Keywords ultrasound training - adaptive learning - prenatal diagnosis - medical education -
image recognition - gamification - cognitive bias
Schlüsselwörter Ultraschalltraining - adaptives Lernen - pränatale Diagnostik - medizinische Ausbildung
- Bilderkennung - Gamifizierung - kognitive Verzerrung
Introduction
Prenatal diagnostics are a critical component of modern obstetrics, playing a vital
role in the ongoing assessment of fetal development and overall health [1 ]. Among the array of diagnostic tools available, ultrasound examination is particularly
important due to its non-invasive nature and its capability to provide real-time visualization
of the fetus [2 ]. This technique is essential for the early detection of fetal anomalies, as well
as for identifying potential complications that could impact both maternal and fetal
outcomes. Furthermore, ultrasound is not only fundamental for the aspects outlined
but also serves as an essential tool for the accurate estimation of fetal weight and
the evaluation of fetal well-being. While these factors may be broadly categorized
under the term “complications,” their distinct importance warrants
explicit mention to highlight their critical role in comprehensive prenatal assessment
and clinical management.
The rapid advancement in imaging technologies has significantly transformed prenatal
diagnostics, leading to a higher demand for precise and accurate diagnostic skills
among medical students and doctors. As imaging technologies continue to evolve, the
necessity for rigorous ultrasound education, particularly within the specialized field
of prenatal medicine, has become increasingly apparent [3 ]. This demand underscores the importance of developing and implementing effective
training programs that can equip healthcare providers with the necessary skills to
perform and interpret ultrasound examinations with a high degree of accuracy.
Historically, ultrasound training has predominantly been delivered through in-person
classes and workshops [4 ]. These traditional methods are only effective to a certain extent, often falling
short in addressing the comprehensive needs of modern ultrasound education. They often
lack personalized, adaptive feedback which allows for targeted and continuous skill
development as provided by good adaptive learning systems [5 ]. Specifically, there is a notable deficiency in training focused on image recognition
and interpretation—two critical skills that are essential for accurate prenatal diagnostics.
This gap in training is concerning because the ability to accurately interpret ultrasound
images is fundamental to the early detection and diagnosis of fetal conditions. Without
adequate training in image recognition, healthcare professionals
may struggle to apply their theoretical knowledge effectively during clinical practice,
potentially leading to missed diagnoses or incorrect interpretations.
To address these educational shortcomings, there has been a growing interest in the
development and adoption of technology-enhanced training methodologies that leverage
adaptive learning systems [6 ]. These systems are designed to personalize the learning experience, adjusting to
the unique competencies and needs of each learner [7 ]. By doing so, they provide a more tailored educational experience that can better
prepare healthcare professionals for the complexities of prenatal ultrasound diagnostics.
Adaptive learning platforms offer a range of training modalities, including simulation-based
training, virtual and augmented reality (VR/AR) experiences, as well as web-based
platforms, all of which are essential in bridging the gaps identified in traditional
ultrasound education [8 ].
Despite advancements in training methodologies, the “satisfaction of search” effect
remains a persistent challenge in diagnostic imaging, particularly in radiology and
prenatal diagnostics. This cognitive bias occurs when detecting one abnormality reduces
the likelihood of identifying additional abnormalities, potentially leading to incomplete
or incorrect diagnoses, which is especially concerning in prenatal care where multiple
undetected anomalies can have serious implications for both mother and fetus [9 ]. To address this issue, ultrasound training programs need to incorporate strategies
that directly target this bias, such as including cases with multiple abnormalities
to encourage continuous vigilance and teaching techniques to maintain focus and prevent
mental fatigue.
Rapid decision-making is another critical component of effective ultrasound diagnostics.
In clinical settings, doctors must quickly and accurately interpret ultrasound images,
a skill that can be significantly enhanced through simulation-based training. Such
hands-on methods allow students to repeatedly practice in a controlled environment,
building the confidence and competence required for timely and accurate evaluations
in high-pressure situations [10 ].
Evaluating recognition performance in ultrasound training involves various methods
that offer insights into cognitive processes. Signal Detection Theory (SDT) is often
used to differentiate between true signals and noise, providing key metrics like sensitivity
and specificity [11 ]. SDT is a framework used to quantify the ability to distinguish between information-bearing
signals and background noise. It provides key metrics like sensitivity and specificity,
which are essential in evaluating the accuracy of diagnostic systems. SDT also considers
the decision-making process under uncertainty, accounting for both hits (correctly
identifying a signal) and false alarms (incorrectly identifying noise as a signal).
