abnormalities within an image, such as tumors, lesions, or fractures.…”