Patients' Attitudes Toward the Use of Artificial Intelligence as a Diagnostic Tool in Radiology in Saudi Arabia: Cross-Sectional Study

BACKGROUND

Artificial intelligence (AI) is widely used in various medical fields, including diagnostic radiology as a tool for greater efficiency, precision, and accuracy. The integration of AI as a radiological diagnostic tool has the potential to mitigate delays in diagnosis, which could, in turn, impact patients' prognosis and treatment outcomes. The literature shows conflicting results regarding patients' attitudes to AI as a diagnostic tool. To the best of our knowledge, no similar study has been conducted in Saudi Arabia.

OBJECTIVE

The objectives of this study are to examine patients' attitudes toward the use of AI as a tool in diagnostic radiology at King Khalid University Hospital, Saudi Arabia. Additionally, we sought to explore potential associations between patients' attitudes and various sociodemographic factors.

METHODS

This descriptive-analytical cross-sectional study was conducted in a tertiary care hospital. Data were collected from patients scheduled for radiological imaging through a validated self-administered questionnaire. The main outcome was to measure patients' attitudes to the use of AI in radiology by calculating mean scores of 5 factors, distrust and accountability (factor 1), procedural knowledge (factor 2), personal interaction and communication (factor 3), efficiency (factor 4), and methods of providing information to patients (factor 5). Data were analyzed using the student t test, one-way analysis of variance followed by post hoc and multivariable analysis.

RESULTS

A total of 382 participants (n=273, 71.5% women and n=109, 28.5% men) completed the surveys and were included in the analysis. The mean age of the respondents was 39.51 (SD 13.26) years. Participants favored physicians over AI for procedural knowledge, personal interaction, and being informed. However, the participants demonstrated a neutral attitude for distrust and accountability and for efficiency. Marital status was found to be associated with distrust and accountability, procedural knowledge, and personal interaction. Associations were also found between self-reported health status and being informed and between the field of specialization and distrust and accountability.

CONCLUSIONS

Patients were keen to understand the work of AI in radiology but favored personal interaction with a radiologist. Patients were impartial toward AI replacing radiologists and the efficiency of AI, which should be a consideration in future policy development and integration. Future research involving multicenter studies in different regions of Saudi Arabia is required.

Operational challenges and collaborative solutions in radiology image interpretation: perspectives from imaging departments in a low-resource setting

INTRODUCTION

Medical imaging's critical role in diagnosis requires prompt and precise image interpretation. Numerous radiology departments, especially in low-resourced settings, encounter challenges such as a shortage of radiologists in their operational setup. This study explored the perceptions of radiographers and radiologists from low-resourced departments in a single country regarding operational challenges and potential solutions in image interpretation.

METHODS

A qualitative approach was utilised, involving heads of departments, senior radiographers, and radiologists with a minimum of 5 years of experience, from three major state referral hospitals. Face-to-face, semi-structured interviews were conducted in November 2022, using an interview guide that included questions on the challenges encountered during image interpretation and the proposed solutions. Data analysis was conducted using Atlas.ti version 9.0, following the four-step content analysis method. All participants willingly provided consent to participate in the study.

RESULTS

Ten participants, comprising two radiologists and eight radiographers participated in the study. The research identified three main themes: image interpretation pathways, image interpretation operational challenges and proposed solutions for image interpretation. In addition, a total of 10 subthemes were generated from the three main themes.

CONCLUSION

The study revealed critical challenges and the need to explore the formal inclusion of radiographers in image interpretation, as a way to improve efficiency. However, a comprehensive assessment of the image interpretation system, encompassing radiographers' knowledge and competence, is recommended for context-specific, empirical-based modifications to enhance service provision.

A Critical Examination of Academic Hospital Practices-Paving the Way for Standardized Structured Reports in Neuroimaging

