Robert Prashanth Anthonappa

Background: Oral health‐related conditions are a leading and preventable cause of paediatric hospital presentations in Australia, contributing substantially to healthcare expenditure and placing increasing pressure on public hospital infrastructure. Preschool‐aged children are disproportionately affected, often presenting late with advanced disease requiring resource‐intensive care, including emergency extractions under general anaesthesia. In Western Australia, these challenges are further compounded by geographic barriers that limit timely access to public dental services. Methods: This study provides longitudinal data on dental‐related emergency presentations at Perth Children's Hospital (PCH) and its predecessor, Princess Margaret Hospital for Children (PMH), over six years (2016–2021). The primary aim was to characterise the nature, volume, and demographic profile of unplanned emergency presentations associated with odontogenic infection. Results: Emergency presentations for odontogenic infections accounted for 46% of all presentations to the dental department. Children under five years accounted for nearly 60% of these cases, demonstrating a pronounced age‐related disparity. A consistent year‐on‐year increase was observed in emergency presentations, with over 30,000 teeth being extracted, the majority of these requiring general anaesthesia. Posterior primary teeth comprised the majority of extractions, likely reflecting advanced, untreated early childhood caries. Conclusions: These findings highlight a persistent and escalating burden of preventable disease presenting at a late stage, where care is more invasive, costly, and resource‐intensive. Without strategic investment in early, accessible, and preventive models of care, increasing demand will continue to strain hospital‐based services and divert resources from other critical areas of paediatric healthcare.

Introduction: Artificial intelligence is emerging in dental education, but its use in preclinical assessment remains limited. Large language models like ChatGPT^® V4.5 enable non-programmers to build AI models through real-time guidance, addressing the coding barrier. Aim: This study aims to empower clinician-led, low-cost, AI-driven assessment models in preclinical restorative dentistry and to evaluate the technical feasibility of using a neural network to score 3D cavity preparations. Methods: Twenty mandibular molars (tooth 46), each with two carious lesions, were prepared and scored by two expert examiners using a 20-point rubric. The teeth were scanned with a Medit i700^® and exported as .OBJ files. Using Open3D, the models were processed into point clouds. With ChatGPT’s guidance, the clinician built a PointNet-based neural model in PyTorch, training it on 20 cases and testing it on 10 unseen preparations. Results: In training, the model achieved an MAE of 0.82, RMSE of 1.02, and Pearson’s r = 0.88, with 66.7% and 93.3% of the predictions within ±5% and ±10% of the examiner scores, respectively. On the test set, it achieved an MAE of 0.97, RMSE of 1.16, and r = 0.92, with 50% and 100% of scores within ±5% and ±10%, respectively. These results show a strong alignment with examiner scores and an early generalizability for scoring preclinical cavity preparations. Conclusions: This study confirms the feasibility of clinician-led, low-cost AI development for 3D cavity assessment using ChatGPT, even without prior coding expertise.

Background/Objectives: This scoping review aims to map existing AI applications in dental education, in student learning, assessment, and diagnostic training, identifying key limitations and challenges. Methods: Following the Arksey and O’Malley framework and PRISMA-ScR guidelines, six databases were searched in March 2025 using combinations of the following search words: “dental education,” “artificial intelligence,” “machine learning,” and “student assessment”. Inclusion was limited to English-language empirical studies focused on dental student education. Of 547 identified studies, 17 met the inclusion criteria. They were categorized into four domains: (1) Preclinical Training, (2) AI in Clinical, Diagnostic Training, and Radiographic Interpretation, (3) AI as an Assessment Tool and Feedback System, and (4) AI in Content Generation for Dental Education. Results: AI has positively influenced various domains, enhancing procedural accuracy, diagnostic confidence, assessment efficiency, and content delivery. However, it struggles to assess nuanced competencies like dexterity and clinical judgment. The challenges faced include disparate definitions of AI, ethical and privacy concerns, model variability, and a deficiency of dental leadership in AI development. At present, most tools are engineered by computer scientists and may not align effectively with the priorities of dental education. Conclusions: AI holds significant potential to enhance dental education outcomes. However, to guarantee its relevance and reliability, it requires standard frameworks, ethical oversight, and clinician-led development. Future research should concentrate on implementing real-time AI-driven feedback systems during preclinical training and advocate for more precise definitions to support consistent AI application and evaluation in dental education.

