Peter Stoustrup

Background: Understanding the psychological and professional consequences of patient harm events is crucial for improving clinical practice and support systems. Aim: (1) To investigate the prevalence of self‐reported patient harm events involving orthodontic professionals, (2) to assess the psychological, physical, and professional impact of these events, and (3) to identify aspects of importance to mitigate the psychological and physical impact among orthodontic care providers involved in patient harm events. Materials & Methods: An online questionnaire‐based survey was conducted among orthodontic care providers, including orthodontists, dentists, hygienists, and dental assistants during two orthodontic conferences in Denmark. The questionnaire assessed respondents' experience with patient harm events, types of harm, the psychological and physical impact of being involved, and their working environment. Results: A total of 297 participants completed the survey, in which 45.9% ( n = 135) reported involvement in a patient harm event and 20.8% ( n = 61) experienced an event within the past year. Following a harmful event, significant psychological distress was reported by 49.2% ( n = 87) of professionals, while 21.5% ( n = 38) experienced physical symptoms and 8.8% ( n = 16) considered leaving their job. A fear of causing harm was also evident, with 32.4% ( n = 95) participants experiencing high to very high levels of psychological stress. Orthodontists reported greater stress related to the risk of causing harm compared to other orthodontic team members ( p = 0.001). Orthodontists involved in a patient harm event also reported greater psychological ( p = 0.002) and physical impact ( p = 0.001) than other clinical staff members. No significant differences were found based on age, gender or years of clinical experience. Respondents most frequently identified structured organizational support, supportive leadership, and peer support as key measures to mitigate the impact of patient harm events. Conclusion: Patient harm events may affect orthodontists and staff and potentially cause significant psychological and physical impact, emphasizing the need for structured support and peer programmes to enhance provider well‐being and patient safety.

Artificial intelligence (AI) is increasingly being integrated into orthodontic research and clinical practice. This review paper consists of three parts and aims to establish a foundational understanding of AI concepts for readers from non‐technical backgrounds while familiarising them with its applications and challenges within the field of orthodontics. The first part provides a comprehensive introduction to the basic concepts of AI, including definitions of machine learning and deep learning models, alongside various learning types: Supervised, unsupervised, and reinforcement learning. To explain how learning occurs and how to optimise it during model training, key features of the “learning” process are introduced. A detailed overview of supervised learning is presented, including data preparation, model selection, and performance evaluation. Following this introduction, different architectures and algorithms of machine learning and neural networks are examined, with examples of their applications in orthodontics, including benefits for clinical decision‐making, image processing, and patient monitoring. Finally, this review will consider innovations in AI that hold promise for enhancing orthodontic practice, such as the use of synthetic data, transformers, generative models, and large language models.

Artificial intelligence (AI) is increasingly affecting orthodontic practice and research, yet its adoption requires a careful understanding of both its capabilities and its limitations. As the final part of a three‐section review, this paper focuses on the practical cautions and constraints that accompany current AI developments in orthodontics. Topics such as data quality, educational preparedness and the ethical and legal responsibilities surrounding AI use are discussed as essential considerations for preventing errors, misuse and misinterpretation. Technical challenges, including model generalizability, transparency and interpretability, are elucidated in relation to their impact on clinical applicability. Finally, new trends and future directions in the evolution of AI within orthodontics and other fields are described. Together, these perspectives aim to provide a realistic and balanced perspective of how AI can be integrated responsibly into orthodontic care and research.