Background/Objectives: Imaging studies are often an integral part of patient evaluation and serve as the primary means of communication between radiologists and referring physicians. This study aimed to evaluate brain Magnetic Resonance Imaging (MRI) reports and to determine whether these reports follow a standardized or narrative format. Methods: A series of 466 anonymized MRI reports from an academic hospital were downloaded from the Picture Archiving and Communication System (PACS) in portable document format (pdf) for the period between August 2017 and March 2018. Two hundred brain MRI reports, written by four radiologists, were compared to a structured report template from the Radiology Society of North America (RSNA) and were included, whereas MR-modified techniques, such as MRI orbits and MR venography reports, were excluded (n = 266). All statistical analyses were conducted using Statistical Package for the Social Sciences (SPSS) statistical software (version 16.4.1, MedCalc Software). Results: None of the included studies used the RSNA template for structured reports (SRs). The highest number of brain-reported pathologies was for vascular disease (24%), while the lowest was for infections (3.5%) and motor dysfunction (5.5%). Radiologists specified the Technique (n = 170, 85%), Clinical Information (n = 187, 93.5%), and Impression (n = 197, 98.5%) in almost all reports. However, information in the Findings section was often missing. As hypothesized, radiologists with less experience showed a greater commitment to reporting additional elements than those with more experience. Conclusions: The SR template for medical imaging has been accessible online for over a decade. However, many hospitals and radiologists still use the free-text style for reporting. Our study was conducted in an academic hospital with a fellowship program, and we found that structured reporting had not yet been implemented. As the health system transitions towards teleservices and teleradiology, more efforts need to be put into advocating standardized reporting in medical imaging.

Lived experiences of radiology caregivers during a health crisis: A COVID-19 case analysis

Background

Health crises have been linked with the exacerbation of pre-existing difficulties and the emergence of unique challenges, as evidenced by the impact of coronavirus disease 2019 (COVID-19) on health caregivers worldwide. Baseline data allow for reflection and preparation for any future health emergencies therefore giving impetus to phenomenological enquiries among the experiencers.

Aim

This study aimed to explore the lived experiences of the eThekwini district frontline radiology caregivers during the COVID-19 pandemic.

Setting

The study was conducted in public and private radiology departments in the eThekwini district of KwaZulu-Natal, South Africa.

Methods

A qualitative, interpretative phenomenological analysis methodology was adopted in a multi-method data-collection context comprising semi-structured interviews and focus group discussions (FGDs) among 24 radiologists, radiographers, and radiology nurses obtained by non-probability sampling. Data were transcribed verbatim and analysed using an interpretative phenomenological approach.

Results

Three superordinate themes emerged, namely: (1) duties and roles during the COVID-19 pandemic, (2) work-related challenges, (3) personal challenges.

Conclusion

Frontline radiology caregivers experienced increased workload, staff shortages, salary cuts, personal protective equipment (PPE) shortages, non-recognition, poor managerial support, disrupted social relations, and poor work-life balance. This necessitates the need for the radiology departments to address staffing, infection prevention and control deficits, and invest in support interventions to assist frontline radiology caregivers during health crises.

Contribution

The findings comprise baseline information that can be used for reflection and guiding radiology departments in preparing for any future health crises.

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.

Network analysis of occupational stress and job satisfaction among radiologists

Background

Occupational stress and job satisfaction significantly impact the well-being and performance of healthcare professionals, including radiologists. Understanding the complex interplay between these factors through network analysis can provide valuable insights into intervention strategies to enhance workplace satisfaction and productivity.

Method

In this study, a convenience sampling method was used to recruit 312 radiologists for participation. Data on socio-demographic characteristics, job satisfaction measured by the Minnesota job satisfaction questionnaire revised short version (MJSQ-RSV), and occupational stress assessed using the occupational stress scale. Network analysis was employed to analyze the data in this study.

Results

The network analysis revealed intricate patterns of associations between occupational stress and job satisfaction symptoms among radiologists. Organizational management and occupational interests emerged as crucial nodes in the network, indicating strong relationships within these domains. Additionally, intrinsic satisfaction was identified as a central symptom with high connectivity in the network structure. The stability analysis demonstrated robustness in the network edges and centrality metrics, supporting the reliability of the findings.

Conclusion

This study sheds light on the complex relationships between occupational stress and job satisfaction in radiologists, offering valuable insights for targeted interventions and support strategies to promote well-being and job satisfaction in healthcare settings.

