Simulation‐Based Training in Dental Radiography: The Impact of Qbion Software on Learning Outcomes for Dentistry Students

ABSTRACT

Introduction

Enhancing the quality of radiographic imaging is crucial for minimising patient exposure to ionising radiation resulting from repeated X‐rays. Addressing technical errors during patient positioning is vital for obtaining accurate extraoral radiographs. Radiological simulation offers dentistry students a unique opportunity to refine their skills and gain experience in taking intraoral and extraoral X‐rays without ionising radiation.

Materials and Methods

A survey was conducted among 98 dental students. Half of them comprised the control group which had taken a course in radiography, and the other 50% were the study group who additionally underwent Qbion training. Respondents answered 13 single‐ and multiple‐choice questions concerning one correct and 12 panoramic radiographs with one, two or three positioning errors. The students were asked to identify the reason for the occurrence of a technical error. The results were analysed descriptively.

Results

A correctly taken panoramic X‐ray was recognised by 79 students (80.6%). Among them, 44 students (55.7%) used the Qbion software. The most commonly identified error was asymmetrical patient positioning, identified by 78 of 98 respondents (79.6%)—43 (55.1%) in the study group and 35 (44.9%) in the control group. The most difficult question concerning the too forward position of the patient was correctly answered only by 34 students.

Discussion

Students utilising Qbion software demonstrated a higher accuracy in answering questions related to radiographic errors, indicating the software's effectiveness in enhancing practical skills. The analysis of multiple‐choice questions showed that the higher the degree of difficulty, the lower the percentage of correct answers.

Conclusions

The results suggest that integrating radiological simulation tools, like Qbion, significantly improves learning outcomes for students. However, further research with larger sample sizes is warranted to validate these findings.

1. Introduction

Simulation has become a cornerstone in healthcare education, particularly in fields like surgery and radiology, where hands‐on practice is vital. For instance, the French National Authority for Health (HAS) emphasises the ethical responsibility of ensuring that practitioners gain experience through simulations before working on patients, particularly in high‐stakes procedures like robotic surgery [1]. In dental education, simulation is equally critical, especially in radiography, where accurate diagnostic skills and patient safety must be prioritised. This is heightened by the need to reduce exposure to ionising radiation during training. Educational computer‐assisted simulation (ECAS) has transformed healthcare education, providing collaborative learning environments that allow students to safely practice and refine their skills. Studies show that simulation‐based training not only improves diagnostic proficiency but also creates a safer learning environment by eliminating the risks associated with real‐world radiography errors [2, 3]. The Qbion software, developed by Umeå University (Sweden), specialises in the development of radiology simulation software for enhancing diagnostic radiology education. The company focuses on providing training without utilising ionising radiation so that the students can acquire ALARA (as low as reasonably chievable) principles. Qbion's portfolio comprises software that addresses different areas of radiology, including bitewing, periapical, object localisation and panoramic imaging. These products are meant to offer feedback and promote comprehensive learning in a virtual, controlled environment. The programme provides exercises that specialise in the analysis of radiographic characteristics and the resolution of difficult problems, thereby rendering a complete know‐how of radiographic techniques. Simulation training in dental radiology is designed to provide hands‐on proficiency in a simulated environment so that the students can acquire basic skills before practicing on real patients (Figure 1). The learning objectives for individuals usually include the proper patient positioning for intraoral and extraoral radiographs, correct sensor or film placement and angulation and use of different radiographic techniques (e.g., paralleling, bisecting‐angle, panoramic and cephalometric). It allows students to learn how to recognise normal anatomical landmarks on dental radiographs, identify common dental pathologies (e.g., caries, periodontal disease, periapical lesions), differentiate between artefacts and true pathology and identify and correct common radiographic errors (e.g., elongation, foreshortening, cone‐cutting) [4, 5]. Such simulation technologies offer advantages over traditional teaching methods, including enhanced skill retention, increased diagnostic accuracy, and objective assessments based on predefined quality standards [6]. The present study investigates the efficacy of Qbion radiography simulation software in improving the diagnostic skills of dentistry students. Specifically, it examines whether simulation‐based training offers advantages over traditional educational methods in radiological education while reducing the risks associated with ionising radiation exposure.