However, when the assumptions of SDT, such as normal distribution and equal variance,
are not met, non-parametric measures like A′ offer alternative performance evaluations.
A′ is a statistic that
provides a robust estimate of a system’s discriminative ability without relying on
the parametric assumptions required by traditional SDT metrics [12 ]. Additionally, reaction time serves as an important indicator of cognitive processing
efficiency, with shorter times reflecting more effective recognition skills [13 ]. Together, these methods provide a comprehensive understanding of image recognition
processes, helping to identify areas for improvement in training.
By integrating adaptive image recognition technology with advanced simulation and
feedback mechanisms, AdaptUS provides a rigorous and personalized approach to ultrasound
education. While the system is adaptive in that incorrectly interpreted images are
revisited for further practice, it currently does not adjust image difficulty based
on individual skill levels. However, this structured repetition still supports effective
learning, and future advancements could further refine its adaptability. Overall,
AdaptUS leverages essential elements of adaptive learning to significantly enhance
the diagnostic skills of medical students and doctors in the complex field of prenatal
diagnostics.
Material and Methods
Technological implementation
The development of AdaptUS was undertaken in collaboration with the Center for Adaptive
Security Research and Applications (CASRA), a Swiss company known for its development
of advanced training solutions in various domains. AdaptUS leverages the X-Ray Tutor
4 (XRT4) system (Version 2.18.1, produced by APSS Software & Services AG), which was
originally developed for training in aviation security. The XRT4 system supports the
visualization of single-view, dual-view, and 3D computed tomography (CT) images, and
is compatible with multiple web browsers without requiring additional plugins, thus
allowing for a highly customizable and versatile training environment.
For its application in prenatal ultrasound education, the XRT4 system has been repurposed
by CASRA itself and extensively modified to support the specific visual and cognitive
tasks involved in ultrasound diagnostics. This web-based adaptive learning platform
was meticulously designed to provide personalized training in prenatal ultrasound
diagnostics. The platform dynamically adjusts its content and exercises in real-time
to address the specific learning needs of each user, with a focus on critical areas
such as fetal anatomy, biometry, and the identification of fetal pathologies. [Fig. 1 ] presents a selection of sample questions from the e-learning module.
Fig. 1
The AdaptUS-E-Learning platform. a A screenshot presenting the question the user is prompted to answer within the learning
system. b A screenshot showing the feedback provided to the user based on their answer, indicating
whether it was correct or incorrect and offering additional explanations.
Study design and participants
This study employed a prospective cross-sectional design with a controlled intervention
to evaluate the effectiveness of an adaptive learning module specifically designed
to enhance ultrasound diagnostic skills among medical students. The project was conducted
at the Department of Obstetrics and Prenatal Medicine at the University Hospital Bonn
and reported in accordance with the Strengthening the Reporting of Observational Studies
in Epidemiology (STROBE) guidelines [14 ]. The study was meticulously carried out in accordance with the ethical standards
of the institutional research committee and adhered to the guidelines of the 1964
Helsinki Declaration and its subsequent amendments. Ethical approval for the study
was obtained from the Ethics Committee of the University Hospital Bonn, under the
approval number 233/23-EP. All participants provided informed consent prior to their
involvement in the
study.
The study cohort consisted of undergraduate medical students from the University Hospital
Bonn, specifically those in their fifth year of the six-year medical program. The
participants were systematically divided into either the interventional cohort or
the control group (see [Fig. 2 ]). Both groups were subjected to two standardized assessments: one prior to the intervention
(T1) and another following the intervention (T2) with the AdaptUS system or a conventional
ultrasound course. Each assessment comprised 16 questions that spanned a range of
topics pertinent to ultrasound diagnostics, including fetal biometry, pathology, and
anatomical structures, along with practical image interpretation tasks. These assessments
were designed to evaluate participants’ knowledge and diagnostic accuracy. To simulate
real-world diagnostic conditions, participants were allotted 30 seconds to respond
to each question. Accompanying
each question was an ultrasound image, and participants were required to determine
whether the provided statement or interpretation related to the image was correct
or incorrect. No immediate feedback was provided after each response during this test.
The assessment was conducted as a written test performed on a computer, further mimicking
the conditions encountered in actual diagnostic practice.
Fig. 2
The study design of the adaptive image recognition system.