Background: Patient safety, defined as the absence of avoidable harm, in orthodontics is still in its early stages of development. Knowledge on the subject is limited. Aim: (1) To conduct an incident analysis, (2) to measure avoidability, (3) to investigate the relationship between contributory factors and perceived avoidability of harm from orthodontic care, and (4) to suggest methods to mitigate potential patient harm incidents. Materials and Methods: Data were retrospectively collected from consecutively filed orthodontic‐related claims from the Danish Dental Compensation Association (DDCA) and included all approved‐for‐compensation claims from September 2019 to August 2024. Reason for filing the claim, harm category, and harm‐causing events were recorded. Incident analysis was conducted for each claim based on the Eindhoven criteria and AI‐assisted qualitative analysis. A large language model was used to identify and interpret the interplay between the contributory factors. Perceived avoidability of harm was based on a 6‐point rating system. Results: A total of 125 claims of approved patient harm were included and reviewed. Based on the Eindhoven incident analysis, the most frequent root causes were human related factors (monitoring, verification and clinical knowledge among others) followed by organizational factors (management priorities, protocols and workplace culture), illustrating complex interrelationships. The majority of claims (70.1%, n = 82) were avoidable harm, being rated as either probably avoidable in more than 50% of cases, high certainty of avoidability, or completely avoidable . While low‐avoidability harm events were characterized by few contributory factors, high‐avoidability cases were characterized by the involvement of numerous human and organizational failures. Conclusion: The analysis of 125 compensated orthodontic harm cases showed that 70.1% of the cases were deemed avoidable and most harms were multifactorial in nature, with human and organizational contributors playing the primary role. This multifactorial pattern provides new insights and forms a foundation for future patient‐safety initiatives in orthodontic practice and research.

Artificial intelligence (AI) is increasingly integrated into orthodontic research and clinical workflows. Yet, the reliability and clinical value of these systems depend fundamentally on the quality of the data used to train them and the rigour with which their performance is evaluated. Continuing from Part 1 of this 3‐part review, this article provides a practical, implementation‐focused overview of data preparation and performance evaluation for orthodontic AI applications. Key considerations for AI models are highlighted to ensure that datasets are representative, reproducible, and clinically meaningful. Various strategies in data processing are presented to enhance generalizability while maintaining patient privacy. This review then focuses on interpreting model performance, detailing the strengths and limitations of common metrics for classification, regression, and segmentation tasks. Key concepts are discussed to guide researchers in evaluating model robustness across varied populations and imaging conditions. Finally, the article discusses the real‐world utility of trustworthy, well‐validated AI systems that can advance orthodontic diagnostics, planning, and research.

Background: Patient safety is a fundamental prerequisite for high‐quality healthcare and central to clinical practice, including orthodontics. Despite the widespread emphasis on safety in healthcare delivery, knowledge specific to patient safety in orthodontics remains limited. Aims: (1) To analyse the reasons why patients file claims for compensation related to orthodontic care, (2) to summarise the events that lead to patient harm, and (3) to describe the types of harms associated with the provision of orthodontic care. Materials & Methods: Data from the Danish Dental Compensation Association (DDCA), a national compensation scheme established by law, were retrospectively extracted from the electronic database and included all patient‐reported claims between June 2017 and August 2024 related to orthodontic care. Results: A total of 644 patient‐reported orthodontic‐related claims were reviewed. Of these claims, 196 (30.4%) were recognized as patient harm and led to compensation. Orthodontic treatment was the most frequently reported source of harm (51.5%, n = 101), followed by delayed diagnosis (24.5%, n = 48) and retention‐related complications (9.2%, n = 18). Among the 196 approved claims, the most common harm was apical root resorption leading to tooth loss (14.8%, n = 29), followed by gingival or soft‐tissue injury (13.3%, n = 26), worsening of occlusion after treatment (9.2%, n = 18), and other harm such as diagnostic delays causing tooth damage (8.2%, n = 16). Conclusion: Harm events can occur at any stage during the orthodontic treatment process, including the diagnostic and planning stage. Orthodontic harm mostly occurs in the active phase of treatment and is frequently a result of root resorption, soft tissue damage, and worsening of the occlusion. There is a discrepancy in patient‐perceived harms and actual harm events that lead to compensation. This study highlights the importance of thorough risk assessment, optimal diagnostics and planning, clinical vigilance throughout treatment, and clear communication regarding potential side effects and patient concerns, particularly where perceptions of harm and compensability diverge.

