Artificial intelligence-assisted full-mouth radiograph mounting in dental education.

OBJECTIVES: With the increasing prevalence of artificial intelligence (AI) and the significant research gap in the application of AI within dentistry, this study aimed to (1) evaluate the efficiency and accuracy of dental students in full-mouth radiograph series (FMS) mounting with and without AI assistance, and (2) assess dental students' perceptions of AI in clinical education to address the impact of AI in dental education. METHODS: An AI-based interface for mounting radiographs on FMS templates was designed and implemented in the study. Forty third-year dental students were randomly assigned to control and test groups. The control group manually mounted FMS radiographs, while the test group reviewed AI-pre-mounted radiographs for adjustments. Students' performance in efficiency and accuracy was evaluated. Pre- and post-study surveys were conducted to gauge students' confidence levels and opinions regarding the usefulness of the AI-assisted program. RESULTS: The test group (using AI) demonstrated significantly faster radiograph mounting times than the control group (manual) (p < 0.05). Accuracy was lower in the test groups, when comparing AI-assisted and manual mounting of FMS (p < 0.01). Self-confidence and confidence in AI were consistent between the control and test groups, both before and after the study. CONCLUSION: Students with AI presented with a decreased accuracy in FMS radiograph mounting. Therefore, AI automation could potentially have negative impacts in a learning environment with inexperienced clinicians.

Application of deep machine learning for the radiographic diagnosis of periodontitis.

OBJECTIVE: Successful application of deep machine learning could reduce time-consuming and labor-intensive clinical work of calculating the amount of radiographic bone loss (RBL) in diagnosing and treatment planning for periodontitis. This study aimed to test the accuracy of RBL classification by machine learning. MATERIALS AND METHODS: A total of 236 patients with standardized full mouth radiographs were included. Each tooth from the periapical films was evaluated by three calibrated periodontists for categorization of RBL and radiographic defect morphology. Each image was pre-processed and augmented to ensure proper data balancing without data pollution, then a novel multitasking InceptionV3 model was applied. RESULTS: The model demonstrated an average accuracy of 0.87 ± 0.01 in the categorization of mild (< 15%) or severe (≥ 15%) bone loss with fivefold cross-validation. Sensitivity, specificity, positive predictive, and negative predictive values of the model were 0.86 ± 0.03, 0.88 ± 0.03, 0.88 ± 0.03, and 0.86 ± 0.02, respectively. CONCLUSIONS: Application of deep machine learning for the detection of alveolar bone loss yielded promising results in this study. Additional data would be beneficial to enhance model construction and enable better machine learning performance for clinical implementation. CLINICAL RELEVANCE: Higher accuracy of radiographic bone loss classification by machine learning can be achieved with more clinical data and proper model construction for valuable clinical application.

Impact of explainable artificial intelligence assistance on clinical decision-making of novice dental clinicians.

Objective: Despite artificial intelligence (AI) being used increasingly in healthcare, implementation challenges exist leading to potential biases during the clinical decision process of the practitioner. The interaction of AI with novice clinicians was investigated through an identification task, an important component of diagnosis, in dental radiography. The study evaluated the performance, efficiency, and confidence level of dental students on radiographic identification of furcation involvement (FI), with and without AI assistance. Materials and Methods: Twenty-two third- and 19 fourth-year dental students (DS3 and DS4, respectively) completed remotely administered surveys to identify FI lesions on a series of dental radiographs. The control group received radiographs without AI assistance while the test group received the same radiographs and AI-labeled radiographs. Data were appropriately analyzed using the Chi-square, Fischer's exact, analysis of variance, or Kruskal-Wallis tests. Results: Performance between groups with and without AI assistance was not statistically significant except for 1 question where tendency was to err with AI-generated answer (P < .05). The efficiency of task completion and confidence levels was not statistically significant between groups. However, both groups with and without AI assistance believed the use of AI would improve the clinical decision-making. Discussion: Dental students detecting FI in radiographs with AI assistance had a tendency towards over-reliance on AI. Conclusion: AI input impacts clinical decision-making, which might be particularly exaggerated in novice clinicians. As it is integrated into routine clinical practice, caution must be taken to prevent overreliance on AI-generated information.

Implications of video chat use for young children's learning and social-emotional development: Learning words, taking turns, and fostering familial relationships.

Parents of young children use video chat differently than other screen media, paralleling expert recommendations (e.g., American Academy of Pediatrics Council on Communications and Media, 2016), which suggest that video chat, unlike other screen media, is acceptable for use by children under 18 months. Video chat is unique among screen media in that it permits contingent (time-sensitive and content-sensitive) social interactions. Contingent social interactions take place between a child and a partner (dyadic), with objects (triadic), and with multiple others (multi-party configurations), which critically underpin development in multiple domains. First, we review how contingent social interaction may underlie video chat's advantages in two domains: for learning (specifically learning new words) and for social-emotional development (specifically taking turns and fostering familial relationships). Second, we describe constraints on video chat use and how using chat with an active adult (co-viewing) may mitigate some of its limitations. Finally, we suggest future research directions that will clarify the potential advantages and impediments to the use of video chat by young children. This article is categorized under: Linguistics > Language Acquisition Psychology > Learning Cognitive Biology > Social Development.

Oral Manifestations of Commonly Prescribed Drugs.

Drugs are being prescribed with more frequency and in higher quantities. A serious adverse drug event from prescribed medications constitutes 2.4% to 16.2% of all hospital admissions. Many of the adverse drug events present intraorally or periorally in isolation or as a clinical symptom of a systemic effect. Clinical recognition and treatment of adverse drug events are important to increase patient adherence, manage drug therapy, or detect early signs of potentially serious outcomes. Oral manifestations of commonly prescribed medications include gingival enlargement, oral hyperpigmentation, oral hypersensitivity reaction, medication-related osteonecrosis, xerostomia, and other oral or perioral conditions. To prevent dose-dependent adverse drug reactions, physicians should prescribe medications judiciously using the lowest effective dose with minimal duration. Alternatively, for oral hypersensitivity reactions that are not dose dependent, quick recognition of clinical symptoms associated with time-dependent drug onset can allow for immediate discontinuation of the medication without discontinuation of other medications. Physicians can manage oral adverse drug events in the office through oral hygiene instructions for gingival enlargement, medication discontinuation for oral pigmentation, and prescription of higher fluoride toothpastes for xerostomia.

Assessing the content, presentation, and readability of dental informed consents.

Informed consents are important aids in helping patients make optimal decisions. Little knowledge exists about the quality of dental informed consents. Fifty-two informed consents used throughout the University of Texas Health Science Center at Houston Dental Branch were evaluated based on the quality of their content, readability, and presentation. Content quality was judged on four basic elements: description of procedure, risk, benefits, and alternatives. Of the clinical consents, 26 percent of forms contained all four of the basic content elements, 48 percent contained three of four elements, 16 percent contained two of four elements, and 10 percent contained one of four elements. Presentation quality was judged on twelve criteria items. The average clinical consent included seven out of twelve presentation items, and the average nonclinical consent included eight out of twelve items. Readability was judged using three standard instruments for rating readability: Flesch Reading Ease, Flesch-Kincaid Grade-Level, and Simple Measure of Gobbledygook (SMOG) grading. Average Flesch-Kincaid Grade-Level was 12.7 (range, 7.4 to 19.1), significantly higher than the recommended ninth grade level (p<.001). The results suggest that many existing dental informed consents may be improved by 1) increasing the comprehensiveness of the content, 2) improving the design and layout, and 3) reducing the readability levels for patient comprehension.