Participants were assigned to the intervention and control groups based on their enrollment
in the gynecology course, but both groups had a comparable baseline knowledge level
in ultrasound diagnostics. This was ensured by selecting students from the same academic
year with similar prior education and exposure to ultrasound topics. Therefore, differences
in outcomes are attributable to the AdaptUS system rather than any pre-existing disparities.
Students of the interventional group, enrolled in the gynecology course, were informed
that their final exam would include ultrasound images covered by the AdaptUS system,
encouraging them to engage with the platform. Further, they were encouraged to rely
on traditional methods, including textbook learning, in-person lectures, and direct
clinical experience, rather than newer simulation-based or digital training techniques.
The control group was composed of volunteers from the same academic cohort not in
the course, also studied with traditional methods, without access to the AdaptUS system.
All participating students shared a common theoretical background, which included
a weekly obstetrics lecture series. The lecture content was supplemented by weekly
slide uploads, which students could access for review. Additionally, all students
took part in a one-week, full-day internship focused on gynecology and obstetrics.
This internship provided students with foundational knowledge and included hands-on
examination courses, allowing them to apply theoretical knowledge in practical settings.
This week concluded with an Objective Structured Clinical Examination (OSCE), designed
to assess students’ practical skills, alongside a written exam covering the topics
discussed in the lectures.
The intervention group in this study, composed of students enrolled in the obstetrics
course during the semester, was granted complimentary access to the adaptive learning
program. This program was designed to offer personalized learning experiences tailored
to individual needs, aimed at improving the students’ ultrasound diagnostic skills.
As part of the study protocol, students in the intervention group were required to
complete both the pre-test and post-test assessments to measure their progress. The
control group, on the other hand, did not have access to the adaptive learning program
during the study period but participated in the same assessments as the intervention
group. To ensure fairness and provide an opportunity for learning, the control group
was offered access to the adaptive learning system after the study concluded.
To ensure consistency in data collection, both tests were conducted under standardized
conditions. T1 was administered at the beginning of the semester, establishing a baseline
of ultrasound competence, while T2 was administered at the end of the semester, following
the intervention period for the experimental group. Participants were instructed to
complete the assessments independently within the allotted timeframe, and the use
of reference materials was strictly prohibited to preserve the integrity of the results.
In addition to the competence tests, demographic data including age, gender, and previous
ultrasound experience were collected via a pre-study questionnaire. This data was
used to control for potential confounding variables in the analysis.
Furthermore, student feedback was collected using a 7-point Likert scale questionnaire,
where participants rated their satisfaction with various aspects of the training.
The results were depicted through various visual representations, including bar charts,
box plots, and histograms, which were created using R Version 4.4.1 (R Foundation
for Statistical Computing, Vienna, Austria) and Apple Key Note Version 14.1 (Apple
Inc, San Cupertino, USA).
Statistical analysis
Statistical analysis was performed using Jamovi software (version 2.3.28). Descriptive
statistical analysis of the demographic data of the participants as well as the assessment
results was performed by calculating the mean and standard deviation (SD) as well
as the median. The normal distribution within the intervention group was assessed
using the Shapiro-Wilk test, which confirmed the appropriateness of using a paired
t-test for this group. The control group’s data exhibited a non-normal distribution,
necessitating the use of both the paired t-test and the Wilcoxon signed-rank test
for analysis. The scores of both groups at T1 were compared using a Mann–Whitney U
test. To compare the differences in test scores between the intervention and control
groups, an unpaired t-test was conducted. P values < 0.05 were considered statistically
significant.
Results
The study sample comprised 76 medical students. Complete demographic data, including
age and other relevant variables, were available for all participants included in
the analysis. The intervention group consisted of 37 students with a mean age of 24.9 ± 1.6
years (see [Table 1 ]). The ages ranged from a minimum of 23 years to a maximum of 25 years. The control
group comprised 39 students (33 females, 6 males) with a mean age of 24.1 ± 1.6 years
ranging from 21 to 28 years. The proportion of female students was notably high in
both groups, with 84.6% of the control group and 73% of the interventional group identifying
as female. All students enrolled in the study were in the 5th year of their 6th year medical degree.
Table 1
Basic demographic characteristics of study participants.
