Assessment of MRI technologists in acceptance and willingness to integrate artificial intelligence into practice

Knowledge, Attitude and Practice of Radiologists Regarding Artificial Intelligence in Medical Imaging

Purpose

This study aimed to investigate the knowledge, attitudes, and practice (KAP) of radiologists regarding artificial intelligence (AI) in medical imaging in the southeast of China.

Methods

This cross-sectional study was conducted among radiologists in the Jiangsu, Zhejiang, and Fujian regions from October to December 2022. A self-administered questionnaire was used to collect demographic data and assess the KAP of participants towards AI in medical imaging. A structural equation model (SEM) was used to analyze the relationships between KAP.

Results

The study included 452 valid questionnaires. The mean knowledge score was 9.01±4.87, the attitude score was 48.96±4.90, and 75.22% of participants actively engaged in AI-related practices. Having a master's degree or above (OR=1.877, P=0.024), 5-10 years of radiology experience (OR=3.481, P=0.010), AI diagnosis-related training (OR=2.915, P<0.001), and engaging in AI diagnosis-related research (OR=3.178, P<0.001) were associated with sufficient knowledge. Participants with a junior college degree (OR=2.139, P=0.028), 5-10 years of radiology experience (OR=2.462, P=0.047), and AI diagnosis-related training (OR=2.264, P<0.001) were associated with a positive attitude. Higher knowledge scores (OR=5.240, P<0.001), an associate senior professional title (OR=4.267, P=0.026), 5-10 years of radiology experience (OR=0.344, P=0.044), utilizing AI diagnosis (OR=3.643, P=0.001), and engaging in AI diagnosis-related research (OR=6.382, P<0.001) were associated with proactive practice. The SEM showed that knowledge had a direct effect on attitude (β=0.481, P<0.001) and practice (β=0.412, P<0.001), and attitude had a direct effect on practice (β=0.135, P<0.001).

Conclusion

Radiologists in southeastern China hold a favorable outlook on AI-assisted medical imaging, showing solid understanding and enthusiasm for its adoption, despite half lacking relevant training. There is a need for more AI diagnosis-related training, an efficient standardized AI database for medical imaging, and active promotion of AI-assisted imaging in clinical practice. Further research with larger sample sizes and more regions is necessary.

The Integration of Deep Learning in Radiotherapy: Exploring Challenges, Opportunities, and Future Directions through an Umbrella Review

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.

Impact of Education and Experience on Radiographers' Ability to Diagnose Acute Appendicitis: A Survey in Private Malaysian Hospitals

Background

Acute appendicitis is a global surgical emergency. Radiographic modalities usually identify acute appendicitis, although radiographers' competence is questionable. This study examines how clinical radiographers' education and experience affect their ability to identify acute appendicitis using computed tomography (CT), magnetic resonance imaging (MRI) and ultrasonography (USG) characteristics. The study also aimed to determine which variable strongly influences their knowledge level.

Methods

The study surveyed radiographers with a four-part self-administered questionnaire containing demographic information and eight knowledge-based questions about the appearance of acute appendicitis in MRI, CT and USG, separately. Before distribution, the questionnaire was validated and checked the reliability.

Results

Clinical radiographers' knowledge about using MRI to diagnose acute appendicitis was strongly affected by education and experience (η2 = 0.13 and 0.14; P < 0.05), with bachelor's degree holders scoring higher regardless of experience. Radiographers with more than 5 years of experience knew more about CT and USG features to identify acute appendicitis (η2 = 0.40 and 0.27; P < 0.05). Radiographers with a bachelor's degree and greater experience had higher overall knowledge of MRI, CT and USG to diagnose acute appendicitis (η2 = 0.51 and 0.11; P < 0.05). With adjusted R2 = 54% (F [2, 44] = 27.94; P < 0.001), education and experience highly predicted the overall knowledge level.

Conclusion

The study found gaps in radiographers' knowledge of the radiographic appearance of acute appendicitis. Clinical radiographers' education level and years of experience substantially affect their knowledge level. In addition, experience is a good predictor than education level for overall knowledge level. Therefore, the study emphasises the importance of continuing education and training for radiographers to diagnose acute appendicitis quickly and accurately.