FIGURE 1.

A visual representation of the Qbion software interface and its training features.

2. Materials and Methods

A questionnaire‐based survey was conducted among 98 dentistry students at the Medical University. The reasoning behind the selection of evaluation methods used in this study was premised on having a standardised and objective basis for measuring knowledge. The aim was to assess the effectiveness of Qbion radiography simulation software in enhancing students' diagnostic abilities, particularly their capacity to identify common radiographic errors. The participants were divided into two equal groups: the control group consisting of 49 students who had completed traditional radiography courses, and the other half was the study group consisting of students who had received training in both traditional radiography and Qbion simulation. A multiple‐choice and single‐choice questionnaire was chosen as the evaluation instrument since it provides a reliable and consistent means of testing participants' understanding. The survey consisted of 13 single‐ and multiple‐choice questions, one of which presented a correctly taken panoramic X‐ray, while the remaining 12 showcased images with various positioning errors, such as asymmetrical or twisted head positions. The questions specifically addressed the recognition of errors encountered in patient positioning for panoramic radiographs. Emphasis on this factor is critical in clinical practice, as correct positioning is essential for obtaining accurate images. Thus even when errors occur, prompt identification and understanding the underlying causes of the inaccuracies is vital to ensure that appropriate corrective measures are taken, avoiding unnecessary exposure to ionising radiation.

3. Results

The data analysis from the 98 participants (49 in the control group and 49 in the Qbion‐trained group) revealed a clear distinction in performance between the two groups. A total of 79 students (80.6%) successfully identified the correctly captured panoramic X‐ray (Figure 2), with 44 of these students (55.7%) being from the Qbion‐trained group, highlighting a notable improvement over the control group, which had 35 students (44.9%) identifying the image correctly. This suggests that Qbion simulation training may have enhanced the students' ability to detect well‐captured radiographs, likely due to the repeated exposure to virtual radiographic examinations that the simulation offers.

FIGURE 2.

Correctly positioned patient and correctly taken panoramic X‐ray.

The most frequently recognised error across all participants was asymmetrical patient positioning (Figure 3), identified by 78 students (79.6%). Among these, 43 students (55.1%) were from the Qbion group, and 35 (44.9%) were from the control group. While the error was relatively easy to identify, the slight difference between the two groups supports the idea that the simulation group may be slightly more attuned to recognising common errors. This finding is in line with prior studies that emphasise how simulation training improves fundamental diagnostic skills in a safe environment [2].

FIGURE 3.

Correctly taken panoramic X‐ray (on the left) and with asymmetrical patient positioning (on the right).

More complex errors, such as excessive forward positioning of the patient (Figure 4), were less frequently identified correctly. Only 34 students (34.7%) in total detected this error, with 27 students (79.4%) being from the Qbion‐trained group, compared to just 7 students (14.3%) from the control group. This significant difference suggests that simulation‐based training, especially involving the Qbion software, greatly improved students' capacity to recognise and correct errors that require a higher level of spatial awareness and technical skill. This is consistent with existing literature, where simulation training was shown to improve the recognition of more complex diagnostic mistakes [6].

FIGURE 4.

Correctly taken panoramic X‐ray (on the left) and with excessive forward positioning of the patient (on the right).

The most challenging question, involving three simultaneous errors (head tilted, Frankfort plane misaligned and forward patient positioning) (Figure 5), was correctly identified by only 17 students, with 15 (88.2%) of these being from the Qbion‐trained group. This high percentage underscores the effectiveness of Qbion simulation in fostering the recognition of multiple errors in a single radiograph, a skill crucial for advanced diagnostic practice. In contrast, only 2 students (4.1%) from the control group identified all three errors, reflecting the greater difficulty of these tasks and the advantage provided by the simulation environment.

FIGURE 5.

