Objective: To assess the quality, reliability, readability, and similarity of the data that a recently created NLP-based artificial intelligence model ChatGPT 4 provides to users in Cleft Lip and Palate (CLP)-related information. Design: In the evaluation of the responses provided by the OpenAI ChatGPT to the CLP-related 50 questions, several tools were utilized, including the Ensuring Quality Information for Patients (EQIP) tool, Reliability Scoring System (Adapted from DISCERN), Flesh Reading Ease Formula (FRES) and Flesch-Kinkaid Reading Grade Level (FKRGL) formulas, Global Quality Scale (GQS), and Similarity Index with plagiarism-detection tool. Jamovi (The Jamovi Project, 2022, version 2.3; Sydney, Australia) software was used for all statistical analyses. Results: Based on the reliability and GQS values, ChatGPT demonstrated high reliability and good quality attributable to CLP. Furthermore, according to the FRES results, ChatGPT's readability is difficult, and the similarity index values of this software exhibit an acceptable level of similarity ratio. There is no significant difference in EQIP, Reliability Score System, FRES, FKGRL, GQS, and Similarity Index values among the two categories. Conclusion: OpenAI ChatGPT provides a highly reliable, high-quality, but challenging to read, and acceptable similarity rate in providing information related to CLP. Ensuring that information obtained through these models is verified and assessed by a qualified medical expert is crucial.

Undefined - Turkish Journal of Orthodontics

Objective: The aim of this study was to develop convolutional neural network (CNN)-based systems to diagnose calculus, plaque, gingival hyperplasia and gingival inflammation in intraoral images from orthodontic patients. Materials and methods: A dataset of 1,000 lateral and frontal intraoral images from orthodontic patients was used to develop CNN-based models. Periodontology specialists annotated areas of dental calculus, plaque, gingival inflammation, and gingival hyperplasia on the teeth and gingiva. The dataset was divided into training (80%), validation (10%), and test (10%) sets for model development. The YOLOv8 and hybrid U-Net + ResNet50 models were examined. Their performance was evaluated on the basis of accuracy, precision, recall, F ₁ score, Tversky loss, intersection over union, mean average precision, Dice coefficient, and Cohen’s kappa. Results: The mean classification accuracy was 0.96 for the YOLOv8 model and 0.93 for the U-Net + ResNet50 model. On the basis of the Dice coefficient, the models performed best in detecting gingival hyperplasia (YOLOv8: 0.78, U-Net + ResNet50: 0.79) and worst in detecting dental calculus (YOLOv8:0.48, U-Net + ResNet50:0.53). Cohen’s kappa coefficient was highest for classifying gingival hyperplasia (YOLOv8: 0.785, U-Net + ResNet50: 0.790). The precision exceeded 0.72 across all the classifications, with the greatest precision in classifying gingival inflammation. Conclusion: Deep learning-based systems can serve as decision support tools by offering rapid and objective evaluations of dental calculus, plaque, gingival inflammation, and gingival hyperplasia. Nonetheless, the definitive diagnostic conclusion should be based on the clinician’s specialized expertise and professional judgment. Clinical relevance: The integration of CNN-based diagnostic models into clinical workflows has the potential to facilitate early periodontal diagnosis and improve accessibility to periodontal assessments in orthodontic patients.