Beyond the Clinic Walls: Examining Radiology Technicians' Experiences in Home-Based Radiography

In recent years, the landscape of diagnostic imaging has undergone a significant transformation with the emergence of home radiology, challenging the traditional paradigm. This shift, bringing diagnostic imaging directly to patients, has gained momentum and has been further accelerated by the global COVID-19 pandemic, highlighting the increasing importance and convenience of decentralized healthcare services. This study aims to offer a nuanced understanding of the attitudes and experiences influencing the integration of in-home radiography into contemporary healthcare practices. The research methodology involves a survey administered through Computer-Aided Web Interviewing (CAWI) tools, enabling real-time engagement with a diverse cohort of medical radiology technicians in the health domain. A second CAWI tool is submitted to experts to assess their feedback on the methodology. The survey explores key themes, including perceived advantages and challenges associated with domiciliary imaging, its impact on patient care, and the technological intricacies specific to conducting radiologic procedures outside the conventional clinical environment. Findings from a sample of 26 medical radiology technicians (drawn from a larger pool of 186 respondents) highlight a spectrum of opinions and constructive feedback. Enthusiasm is evident for the potential of domiciliary imaging to enhance patient convenience and provide a more patient-centric approach to healthcare. Simultaneously, this study suggests areas of intervention to improve the diffusion of home-based radiology. The methodology based on CAWI tools proves instrumental in the efficiency and depth of data collection, as evaluated by 16 experts from diverse professional backgrounds. The dynamic and responsive nature of this approach allows for a more allocated exploration of technicians' opinions, contributing to a comprehensive understanding of the evolving landscape of medical imaging services. Emphasis is placed on the need for national and international initiatives in the field, supported by scientific societies, to further explore the evolving landscape of teleradiology and the integration of artificial intelligence in radiology. This study encourages expansion involving other key figures in this practice, including, naturally, medical radiologists, general practitioners, medical physicists, and other stakeholders.

"I think this could be a big success" - A mixed methods study on practitioner perspectives on the acceptance of a virtual reality tool for preparation in MRI

INTRODUCTION

A key part of a radiographer's role within MRI is providing the required emotional support to help patients succeed with a scan. Being informed is important; whilst information leaflets and videos are commonly used, these can be limited in their representation of the experience. Virtual reality tools are being shown to reasonably replicate a scan experience, having a positive impact on patient satisfaction and anxiety. The aim was to obtain the views of practitioners on the use and implementation of such a tool in practice.

METHODS

A mixed methods study was conducted looking at the use of a virtual scan experience for patients prior to MRI. Nine radiographers attended two focus group sessions to see the tool and undergo a virtual experience. Following this, a survey based on the technology acceptance model was completed along with a semi-structured discussion about its use.

RESULTS

Perceived usefulness, ease of use, attitude and intention to use were all positive towards the virtual scan tool. All practitioners saw value in such a tool and how it could be implemented within practice, highlighting areas for improvement and development.

CONCLUSION

The practitioner's perspective was that access to such a virtual scan experience could be of use to better prepare and support those patients needing extra support before a real scan. Acknowledgement of having time to discuss patient concerns was noted and this could provide a means of doing so away from busy scanning lists whilst not taking up additional time.

IMPLICATIONS FOR PRACTICE

Use of VR tools could be a conduit through which trust and rapport are built in advance away from busy scanning lists, thereby not impacting on operational throughput and hindering efficiency.

A closer look at the current knowledge and prospects of artificial intelligence integration in dentistry practice: A cross-sectional study

Background

Healthcare professionals have expressed worries about using AI, while others anticipate more work opportunities in the future and better patient care. Integrating AI into practice will directly impact dentistry practice. The purpose of the study is to evaluate organizational readiness, knowledge, attitude, and willingness to integrate AI into dentistry practice.

Methods

a cross-sectional exploratory study of dentists, academic faculty and students who practice and study dentistry in UAE. Participants were invited to participate in a previously validated survey used to collect participants' demographics, knowledge, perceptions, and organizational readiness.

Results

One hundred thirty-four responded to the survey with a response rate was 78% from the invited group. Results showed excitement to implement AI in practice accompanied by medium to high knowledge and a lack of education and training programs. As a result, organizations were not well prepared and had to ensure readiness for AI implementation.

Conclusion

An effort to ensure professional and student readiness will improve AI integration in practice. In addition, dental professional societies and educational institutions must collaborate to develop proper training programs for dentists to close the knowledge gap.

Can MRI Be Used as a Sensor to Record Neural Activity?