Group
Number of students
Gender distribution
Mean age ± SD
(years)
Assessments and Intervention
Female
Male
Intervention group
37
27
10
24.92 ± 1.6
T1
AdaptUS,
T2
Control group
39
33
6
24.1 ± 1.6
T1,
T2
In the assessment T1 prior to the intervention, students in the intervention group
achieved a mean score of 70.9% (SD ± 12.9%), with a median score of 68.8% (see [Fig. 3 ]). In comparison, the control group averagely reached a score of 62.0% (SD ± 12.0%)
and a median score of 63.0%. A Mann–Whitney U test showed a significant difference
in the scores in T1 between the groups, with a U-statistic of 470.0 and a p value
of 0.045, indicating that the two groups had statistically different starting points
before the intervention.
Fig. 3
Test results of control and intervention group in T1 and T2, respectively. Bars show
the mean test result (in percent) and error bars indicate the corresponding standard
error. Paired t-test (intervention group) and Wilcoxon-signed-rank test (control group)
were performed to compare T1 and T2 results within the control and intervention groups
(ns: not significant, *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001).
Following the AdaptUS module, the intervention group’s mean score improved significantly
to 86.0%, with a median score of 87.5% and a reduced SD of ±7.28%. Conversely, the
control group exhibited a slight decline, with a mean score of 59.0%, a median score
of 60.0%, and a standard deviation of ±12.0%.
Statistical analysis demonstrated a significant enhancement in ultrasound competence
within the intervention group, as evidenced by the results of a paired t-test, which
yielded a p value of less than 0.001 (see [Fig. 3 ]). The normality of the differences in scores between pre- and post-intervention
was confirmed by the Shapiro–Wilk test (W = 0.964, p = 0.273), justifying the use
of the paired t-test for this analysis.
In contrast, the control group did not exhibit a statistically significant improvement
in ultrasound competence. The paired t-test for the control group produced a t-statistic
of 1.867 with a p value of 0.071, suggesting no significant change. Additionally,
the Shapiro–Wilk test indicated that the data from the control group did not follow
a normal distribution. Consequently, the non-parametric Wilcoxon signed-rank test
was conducted, which also showed no significant improvement (W = 101.0, p value = 0.058,
[Fig. 3 ]).
Further comparative analysis between the two groups was performed using an unpaired
t-test to evaluate the differences in score improvements. This analysis revealed a
significant difference in the extent of improvement between the intervention and control
groups, with the t-test yielding a t-value of −7.056 and a p value of 1.202 × 10−9 (see [Fig. 4 ]).
Fig. 4
Boxplots of differences between T2 and T1 for the control and the intervention group.
Borders of the box indicate lower and upper quartiles; middle line indicates the median.
Length of the whiskers is maximal 1.5 interquartile ranges. An unpaired t-test was
performed to compare the learning improvement between the control and intervention
groups (ns: not significant, *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001).
Feedback
The feedback gathered from the participating medical students regarding the AdaptUS
system was generally positive, with students reporting improvements in their ultrasound
diagnostic skills. Many students attributed these improvements to the system’s flexible
nature, which allowed for personalized learning by focusing on areas of identified
weakness in ultrasound diagnostics. 55.29% of the participants would recommend the
learning tool for future courses and expressed their support for the broader integration
of adaptive learning systems, such as AdaptUS, into the medical curriculum. Item v41
and Item v42 were perceived to be very comprehensible by the majority of participants,
while Itemv43 was rated less comprehensible. The detailed responses on the learning
system can be seen in [Fig. 5 ].
Fig. 5
The figure summarizes participant feedback on various aspects of the learning system.
It includes data from the Item “I would recommend the image interpretation training
to other medical students”, which measures the likelihood of recommending the system,
with responses ranging from “totally true” to “disagree,” reflecting generally positive
feedback. Additionally, the figure presents data for the Items “The questions asked
are understandable to me.”, “The feedback on the incorrectly answered questions is
comprehensible”, and “The extent of the feedback is sufficient”, assessing the clarity
of the system and the feedback on incorrect answers. Responses also span from “totally
true” to “disagree,” showing overall satisfaction with both understandability and
feedback. Further details regarding the feedback can be found in the attachment.
Students also provided several suggestions for enhancing the AdaptUS system. These
included incorporating more complex ultrasound images to further challenge and develop
advanced diagnostic skills, as well as optimizing the system for mobile use to increase
accessibility and flexibility. Additional feedback highlighted the need for detailed
explanations of incorrect answers and clearer descriptions of image content during
exercises. These suggestions were aimed at addressing knowledge gaps and improving
understanding of ultrasound diagnostics (see [Table 2 ]).
Table 2
Summary of the feedback of the participants.