Correctly taken panoramic X‐ray (on the left) and with three errors: that is head of the patient tilted to one side, incorrectly aligned Frankfort plane, too forward positioning of the patient (on the right).

Additionally, a question with two errors (misaligned Frankfort plane and forward positioning of the patient) was correctly answered by just 20 students (20.4%). Of these, 19 (95%) were from the Qbion‐trained group. This again highlights the superiority of Qbion‐based training in helping students recognise errors in more complex radiographic settings. The high rate of correct answers in the Qbion group further supports the argument that the software improves students' diagnostic skills, particularly in recognising subtle, difficult‐to‐spot errors (Figures 6 and 7).

FIGURE 6.

Graph showing the participation of the study and control groups among the correct answers.

FIGURE 7.

A table summarising the main differences in performance between the control and experimental groups.

Statistical analysis using a chi‐square test confirmed these findings. The test revealed a significant difference in the performance between the two groups, with a chi‐square value of χ ² = 21.37 and a p‐value = 0.00027, indicating that the differences observed in error recognition were not due to random chance. The p‐value indicates a high level of statistical significance, strengthening the conclusion that Qbion simulation training had a positive and measurable impact on students' ability to identify radiographic errors. These results are consistent with previous research demonstrating that simulation‐based training improves diagnostic accuracy and error recognition, particularly in the context of complex or nuanced tasks [5, 7]. What is more, prior research has also indicated that simulated training environments enhance students' ability to retain diagnostic skills over time, with one study demonstrating that simulator‐trained students maintained higher performance levels even after 8 months (p = 0.01) [6].

Overall, the results clearly indicate that students trained with the Qbion simulation software outperformed those in the traditional radiography training group, particularly in recognising complex errors (Figures 6, 7, 8). These findings are consistent with previous studies that have demonstrated the superiority of simulation technologies in dental education, where virtual reality–based platforms have been shown to enhance learning outcomes by providing repeated practice and immediate feedback in a safe, controlled environment [8, 9]. Additionally, the reduction in ionising radiation exposure associated with simulation training is an important safety advantage, aligning with the ethical principles of reducing radiation risk during medical and dental training [2].

FIGURE 8.

A graphic representation of error detection rates for different error types.

4. Discussion

The study's results align with previous research that supports the use of simulation technologies in medical and dental education. Radiological virtual reality simulators, such as Qbion, provide a safe and interactive platform for students to practice and improve their diagnostic skills without risking exposure to ionising radiation [2]. By leveraging computer‐supported collaborative learning (CSCL), simulation‐based environments not only enhance individual learning but also promote group interactions, fostering better skill retention and practical application through collaborative efforts [3].

Further studies underscore the long‐term benefits of simulator‐supported training. For instance, research on dental students demonstrated that skills acquired via radiological simulators remain significantly improved even after 8 months (p = 0.01), far surpassing retention levels achieved through traditional education alone [7, 8]. This suggests that simulation serves as an important complement to traditional teaching methods, offering students an opportunity for continuous practice and skill enhancement beyond the classroom.

Moreover, studies on the effectiveness of objective structured clinical examinations (OSCE) in radiology education have demonstrated the importance of structured assessments in evaluating student competencies. OSCE has proven reliable in both physical and virtual settings, providing an objective framework to assess students' clinical and technical skills in oral radiology [9]. By incorporating technologies like augmented reality (AR) and virtual reality (VR) into assessments, educators can offer more engaging, hands‐on training experiences that enhance student learning outcomes [10].