Medico-Legal Cases in Breast Imaging in Canada: A Trend Analysis

Purpose: Breast imaging accounts for a large proportion of medico-legal cases involving radiologists in several countries and may be a disincentive to breast imaging. As this has not been well studied in Canada, we evaluated the key medico-legal issues of breast imaging in Canada and their implications for health care providers and patient safety. Methods: In collaboration with Canadian Medical Protective Association (CMPA), we obtained information from the medico-legal repository, including civil-legal, medical regulatory authority (College) and hospital complaints occurring between 2002-2021. Canadian Classification of Health Interventions (CCI) codes were used for breast imaging and biopsy. Trend analysis was done comparing cases involving breast imaging/biopsy to all cases where a radiologist was named. Results: Radiologists were named in 3108 medico-legal cases, 188 (6%, 188/3108) of which were CCI coded for breast imaging or biopsy. Factors related to radiologists were most frequent (64%, 120/188), followed by team (23.4%, 44/188) and system (6.9%, 13/188). Equal representation of male and female radiologists was found (IRR = 1.22; 95% CI: .89, 1.56). In a 10-year test window from 2006 - 2015 we identified an increasing trend for all cases involving radiologists (P = 0,0128) but a decreasing trend for cases coded with breast imaging or biopsy (P = 0,0099). Conclusions: A significant decrease in cases involving breast imaging were found from 2006-2015, accounting for 6% of the medico-legal cases. The lower risk of breast imaging medico-legal issues may encourage more radiologists in breast imaging.

Mounier-Kuhn syndrome in poorly controlled asthma

INTRODUCTION

Mounier-Kuhn syndrome or tracheobronchomegaly, is a rare condition that consists of abnormal dilation of the trachea and main bronchi due to a pathological arrangement of smooth muscle fibers in this area.

CASE REPORT

We present the case of a 46-year-old woman with poorly controlled asthma and recurrent infections, who was diagnosed with Mounier-Kuhn syndrome through a computed tomography scan revealing an unusual enlargement of the trachea with associated bronchiectasis.

RESULTS

The diagnosis of Mounier-Kuhn syndrome is radiological, involving measurement of the trachea where a diameter >25 mm in men and >21 mm in women is observed. While diagnosis is sometimes incidental, there is an association with respiratory diseases such as asthma or COPD, hence clinical suspicion is important in patients with poorly controlled underlying conditions who present with recurrent infections, inadequate secretion management, or even hemoptysis.

CONCLUSIONS

Despite its rarity, this syndrome significantly impacts patients' quality of life. Diagnosis and management involve comprehensive evaluations including computed tomography, with a multidisciplinary approach including pulmonologists and radiologists. Exploring its clinical features, associations with other respiratory diseases and treatment options is crucial in managing this rare respiratory condition.

Applying machine-learning models to differentiate benign and malignant thyroid nodules classified as C-TIRADS 4 based on 2D-ultrasound combined with five contrast-enhanced ultrasound key frames

Objectives

To apply machine learning to extract radiomics features from thyroid two-dimensional ultrasound (2D-US) combined with contrast-enhanced ultrasound (CEUS) images to classify and predict benign and malignant thyroid nodules, classified according to the Chinese version of the thyroid imaging reporting and data system (C-TIRADS) as category 4.

Materials and methods

This retrospective study included 313 pathologically diagnosed thyroid nodules (203 malignant and 110 benign). Two 2D-US images and five CEUS key frames ("2nd second after the arrival time" frame, "time to peak" frame, "2nd second after peak" frame, "first-flash" frame, and "second-flash" frame) were selected to manually label the region of interest using the "Labelme" tool. A total of 7 images of each nodule and their annotates were imported into the Darwin Research Platform for radiomics analysis. The datasets were randomly split into training and test cohorts in a 9:1 ratio. Six classifiers, namely, support vector machine, logistic regression, decision tree, random forest (RF), gradient boosting decision tree and extreme gradient boosting, were used to construct and test the models. Performance was evaluated using a receiver operating characteristic curve analysis. The area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy (ACC), and F1-score were calculated. One junior radiologist and one senior radiologist reviewed the 2D-US image and CEUS videos of each nodule and made a diagnosis. We then compared their AUC and ACC with those of our best model.

Results

The AUC of the diagnosis of US, CEUS and US combined CEUS by junior radiologist and senior radiologist were 0.755, 0.750, 0.784, 0.800, 0.873, 0.890, respectively. The RF classifier performed better than the other five, with an AUC of 1 for the training cohort and 0.94 (95% confidence interval 0.88-1) for the test cohort. The sensitivity, specificity, accuracy, PPV, NPV, and F1-score of the RF model in the test cohort were 0.82, 0.93, 0.90, 0.85, 0.92, and 0.84, respectively. The RF model with 2D-US combined with CEUS key frames achieved equivalent performance as the senior radiologist (AUC: 0.94 vs. 0.92, P = 0.798; ACC: 0.90 vs. 0.92) and outperformed the junior radiologist (AUC: 0.94 vs. 0.80, P = 0.039, ACC: 0.90 vs. 0.81) in the test cohort.