Background/Objectives: This scoping review aims to map existing AI applications in dental education, in student learning, assessment, and diagnostic training, identifying key limitations and challenges. Methods: Following the Arksey and O’Malley framework and PRISMA-ScR guidelines, six databases were searched in March 2025 using combinations of the following search words: “dental education,” “artificial intelligence,” “machine learning,” and “student assessment”. Inclusion was limited to English-language empirical studies focused on dental student education. Of 547 identified studies, 17 met the inclusion criteria. They were categorized into four domains: (1) Preclinical Training, (2) AI in Clinical, Diagnostic Training, and Radiographic Interpretation, (3) AI as an Assessment Tool and Feedback System, and (4) AI in Content Generation for Dental Education. Results: AI has positively influenced various domains, enhancing procedural accuracy, diagnostic confidence, assessment efficiency, and content delivery. However, it struggles to assess nuanced competencies like dexterity and clinical judgment. The challenges faced include disparate definitions of AI, ethical and privacy concerns, model variability, and a deficiency of dental leadership in AI development. At present, most tools are engineered by computer scientists and may not align effectively with the priorities of dental education. Conclusion: AI holds significant potential to enhance dental education outcomes. However, to guarantee its relevance and reliability, it requires standard frameworks, ethical oversight, and clinician-led development. Future research should concentrate on implementing real-time AI-driven feedback systems during preclinical training and advocate for more precise definitions to support consistent AI application and evaluation in dental education.

Purpose Mobile health (mHealth) applications (apps) have emerged as powerful tools for enhancing clinical decision-making, knowledge dissemination, and proactive care in traumatic dental injuries (TDIs). This systematic review aims to collate and assess the available evidence on the usefulness of mobile apps in (1) diagnosis, (2) prevention, (3) management, and (4) education of TDIs. Methods This review examined studies published between January 2012 and August 2024, focusing on randomised controlled trials (RCTs), clinical trials, cohort studies, and cross-sectional studies that assessed the effectiveness of mobile health apps in the management of TDI care. Study quality was assessed using the Cochrane Risk of Bias Tool 2.0 for RCTs and the modified Newcastle–Ottawa Scale for cross-sectional studies. The GRADE approach was used to assess the certainty of the evidence. Results Sixteen studies met the inclusion criteria, including 12 cross-sectional studies and 4 RCTs. The cross-sectional studies primarily assessed knowledge improvement, while the RCTs focused on skill development and practical applications. Quality assessments indicated notable methodological concerns. All four RCTs were rated as high risk of bias due to inadequate randomisation, lack of blinding, and participant attrition. According to the GRADE approach, the overall certainty of the evidence was judged to be very low. Conclusion While mobile health applications such as ToothSOS, Dental Trauma, and Injured Tooth show promise in improving knowledge and decision-making related to TDIs, the evidence is limited by high risk of bias and low quality. Future studies should focus on rigorous RCTs and explore AI integration to enhance effectiveness.

Introduction: Aplastic anemia (AA) is a rare condition that frequently manifests with pancytopenia. Management of severe disease is through either allogenic stem cell transplantation or immunosuppressive therapy with supportive care. Drug‐induced gingival overgrowth (DIGO) is a potential complication of a number of medications, including cyclosporine and amlodipine. Case Report: The following report presents the case of a male child who was diagnosed with severe AA at 7 years of age. He was managed with immunosuppressive therapy and developed DIGO as a side effect of treatment, exacerbated by poor oral hygiene. Nonsurgical and surgical management strategies were utilized. Gingival debulking surgery was completed at 9 years of age, once the patient's condition had stabilized. Postoperative care included regular dental prophylaxis and oral hygiene instruction. The patient's medical condition remained stable, and his oral health was maintained without relapse of DIGO. Conclusion: This case emphasizes the importance of coordinated care for managing DIGO in immunocompromised pediatric patients and describes the successful management of DIGO using both nonsurgical and surgical methods.