Magnetic resonance provides exquisite anatomical images and functional MRI monitors physiological activity by recording blood oxygenation. This review attempts to answer the following question: Can MRI be used as a sensor to directly record neural behavior? It considers MRI sensing of electrical activity in the heart and in peripheral nerves before turning to the central topic: recording of brain activity. The primary hypothesis is that bioelectric current produced by a nerve or muscle creates a magnetic field that influences the magnetic resonance signal, although other mechanisms for detection are also considered. Recent studies have provided evidence that using MRI to sense neural activity is possible under ideal conditions. Whether it can be used routinely to provide functional information about brain processes in people remains an open question. The review concludes with a survey of artificial intelligence techniques that have been applied to functional MRI and may be appropriate for MRI sensing of neural activity.

Integration of artificial intelligence into nursing practice

Background

Methods

Results

Conclusions

Australian perspectives on artificial intelligence in medical imaging

INTRODUCTION

While artificial intelligence (AI) and recent developments in deep learning (DL) have sparked interest in medical imaging, there has been little commentary on the impact of AI on imaging technologists. The aim of this survey was to understand the attitudes, applications and concerns among nuclear medicine and radiography professionals in Australia with regard to the rapidly emerging applications of AI.

METHODS

An anonymous online survey with invitation to participate was circulated to nuclear medicine and radiography members of the Rural Alliance in Nuclear Scintigraphy and the Australian Society of Medical Imaging and Radiation Therapy. The survey invitations were sent to members via email and as a push via social media with the survey open for 10 weeks. All information collected was anonymised and there is no disclosure of personal information as it was de-identified from commencement.

RESULTS

Among the 102 respondents, there was a high level of acceptance of lower order tasks (e.g. patient registration, triaging and dispensing) and less acceptance of high order task automation (e.g. surgery and interpretation). There was a low priority perception for the role of AI in higher order tasks (e.g. diagnosis, interpretation and decision making) and high priority for those applications that automate complex tasks (e.g. quantitation, segmentation, reconstruction) or improve image quality (e.g. dose / noise reduction and pseudo CT for attenuation correction). Medico-legal, ethical, diversity and privacy issues posed moderate or high concern while there appeared to be no concern regarding AI being clinically useful and improving efficiency. Mild concerns included redundancy, training bias, transparency and validity.

CONCLUSION

Australian nuclear medicine technologists and radiographers recognise important applications of AI for assisting with repetitive tasks, performing less complex tasks and enhancing the quality of outputs in medical imaging. There are concerns relating to ethical aspects of algorithm development and implementation.

Image Quality Control in Lumbar Spine Radiography Using Enhanced U-Net Neural Networks

Purpose

To standardize the radiography imaging procedure, an image quality control framework using the deep learning technique was developed to segment and evaluate lumbar spine x-ray images according to a defined quality control standard.

Materials and Methods

A dataset comprising anteroposterior, lateral, and oblique position lumbar spine x-ray images from 1,389 patients was analyzed in this study. The training set consisted of digital radiography images of 1,070 patients (800, 798, and 623 images of the anteroposterior, lateral, and oblique position, respectively) and the validation set included 319 patients (200, 205, and 156 images of the anteroposterior, lateral, and oblique position, respectively). The quality control standard for lumbar spine x-ray radiography in this study was defined using textbook guidelines of as a reference. An enhanced encoder-decoder fully convolutional network with U-net as the backbone was implemented to segment the anatomical structures in the x-ray images. The segmentations were used to build an automatic assessment method to detect unqualified images. The dice similarity coefficient was used to evaluate segmentation performance.

Results

The dice similarity coefficient of the anteroposterior position images ranged from 0.82 to 0.96 (mean 0.91 ± 0.06); the dice similarity coefficient of the lateral position images ranged from 0.71 to 0.95 (mean 0.87 ± 0.10); the dice similarity coefficient of the oblique position images ranged from 0.66 to 0.93 (mean 0.80 ± 0.14). The accuracy, sensitivity, and specificity of the assessment method on the validation set were 0.971-0.990 (mean 0.98 ± 0.10), 0.714-0.933 (mean 0.86 ± 0.13), and 0.995-1.000 (mean 0.99 ± 0.12) for the three positions, respectively.

Conclusion

This deep learning-based algorithm achieves accurate segmentation of lumbar spine x-ray images. It provides a reliable and efficient method to identify the shape of the lumbar spine while automatically determining the radiographic image quality.

Comment on Patel, B.; Makaryus, A.N. Artificial Intelligence Advances in the World of Cardiovascular Imaging. Healthcare 2022, 10, 154

Regarding Dr. Makaryus's interesting review study [...].