Feedback category
Details
Design consideration
Overall sentiment
Positive
User-centric design
Skill improvement
Significant improvements in ultra sound diagnostic skills
Detailed feedback for incorrect answers
Curriculum integration
Potential benefits for medical curriculum integration
Accommodates various learning paces and styles
Novelty of system
Novel use of adaptive image-interpretation in fetal medicine
Ensure deeper understanding of ultrasound diagnostics
Suggested enhancements
Incorporate more complex images, optimize for mobile use
Enhance effectiveness of AdaptUS tool
In conclusion, the feedback indicated that the AdaptUS system had a positive impact
on students’ diagnostic skills and offered insights into potential areas for improvement
to better meet the needs of learners. Implementing these suggestions may further enhance
the educational value of the platform and its application in medical education programs.
Discussion
The results of this study demonstrate that an adaptive imaging training module significantly
enhances ultrasound diagnostic skills among medical students, particularly in the
specialized area of fetal medicine. This finding is supported by the substantial improvements
in test scores observed in the intervention group. The effectiveness of ultrasound
as a diagnostic tool in gynecology and fetal medicine is well-documented, as it plays
a crucial role in monitoring fetal development and detecting potential complications
[15 ]. Numerous training systems have been developed to improve proficiency in ultrasound
diagnostics, and the evidence suggests that structured training programs are particularly
effective.
Experiential learning models focused on fetal ultrasound have shown that students
can markedly improve their ability to interpret ultrasound images and conduct examinations
following structured training sessions [16 ]. Early exposure to ultrasound technology is essential for building a solid foundation
that prepares students for future clinical practice, as demonstrated by improvements
in visual-spatial skills and understanding of anatomical relation [17 ]. For example, studies have highlighted that ultrasound training programs incorporating
hands-on practice and guided sessions not only enhance students’ competence in identifying
and diagnosing fetal conditions but also boost their confidence in using this technology
in real-world settings [18 ].
Beyond gynecology, the application of ultrasound learning systems across various medical
disciplines underscores their versatility and importance in medical education. Structured
tutorial systems, especially in general medical training, have been found to significantly
improve residents’ understanding and application of ultrasound techniques [19 ]. These systems offer targeted, structured training that bridges the gap between
theoretical knowledge and practical application, which is crucial for the accurate
interpretation of ultrasound images. In radiology, for example, the integration of
portable ultrasound devices during anatomy sessions has been shown to significantly
enhance medical students’ understanding of anatomical structures and their spatial
relationships by providing real-time visualization [20 ].
The findings of this study underscore the critical need to integrate adaptive learning
methodologies within medical curricula, especially in fields that require hands-on
training. Adaptive learning tools like the AdaptUS module are crucial for bridging
the gap between theoretical knowledge and practical application, thereby enhancing
the overall standard of patient care. These tools not only facilitate the acquisition
of technical skills, such as ultrasound diagnostics, but also reinforce theoretical
concepts through visual and interactive learning methods. This is particularly important
in medical education, where students must transition from highly theoretical coursework
to practical, hands-on experiences.
One of the key advantages of the AdaptUS program is its online accessibility, which
allows students to engage with the material remotely. This flexibility is particularly
valuable as it opens up the possibility for broader application and could be a critical
feature in expanding future studies to include a more diverse participant pool [21 ]. However, the current version of the program does not include pathologies in the
ultrasound findings, which is a limitation that should be addressed in future updates.
Including a wider range of diagnostic scenarios would provide a more comprehensive
training experience and better prepare students for real-world clinical situations.
Although feedback is provided after assessments, ensuring that students do not reinforce
incorrect techniques during training remains a significant challenge. It is crucial
to develop strategies that prevent the learning of incorrect practices, which could
be carried into clinical settings. Future iterations of the program could benefit
from incorporating a hybrid approach to error detection and correction, where incorrectly
interpreted images are revisited both within the same training session for immediate
reinforcement and in subsequent sessions to strengthen long-term retention and mastery.
The significant improvement in ultrasound skills facilitated by the AdaptUS training
module has important implications for prenatal care. Accurate and early identification
of fetal pathologies is vital for effective monitoring, timely interventions, and
strategic planning for delivery. Enhanced diagnostic proficiency can lead to more
reliable detection of conditions, potentially reducing the incidence of misdiagnoses
[22 ]. This reduction in diagnostic errors not only alleviates undue stress for expectant
parents but also safeguards the health of both the mother and fetus. Therefore, improving
ultrasound skills through adaptive training directly enhances the quality of patient
care by minimizing errors and improving anomaly detection capabilities. This, in turn,
strengthens the trust between patients and healthcare providers, which is essential
for managing the complexities of pregnancy.