Conclusions

Our model, based on 2D-US and CEUS key frames radiomics features, had good diagnostic efficacy for thyroid nodules, which are classified as C-TIRADS 4. It shows promising potential in assisting less experienced junior radiologists.

The Impact of Expectation Management and Model Transparency on Radiologists' Trust and Utilization of AI Recommendations for Lung Nodule Assessment on Computed Tomography: Simulated Use Study

BACKGROUND

Many promising artificial intelligence (AI) and computer-aided detection and diagnosis systems have been developed, but few have been successfully integrated into clinical practice. This is partially owing to a lack of user-centered design of AI-based computer-aided detection or diagnosis (AI-CAD) systems.

OBJECTIVE

We aimed to assess the impact of different onboarding tutorials and levels of AI model explainability on radiologists' trust in AI and the use of AI recommendations in lung nodule assessment on computed tomography (CT) scans.

METHODS

In total, 20 radiologists from 7 Dutch medical centers performed lung nodule assessment on CT scans under different conditions in a simulated use study as part of a 2×2 repeated-measures quasi-experimental design. Two types of AI onboarding tutorials (reflective vs informative) and 2 levels of AI output (black box vs explainable) were designed. The radiologists first received an onboarding tutorial that was either informative or reflective. Subsequently, each radiologist assessed 7 CT scans, first without AI recommendations. AI recommendations were shown to the radiologist, and they could adjust their initial assessment. Half of the participants received the recommendations via black box AI output and half received explainable AI output. Mental model and psychological trust were measured before onboarding, after onboarding, and after assessing the 7 CT scans. We recorded whether radiologists changed their assessment on found nodules, malignancy prediction, and follow-up advice for each CT assessment. In addition, we analyzed whether radiologists' trust in their assessments had changed based on the AI recommendations.

RESULTS

Both variations of onboarding tutorials resulted in a significantly improved mental model of the AI-CAD system (informative P=.01 and reflective P=.01). After using AI-CAD, psychological trust significantly decreased for the group with explainable AI output (P=.02). On the basis of the AI recommendations, radiologists changed the number of reported nodules in 27 of 140 assessments, malignancy prediction in 32 of 140 assessments, and follow-up advice in 12 of 140 assessments. The changes were mostly an increased number of reported nodules, a higher estimated probability of malignancy, and earlier follow-up. The radiologists' confidence in their found nodules changed in 82 of 140 assessments, in their estimated probability of malignancy in 50 of 140 assessments, and in their follow-up advice in 28 of 140 assessments. These changes were predominantly increases in confidence. The number of changed assessments and radiologists' confidence did not significantly differ between the groups that received different onboarding tutorials and AI outputs.

CONCLUSIONS

Onboarding tutorials help radiologists gain a better understanding of AI-CAD and facilitate the formation of a correct mental model. If AI explanations do not consistently substantiate the probability of malignancy across patient cases, radiologists' trust in the AI-CAD system can be impaired. Radiologists' confidence in their assessments was improved by using the AI recommendations.

Synergy between formal and informal education

The informal components of education can shape a person's capacity to contribute. Such informal components might include cultural backgrounds, work experiences, and extracurricular pursuits. To appreciate the synergy between formal and informal education it can be helpful to explore a particular case of someone who actually combined the two to make the whole more than the sum of its parts.

Radiologists' experiences and perceptions regarding the use of teleradiology in South Africa

Background

Teleradiology was implemented in South Africa in 1999, but the subsequent uptake was low and slow. The onset of the coronavirus disease 2019 (COVID-19) pandemic catapulted South African healthcare into the arena of teleradiology. This created the environment for re-examining the factors that enable or inhibit the uptake of teleradiology in both the public and private sectors.

Objectives

This article reports on a study of a select sample of private and public sector radiologists' experiences with, and perceptions of, the benefits, opportunities, challenges and barriers to the implementation of teleradiology in the South African context.

Method

Qualitative data on the perceived benefits and challenges of teleradiology, as well as on its enablers and the barriers to its implementation, were collected and analysed.

Results

The uptake of teleradiology in the sample increased by 15.9% during the COVID-19 pandemic. The results demonstrated that teleradiology was perceived to have clear benefits on operational, personal and societal levels.

Conclusion

It is important to address structural barriers to the implementation of teleradiology. Clear communication strategies and multistakeholder engagement are also required.