Purpose: This systematic review aims to consolidate existing genetic and clinical data on non-syndromic dentinogenesis imperfecta (DI) to enhance understanding of its etiology. Methods: Electronic databases were searched for genetic familial linkage studies published in English without time restrictions. Genetic familial linkage studies that reported cases of Shield’s classifications: DI-II, DI-III or DD-II were included. After removing duplicates and excluding non-eligible articles, two reviewers screened relevant articles independently, followed by data extraction. Results: The systematic search identified 3475 articles, with 135 suitable for full-text review and a final 41 that met inclusion criteria. Within this set of studies, 10 conducted a histopathologic examination of teeth from affected participants. DSPP mutations were the most frequently reported, with 59 documented mutations. Four studies identified mutations in COL1A1 and COL1A2, revealing non-syndromic DI cases, predominantly in individuals of Asian descent. Histopathological analysis of affected teeth showed variations in pulp chamber size, dentinal tubule irregularities, enamel malformations, and mineral density reductions, depending on DI phenotype. Conclusions: This review consolidates genetic and clinical data to advance the understanding of non-syndromic DI. It highlights the role of DSPP, COL1A1 and COL1A2 and the potential involvement of other genes, emphasizing the effectiveness of whole-exome sequencing in identifying causative mutations.

Dental caries (tooth decay) is a disease with a significant global burden. Management may necessitate the extraction of teeth to restore oral health. The association between dental extractions and children's speech is unclear, with clinical implications for speech-language pathologists and dentists. This case series describes a prospective study reporting the impact of primary maxillary incisor teeth extraction on speech sound accuracy for three children (C1 aged 5;6 (years; months), C2 aged 4;6, C3 aged 3;10). Their speech was assessed using the Diagnostic Evaluation of Articulation and Phonology (DEAP) and the Intelligibility in Context Scale (ICS) before (T1) and 1 month after dental treatment (T2). Speech analysis included the percentage of consonants correct (PCC) and error-type analyses. Caregiver and child perception of the child's oral health-related quality of life (OHRQoL) were assessed pre- and post-operatively using a modified Scale of Oral Health Outcomes for 5-year-old children (SOHO-5). At T1, all three children scored >1 standard deviation below the mean on normative data in the DEAP. There was no clinically significant change in PCC for any child (C1 T1: 89.6%, T2: 90.6%, C2 T1: 78.0%, T2: 75.9%, C3 T1: 56.1%, T2: 63.1%). OHRQoL measures were improved for C1 by the carergiver report and remained stable for C2 and C3 and all child reports. Speech sound difficulties were present before dental treatment in all participants and extraction of primary maxillary incisors did not significantly impact speech production. Dental extractions appear to be independent from speech production in this case series of preschool children.

Background: Limitations in traditional caries detection tools have driven the development of alternatives methods, focused on the early lesion detection such as near‐infrared digital imaging transillumination (NIDIT).Aim: The aim of this study was to evaluate the performance of NIDIT compared with bitewing radiography (BWR) in the detection of interproximal carious lesions in children.Design: A retrospective audit of data from children who had NIDIT, BWR and intraoral photographs was conducted. Carious lesions were scored on a tooth surface level with BWR acting as the primary reference for comparison. Accuracy was determined using multi‐class area under the curve (AUC), and correlation was determined using Fleiss' Kappa.Results: Data from 499 tooth surfaces involving 44 children were included in this study. The average age across the participants was 86 months (~7 years) with an average dmft (decayed, missing and filled teeth in primary dentition) of 5.29. Multi‐class AUC comparing NIDIT to BWR was 0.70. The correlation between NIDIT and BWR was moderate (0.43), whereas the correlation between photographic examination and BWR was 0.30, which is fair.Conclusion: When compared to BWR, NIDIT showed a high specificity but a low sensitivity for proximal caries detection in primary teeth.