Objective: Unilateral craniofacial microsomia (UCM) presents varying unilateral deficiencies of the temporomandibular joint, mandibular ramus, masticatory muscles, and adjacent soft tissues and ear. Reduced mandibular and maxillary dimensions may decrease upper airway volume and may increase the risk of sleep‐disordered breathing (SDB). This retrospective pilot study aimed to evaluate a novel three‐dimensional (3D) method for assessing nasal cavity (NC) and pharyngeal airway (PA) dimensions in growing patients with UCM compared with healthy controls, and to explore the potential associations between airway dimensions and SDB risk using the pediatric Sleep Questionnaire (PSQ). Material and Methods: This retrospective case–control pilot study analysed Cone‐beam computed tomography (CBCT) scans from 11 patients with UCM (mean age 12.7 ± 4.4 years) and 11 age‐matched controls (mean age 12.1 ± 1.1 years). Two calibrated investigators performed airway measurements, including total and partial NC and PA volumes, minimal cross‐sectional area (MCS), and hydraulic diameter. SDB risk in the UCM group was assessed using PSQ scores. Reliability was evaluated with intra‐class correlation coefficients and Dahlberg's formula. Group differences were tested using unpaired t ‐tests. Results: Patients with UCM showed significantly smaller NC volume (9714.9 ± 3032 mm ³ ) than controls (12773.9 ± 1963 mm ³ ; p < 0.0001). PA volume and MCS did not differ significantly. Four of the eleven patients with UCM showed a high PSQ‐based risk of SDB and tended to have smaller airway dimensions. Measurement reliability was acceptable. Conclusions: CBCT‐based 3D assessment of upper airway morphology was feasible in patients with UCM. Patients with UCM demonstrated significantly reduced nasal cavity volume compared with controls. Exploratory analysis suggested that some patients may be at increased risk of SDB; however, these findings require confirmation in larger studies.

This study examines the nocebo effect in orthodontics by assessing how parental orthodontic experiences and attitudes toward dental care influence children’s pain and discomfort at treatment onset. Consecutive orthodontic patients aged 12–18 years were included in this prospective observational cohort study. Parents’ prior orthodontic experiences, perceptions of dental care, and pain expectations were assessed by questionnaire. Children completed the Pain Catastrophizing Scale (PCS) and the Spielberger State Trait Anxiety Inventory for Children (STAIC) before fixed appliance bonding. Follow-up questionnaires 3–5 days post-bonding assessed pain, dysfunction, medication use, and oral health impact (OHIP-14). Non-parametric tests and regression analyses were used to assess associations between parental expectations and patient-reported outcomes. Seventy patients (mean age 14.1 ± 1.7 years; 51.3% females) were included. Greater pain medication use was reported among patients whose parents had prior orthodontic experience (p = 0.038). Parental associations of orthodontic care with pain were linked to higher patient anxiety and pain catastrophizing. Regression analyses confirmed associations between parental expectations and patient orofacial dysfunction, anxiety, pain catastrophizing, and oral health impact. The study indicates a proxy nocebo effect in orthodontics, where parental experiences and perceptions influence children’s pain during treatment. Addressing parental perceptions may help reduce this effect and improve treatment experiences for children and adolescents.

Srivastav S, et al. Imaging Sci Dent. 2025 Nov;55:e47. https://doi.org/10.5624/isd.20250201

Objective: This cross‐sectional study examined differences in orofacial features and oral health‐related quality of life in previously treated young adults with either idiopathic condylar resorption (ICR) or TMJ involvement from juvenile idiopathic arthritis (JIA). Material and Methods: The study included 17 patients with ICR and 15 patients with JIA‐related TMJ involvement, all treated at the Section for Orthodontics, Aarhus University Craniofacial Clinic, Denmark and the Department of Oral and Maxillofacial Surgery, Aarhus University Hospital, Denmark. Data compared came from a standardised radiological examination with cone‐beam computed tomography, a standardised clinical orofacial examination and a completed oral health‐related quality of life questionnaire (OHIP‐14). Validated methodologies were used to evaluate TMJ and dentofacial morphology. Results: The ICR patients reported a markedly higher number of TMJ pain symptoms, affected jaw function and difficulties in chewing food. The ICR group showed significantly more pronounced aberrant dentofacial morphology in relation to mandibular inclination, anterior/posterior face height ratio and mandibular sagittal position. No statistically significant differences were detected in the severity of condylar deformity between ICR and JIA patients. The oral health‐related quality of life was significantly more negatively affected in the ICR group compared to the JIA group in the following domains: physical pain, psychological discomfort and psychological disability. Conclusion: The present study illustrates more severe TMJ signs and symptoms, a greater degree of dentofacial deformity and negatively affected oral health‐related quality of life in the ICR group when compared with the JIA group. The same degree of TMJ deformity was found in both groups. Our findings contribute valuable insights towards establishing diagnostic criteria for ICR patients and improving the differential diagnosis of JIA‐related TMJ involvement.