Contribution

By investigating radiologists' experience with teleradiology, this study provides an understanding of the benefits, opportunities, challenges and barriers to implementation of services. These insights enable informed decision-making and stakeholder engagement and provide a foundation for establishing recommendations for the viable implementation of teleradiology in South Africa and other lower- and middle-income countries to promote access to healthcare.

Towards equality: gender representation at the Royal College of Radiologists' Annual Scientific Meeting 2014-2021

Background: Conferences facilitate career advancement, but gender imbalances in public fora may negatively impact both women and men, and society. We aimed to describe the gender distribution of presenters at the UK's 2014-2021 Royal College of Radiologists' (RCR) Annual Scientific Meeting. Methods: We extracted data on presenter name, role and session type from meeting programmes. We classified gender as male or female using names, records or personal pronouns, accepting the limitations of these categories. We classified roles by prestige: lead, other (speakers and workshop faculty), proffered paper or poster presenters. We calculated odds ratios (OR) and 95% confidence intervals (CI) for associations between gender and binary outcomes using logistic regression.  Results: Women held 1,059 (37.5%) of 2,826 conference roles and presented 9/27 keynotes. Compared to men, women were less likely to hold other roles such as speakers and workshop faculty (OR 0.72 95% CI 0.61-0.83), and more likely to present posters (OR 1.49 95% CI 1.27-1.76). There were 60 male-only and eight women-only multi-presenter sessions. Sessions led by women had higher proportions of women speakers. The odds of roles being held by women increased during online meetings during COVID in 2020 and 2021 (OR 1.61, 95% CI 1.36-1.91) compared to earlier years. Conclusion: The proportion of women presenters and keynote speakers reflects that of RCR membership, but not of wider society. Disadvantage starts from the earliest career stages, prejudicing career opportunities. Efforts to improve inclusion and diversity are needed; focusing on lead roles and hybrid online/in-person formats may accelerate change.

[Role of Interventional Radiologists in Trauma Centers]

Based on statistics available in Korea, trauma centers play a critical role in treatment of patients with trauma. Interventional radiologists in trauma centers perform various procedures, including embolization, which constitutes the basic treatment for control of hemorrhage, although interventions such as stent graft insertion may also be used. Although emergency interventional procedures have been used conventionally, rapid and effective hemorrhage control is important in patients with trauma. Therefore, it is important to accurately understand and implement the concept of damage control interventional radiology, which has gained attention in recent times, to reduce preventable trauma-induced mortality rates.

Developing a Supplementary Diagnostic Tool for Breast Cancer Risk Estimation Using Ensemble Transfer Learning

Breast cancer is the most prevalent cancer worldwide. Thus, it is necessary to improve the efficiency of the medical workflow of the disease. Therefore, this study aims to develop a supplementary diagnostic tool for radiologists using ensemble transfer learning and digital mammograms. The digital mammograms and their associated information were collected from the department of radiology and pathology at Hospital Universiti Sains Malaysia. Thirteen pre-trained networks were selected and tested in this study. ResNet101V2 and ResNet152 had the highest mean PR-AUC, MobileNetV3Small and ResNet152 had the highest mean precision, ResNet101 had the highest mean F1 score, and ResNet152 and ResNet152V2 had the highest mean Youden J index. Subsequently, three ensemble models were developed using the top three pre-trained networks whose ranking was based on PR-AUC values, precision, and F1 scores. The final ensemble model, which consisted of Resnet101, Resnet152, and ResNet50V2, had a mean precision value, F1 score, and Youden J index of 0.82, 0.68, and 0.12, respectively. Additionally, the final model demonstrated balanced performance across mammographic density. In conclusion, this study demonstrates the good performance of ensemble transfer learning and digital mammograms in breast cancer risk estimation. This model can be utilised as a supplementary diagnostic tool for radiologists, thus reducing their workloads and further improving the medical workflow in the screening and diagnosis of breast cancer.

Effectiveness of ultrasound screening in right upper quadrant pain: A comparative study in a basic emergency service

Background and Aims

The use of ultrasound screening is primarily facilitated by point-of-care ultrasound (POCUS) and its integration into healthcare systems is a result of the versatility of this imaging technique. This study intends to compare the accuracy and pertinence of sonographic findings obtained by a sonographer in a Basic Emergency Service (BES) with that of radiologists at referral hospital (RH) in Portugal.