Advanced Tumor Imaging Approaches in Human Tumors

The management of cancer has always relied heavily on the imaging modalities used to detect and monitor it. While many of these modalities have been around for decades, the technology surrounding them is always improving, and much has been discovered in recent years about the nature of tumors because of this. There have been several areas that have aided those discoveries. The use of artificial intelligence has already helped immensely in the quality of images taken but has not yet been widely implemented in clinical settings. Molecular imaging has proven to be useful in diagnosing different types of cancers based on the specificity of the probes/contrast agents used. Intravital imaging has already uncovered new information regarding the heterogeneity of the tumor vasculature. These three areas have provided a lot of useful information for the diagnosis and treatment of cancer, but further research and development in human trials is necessary to allow these techniques to fully utilize the information obtained thus far.

The Artificial Intelligence in Digital Radiology: Part 1: The Challenges, Acceptance and Consensus

Artificial intelligence is having important developments in the world of digital radiology also thanks to the boost given to the research sector by the COVID-19 pandemic. In the last two years, there was an important development of studies focused on both challenges and acceptance and consensus in the field of Artificial Intelligence. The challenges and acceptance and consensus are two strategic aspects in the development and integration of technologies in the health domain. The study conducted two narrative reviews by means of two parallel points of view to take stock both on the ongoing challenges and on initiatives conducted to face the acceptance and consensus in this area. The methodology of the review was based on: (I) search of PubMed and Scopus and (II) an eligibility assessment, using parameters with 5 levels of score. The results have: (a) highlighted and categorized the important challenges in place. (b) Illustrated the different types of studies conducted through original questionnaires. The study suggests for future research based on questionnaires a better calibration and inclusion of the challenges in place together with validation and administration paths at an international level.

The Artificial Intelligence in Digital Radiology: Part 2: Towards an Investigation of acceptance and consensus on the Insiders

Background. The study deals with the introduction of the artificial intelligence in digital radiology. There is a growing interest in this area of scientific research in acceptance and consensus studies involving both insiders and the public, based on surveys focused mainly on single professionals. Purpose. The goal of the study is to perform a contemporary investigation on the acceptance and the consensus of the three key professional figures approaching in this field of application: (1) Medical specialists in image diagnostics: the medical specialists (MS)s; (2) experts in physical imaging processes: the medical physicists (MP)s; (3) AI designers: specialists of applied sciences (SAS)s. Methods. Participants (MSs = 92: 48 males/44 females, averaged age 37.9; MPs = 91: 43 males/48 females, averaged age 36.1; SAS = 90: 47 males/43 females, averaged age 37.3) were properly recruited based on specific training. An electronic survey was designed and submitted to the participants with a wide range questions starting from the training and background up to the different applications of the AI and the environment of application. Results. The results show that generally, the three professionals show (a) a high degree of encouraging agreement on the introduction of AI both in imaging and in non-imaging applications using both standalone applications and/or mHealth/eHealth, and (b) a different consent on AI use depending on the training background. Conclusions. The study highlights the usefulness of focusing on both the three key professionals and the usefulness of the investigation schemes facing a wide range of issues. The study also suggests the importance of different methods of administration to improve the adhesion and the need to continue these investigations both with federated and specific initiatives.

Information Security in Medical Robotics: A Survey on the Level of Training, Awareness and Use of the Physiotherapist

Cybersecurity is becoming an increasingly important aspect to investigate for the adoption and use of care robots, in term of both patients' safety, and the availability, integrity and privacy of their data. This study focuses on opinions about cybersecurity relevance and related skills for physiotherapists involved in rehabilitation and assistance thanks to the aid of robotics. The goal was to investigate the awareness among insiders about some facets of cybersecurity concerning human-robot interactions. We designed an electronic questionnaire and submitted it to a relevant sample of physiotherapists. The questionnaire allowed us to collect data related to: (i) use of robots and its relationship with cybersecurity in the context of physiotherapy; (ii) training in cybersecurity and robotics for the insiders; (iii) insiders' self-assessment on cybersecurity and robotics in some usage scenarios, and (iv) their experiences of cyber-attacks in this area and proposals for improvement. Besides contributing some specific statistics, the study highlights the importance of both acculturation processes in this field and monitoring initiatives based on surveys. The study exposes direct suggestions for continuation of these types of investigations in the context of scientific societies operating in the rehabilitation and assistance robotics. The study also shows the need to stimulate similar initiatives in other sectors of medical robotics (robotic surgery, care and socially assistive robots, rehabilitation systems, training for health and care workers) involving insiders.