In addition to the quantitative improvements observed, the positive feedback from
students underscores the effectiveness of the AdaptUS module in medical education.
Participants highlighted the flexible nature of the system, which tailored its content
to address individual weaknesses—an aspect that traditional training methods often
overlook. This personalized learning experience not only enhanced their ultrasound
diagnostic skills but also reinforced the theoretical knowledge necessary for accurate
image interpretation, effectively bridging the gap between theory and practical application.
Many students agreed that incorporating adaptive learning technology into medical
curricula, particularly in fields like fetal medicine, would significantly benefit
future clinicians. This sentiment is consistent with existing literature that underscores
the value of early and structured exposure to ultrasound technology in building a
strong foundation for clinical practice [19 ].
Students also provided several suggestions for enhancing the AdaptUS system, including
the addition of more complex cases, extended image presentation times, clearer instructions
for probe positioning, and the ability to revisit specific modules. These recommendations
reflect the broader trend in medical education toward creating flexible, user-centered
learning environments that accommodate various learning paces and styles. Moreover,
the integration of mobile accessibility and improved text readability would support
the growing demand for remote learning tools, as evidenced by the increasing popularity
of online and gamified learning platforms in medical education. By addressing these
areas for improvement, the AdaptUS tool could further enhance its impact, similar
to other successful educational innovations that blend interactive learning with traditional
methodologies. The combination of these features not only promotes the accurate interpretation
of ultrasound images but
also strengthens the ability to apply this knowledge in clinical settings, ultimately
improving patient care outcomes.
While the findings of this study are promising, several limitations must be acknowledged.
The participant pool was limited to a single medical faculty, which may impact the
generalizability of the results. Furthermore, the reliance on specific tests to assess
ultrasound competence might not fully capture the diagnostic spectrum required in
clinical practice, highlighting the importance of hands-on training to ensure practical
skills and real-world application are adequately addressed. Future research should
consider incorporating practical examinations and expert evaluations to provide a
more comprehensive assessment of competencies. Additionally, expanding the demographic
scope of the research and investigating the long-term impact of adaptive ultrasound
training on clinical accuracy and patient safety are essential areas for further study.
Integrating virtual reality (VR) technologies into the learning framework could also
enhance the educational impact by offering
immersive and interactive training environments.
Moreover, the study did not account for prior experience with fetal ultrasound among
participants in both the intervention and control groups, which may have influenced
the learning outcomes. This is a key limitation, as pre-existing knowledge could lead
to unequal starting points among participants. Additionally, the statistical analysis
revealed a significant difference in the baseline performance (T1) between the intervention
and control groups (p = 0.045), suggesting that the groups did not have equal starting
points before the intervention. This imbalance at the outset may have influenced the
observed improvements, and future studies should aim for better matching of participants
or statistically control for baseline differences. Finally, the individual motivation
of the students, which can significantly affect learning success, was not measured
or controlled for. This could have introduced variability in the effectiveness of
the intervention.
Conclusion
This study provides robust evidence supporting the efficacy of an adaptive image recognition
training module in significantly enhancing ultrasound diagnostic skills, particularly
in the field of prenatal medicine. The results demonstrate that medical students who
engaged with the AdaptUS system showed marked improvements in their ability to accurately
interpret ultrasound images compared to those who did not receive this adaptive training.
These findings are particularly relevant given the critical role of ultrasound in
monitoring fetal development and diagnosing potential complications in obstetrics
and gynecology.
The AdaptUS module’s success highlights the importance of incorporating adaptive learning
technologies into medical education, especially in areas that require the integration
of theoretical knowledge with practical skills. The personalized approach of AdaptUS,
which adjusts content based on individual performance, effectively addresses the gaps
often seen in traditional ultrasound training methods. This adaptability not only
enhances the acquisition of technical skills but also reinforces the underlying theoretical
concepts necessary for accurate diagnostic interpretation.
Declarations
Funding: There was no funding to declare.
Ethics approval: This study was performed in line with the principles of the Declaration of Helsinki.
Approval was granted by the Ethics Committee of University Bonn (Approval Number 233/23-EP).
Consent to participate: Informed consent was obtained from all individual participants included in the study.