Group dynamics also play a significant role in simulator‐based education. One study comparing traditional and simulator training environments found that students using simulators showed greater improvement in test scores and demonstrated enhanced group collaboration, likely due to the interactive and feedback‐rich nature of the simulation environment [11]. Such findings emphasise that simulators not only improve individual learning but also foster critical group interactions, which are vital for the development of practical skills in clinical settings. Finally, advances in simulation technology have significantly improved radiographic training accuracy and reliability. A study on simulation‐supported training methods showed that simulators could enhance interpretative skills, particularly in individuals with initially lower visual‐spatial abilities [12, 13]. The continuous refinement of high‐precision simulators contributes to a better understanding and application of spatial relationships in radiographs, leading to marked improvements in clinical competency [14]. Nevertheless, in another study, in which 36 dental students took part, there was an examination of how feedback formats influenced the use of specialised terminology. While feedback and task type affected language application, no link to proficiency development was found. The authors also observed interaction patterns between students and simulators, noting turn‐taking and dominance, but again, these did not correlate with skill enhancement [15]. The systematic review by Alharbi et al. [16] suggests that simulation‐based learning (SBL) is an effective pedagogical approach for promoting the acquisition and retention of knowledge and skills across various educational topics. However, because less than 25% of the studies included in the review assessed the retention of outcomes beyond 5 months post intervention, the findings are not sufficiently representative regarding the long‐term sustainability of the impact of simulation‐based learning. Sivanjali, in her publication [17], emphasises that academic teachers must be oriented towards SBL, and students should recognise the effectiveness of this method and willingly participate in such educational activities. The use of standardised patients in SBL is becoming increasingly popular due to difficulties in accessing real patients, as well as other benefits such as feedback and standardisation. Although simulation has its advantages, there are challenges associated with creating a simulation lab. Elndu et al. [18] point out that the implementation of simulation‐based technology (SBT) comes with certain challenges, such as high costs, the need for specialised equipment and infrastructure, faculty training, and limitations in realism compared to actual clinical settings. By acknowledging and strategically managing these challenges, educational institutions can maximise the potential of SBT in more effectively preparing future healthcare professionals.

The findings from this study highlight the valuable role that simulation‐based training, particularly Qbion radiography software, plays in dental education. The software allows students to practice radiographic examination in a safe, controlled environment, with the added benefit of reducing ionising radiation exposure. The results also demonstrate that students trained using simulation technology perform better at recognising common radiographic errors, particularly in complex cases that require a higher level of spatial awareness and technical skill. As simulation technologies like Qbion become more integrated into dental education, they will likely enhance both diagnostic proficiency and collaborative learning among students. The continuous improvement of simulators, combined with structured assessment methods such as OSCE, provides a comprehensive educational framework that prepares students for real‐world clinical challenges. Moving forward, future research should focus on standardising simulation practices across different educational settings and exploring their broader applicability. This will ensure that radiography learners, regardless of their initial skill levels, can fully benefit from the advancements in simulation‐based education.

One of the limitations of this study was the relatively small number of participants in both the experimental and control groups. Additionally, the study had a short observation period, which prevented a full assessment of the long‐term impact of the simulation on the development of diagnostic skills. Further research should focus on analysing the long‐term effects of simulation training on clinical practice with a larger group of students in order to determine the durability of acquired competencies and their actual translation into diagnostic effectiveness in daily practice. It will also be important to identify the best strategies for implementing simulations in various educational contexts to optimally support the student learning process and enhance the effectiveness of teaching dental radiology.

5. Conclusion

In conclusion, this study shows that Qbion radiography simulation software significantly enhances the diagnostic skills of dentistry students compared to traditional methods. Students using the simulation demonstrated better error recognition, particularly for complex cases, while also benefiting from reduced exposure to ionising radiation. These findings highlight the value of integrating simulation‐based training into dental education, paving the way for improved competencies and safer learning environments. Therefore, it is recommended that educational institutions implement simulation technologies as a standard component of radiological training. Policymakers should consider introducing regulations and standards to support the integration of simulation into curricula, particularly in the context of minimising exposure to ionising radiation. Funding further research on the effectiveness of simulation in radiological education may contribute to optimising teaching methods and further improving simulation tools.

Conflicts of Interest

The authors declare no conflicts of interest.

Krzyżanowska M., Łabno D., Smala K., Miazek W., Piskórz M., and Różyło‐Kalinowska I., “Simulation‐Based Training in Dental Radiography: The Impact of Qbion Software on Learning Outcomes for Dentistry Students,” European Journal of Dental Education 30, no. 3 (2026): 1168–1174, 10.1111/eje.70072.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.