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This study delves into the transformative potential of integrating augmented reality (AR) within imaging technologies, shedding light on this evolving landscape. Through a comprehensive narrative review, this research uncovers a wealth of literature exploring the intersection between AR and medical imaging, highlighting its growing prominence in healthcare. AR's integration offers a host of potential opportunities to enhance surgical precision, bolster patient engagement, and customize medical interventions. Moreover, when combined with technologies like virtual reality (VR), artificial intelligence (AI), and robotics, AR opens up new avenues for innovation in clinical practice, education, and training. However, amidst these promising prospects lie numerous unanswered questions and areas ripe for exploration. This study emphasizes the need for rigorous research to elucidate the clinical efficacy of AR-integrated interventions, optimize surgical workflows, and address technological challenges. As the healthcare landscape continues to evolve, sustained research efforts are crucial to fully realizing AR's transformative impact in medical imaging. Systematic reviews on AR in healthcare also overlook regulatory and developmental factors, particularly in regard to medical devices. These include compliance with standards, safety regulations, risk management, clinical validation, and developmental processes. Addressing these aspects will provide a comprehensive understanding of the challenges and opportunities in integrating AR into clinical settings, informing stakeholders about crucial regulatory and developmental considerations for successful implementation. Moreover, navigating the regulatory approval process requires substantial financial resources and expertise, presenting barriers to entry for smaller innovators. Collaboration across disciplines and concerted efforts to overcome barriers will be essential in navigating this frontier and harnessing the potential of AR to revolutionize healthcare delivery.
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Introduction: The radiotherapy workflow involves the collaboration of multiple professionals and the execution of several steps to results in an effective treatment. In this study, we described the clinical implementation of an electronic checklist, developed to standardize the process of the chart review prior to the first treatment fraction by the radiation therapists (RTTs). Materials and Methods: A customized electronic checklist was developed based on the recommendations of American Association of Physicists in Medicine (AAPM) Task Groups 275 and 315 and integrated into the Record and Verify System (RVS). The checklist consisted of 16 items requiring binary (yes/no) responses, with mandatory completion and review by RTTs prior to treatment. The utility of the checklist and its impact on workflow were assessed by analysing checklist reports, and by soliciting feedback to RTTs through an anonymized survey. Results: During the first trial phase, from June to November 2023, 285 checklists were completed with a 98% compilation rate and 94.4% review rate. Forty errors were detected, mainly due to missing signed treatment plans and absence of Beam's Eye View documentation. Ninety percent of detected errors were fixed before the treatment start. In 4 cases, the problem could not be fixed before the first fraction, resulting in a suboptimal first treatment. The feedback survey showed that RTTs described the checklist as useful, with minimal impact on workload, and supported its implementation. Discussion: The introduction of a customized electronic checklist improved the detection and correction of errors, thereby enhancing patient safety. The positive response from RTTs and the minimal impact on workflow underscore the value of the checklist as standard practice in radiotherapy departments.
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This study investigates, through a narrative review, the transformative impact of deep learning (DL) in the field of radiotherapy, particularly in light of the accelerated developments prompted by the COVID-19 pandemic. The proposed approach was based on an umbrella review following a standard narrative checklist and a qualification process. The selection process identified 19 systematic review studies. Through an analysis of current research, the study highlights the revolutionary potential of DL algorithms in optimizing treatment planning, image analysis, and patient outcome prediction in radiotherapy. It underscores the necessity of further exploration into specific research areas to unlock the full capabilities of DL technology. Moreover, the study emphasizes the intricate interplay between digital radiology and radiotherapy, revealing how advancements in one field can significantly influence the other. This interdependence is crucial for addressing complex challenges and advancing the integration of cutting-edge technologies into clinical practice. Collaborative efforts among researchers, clinicians, and regulatory bodies are deemed essential to effectively navigate the evolving landscape of DL in radiotherapy. By fostering interdisciplinary collaborations and conducting thorough investigations, stakeholders can fully leverage the transformative power of DL to enhance patient care and refine therapeutic strategies. Ultimately, this promises to usher in a new era of personalized and optimized radiotherapy treatment for improved patient outcomes.
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BACKGROUND: Virtual Environment Radiotherapy Training (VERT) is a virtual tool used in radiotherapy with a dual purpose: patient education and student training. This scoping review aims to identify the applications of VERT to acquire new skills in specific activities of Radiation Therapists (RTTs) clinical practice and education as reported in the literature. This scoping review will identify any gaps in this field and provide suggestions for future research. METHODS: In accordance with Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) extension for scoping reviews and Arskey and O'Malley framework, an electronic search was conducted to retrieve complete original studies, reporting the use and implementation of VERT for teaching skills to RTTs. Studies were searched in PubMed, EMBASE, and SCOPUS databases and included retrieved articles if they investigated the use of VERT for RTTs training. RESULTS: Of 251 titles, 16 articles fulfilled the selection criteria and most of the studies were qualitative evaluation studies (n=5) and pilot studies (n=4). The specific use of VERT for RTTs training was grouped into four categories (Planning CT, Set-up, IGRT, and TPS). CONCLUSION: The use of VERT was described for each category by examining the interaction of the students or trainee RTTs in performing each phase within the virtual environment and describing their perceptions. This system Virtual Reality (VR) enables the development of specific motor skills without interfering and pressurising clinical resources by using clinical equipment in a risk-free offline environment, improving the clinical confidence of students or trainee RTTs. However, even if VR can be integrated into the RTTs training with a great advantage, VERT has still not been embraced. This mainly due to the presence of significant issues and limitations, such as inadequate coverage within the current literature, software and hardware costs.
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Virtual Reality , Humans , Radiotherapy , Clinical CompetenceABSTRACT
INTRODUCTION: Both cone-beam computed tomography (CBCT) and surface-guided radiotherapy (SGRT) are used for breast patient positioning verification before treatment delivery. SGRT may reduce treatment time and imaging dose by potentially reduce the number of CBCT needed. The aim of this study was to compare the displacements resulting in positioning from the Image Guided Radiation Therapy (IGRT) 3D and SGRT methods and to design a clinical workflow for SGRT implementation in breast radiotherapy to establish an imaging strategy based on the data obtained. METHODS: For this study 128 breast cancer patients treated with 42.5 Gy in 16 fractions using 3D conformal radiotherapy with free breathing technique were enroled. A total of 366 CBCT images were evaluated for patient setup verification and compared with SGRT. Image registrations between planning CT images and CBCT images were performed in mutual agreement and in online mode by three health professionals. Student's paired t-test was used to compare the absolute difference in vector shift, measured in mm, for each orthogonal axis (x, y, z) between SGRT and CBCT methods. The multidisciplinary team evaluated a review of the original clinical workflow for SGRT implementation and data about patients treated with the updated workflow were reported. RESULTS: Comparison of the shifts obtained with IGRT and SGRT for each orthogonal axis (for the x-axes the average displacement was 0.9 ± 0.7 mm, y = 1.1 ± 0.8 mm and z = 1.0 ± 0.7 mm) revealed no significant statistical differences (p > 0.05). Using the updated workflow the difference between SGRT and IGRT displacements was <3 mm in 91.4 % of patients with a reduction in total treatment time of approximately 20 %, due to the reduce frequency of the CBCT images acquisition and matching. CONCLUSIONS: This study has shown that IGRT and SGRT agree in positioning patients with breast cancer within a millimetre tolerance. SGRT can be used for patient positioning, with the advantages of reducing radiation exposure and shorter overall treatment time.