Methods

Twenty patients with right upper quadrant (RUQ) pain and suspected cholecystitis or biliary pathology underwent sonography screening using POCUS in the BES. They were then forwarded to the RH where a radiologist performed a conventional ultrasound exam on the same patients. The results of both exams were compared to determine if the findings obtained in the BES were confirmed by the radiologist in the RH.

Results

In our sample, 60% of cases were related to biliary pathology, 20% were liver-related, 10% had hepatopancreatic biliary etiology, and 10% had unknown etiology. A strong association between the sonographic findings in the BES and the RH was found in the variables "Sonographic Murphy sign" (V = 0.859; p = 0.001), "Cholelithiasis/Gallbladder sludge" (V = 0.840; p = 0.001), and "Intrahepatic biliary tract dilatation" (V = 0.717; p = 0.006). Adequate measures of agreement between the findings of the radiographer and radiologist were obtained for the "Sonographic Murphy sign" (k = 0.664; p = 0.001) and the presence of "Cholelithiasis/Gallbladder sludge" (k = 0.712; p = 0.000).

Conclusion

Major biliary abnormalities were detected in patients with RUQ pain in BES using sonography. The correlation between the sonographic findings obtained by the sonographers at BES and those obtained by radiologists at the RH in Portugal was strong, showing that POCUS screening could be extended to other similar settings; however, more studies are needed.

Awareness of Artificial Intelligence in Medical Imaging Among Radiologists and Radiologic Technologists

BACKGROUND

Current technological developments in medical imaging are primarily focused on increasing the integration of artificial intelligence (AI) into all medical imaging modalities. They are already considered capable of handling tasks such as image reconstruction, processing (denoising, segmentation), analysis, and predictive modeling. The purpose of this study is to assess the awareness (knowledge, attitudes, and practices) of radiologists and radiologic technologists regarding AI in medical imaging.

MATERIALS AND METHODS

This cross-sectional, qualitative study focuses on radiologists and radiologic technologists in Saudi Arabia, Sudan, and Yemen. A self-administered questionnaire based on published studies was used to collect primary data. Version 25.0 of IBM SPSS Statistics (IBM Corp., Armonk, NY) was used for the statistical analysis. The demographics were summarized as frequency and percentage. Independent samples t-tests and ANOVA tests were used to evaluate and compare the degree of AI awareness among the study groups.

RESULTS

A total of 210 individuals completed the survey. According to demographic information, there were 134 (63.8%) radiologic technologists and 76 radiologists (36.2%). Of the participants, 131 (62%) were male, while 79 (37.6%) were female. A total of 130 (61.9%) of the targeted respondents had a positive attitude, 105 (50%) had appropriate practice, and 122 (58.1%) of them were informed (knowledgeable) about AI in medical imaging. There was a significant difference in knowledge awareness between radiologists and radiologic technologists (p-value: <0.05). Radiologists were more knowledgeable than radiologic technologists, and females were more knowledgeable than males (p-value: 0.049). For attitude awareness, there were no significant differences regarding specialization, gender, age, academic qualification, and experience (p-value > 0.05). Regarding practice awareness, it turned out that females are more knowledgeable than males (p-value: 0.007). Additionally, it was discovered that significant differences indicated that bachelor's degree holders have a higher level of practice awareness than diploma holders (p-value: <0.05).

CONCLUSION

Significant differences between the respondent's knowledge awareness regarding specialization, gender, and experience are linked with relatively sufficient AI-basic knowledge and positive attitude awareness among radiologists and radiologic technologists. Only half of the study participants had appropriate practical awareness; therefore, additional training could enhance practical awareness.

Assessment of the statistical optimization strategies and clinical evaluation of an artificial intelligence-based automated diagnostic system for thyroid nodule screening

Background

Thyroid cancer is the most common endocrine cancer in the world. Accurately distinguishing between benign and malignant thyroid nodules is particularly important for the early diagnosis and treatment of thyroid cancer. This study aimed to investigate the best possible optimization strategies for an already-trained artificial intelligence (AI)-based automated diagnostic system for thyroid nodule screening and, in addition, to scrutinize the clinically relevant limitations using stratified analysis to better standardize the application in clinical workflows.

Methods

We retrospectively reviewed a total of 1,092 ultrasound images associated with 397 thyroid nodules collected from 287 patients between April 2019 and January 2021, applying postoperative pathology as the gold standard. We applied different statistical approaches, including averages, maximums, and percentiles, to estimate per-nodule-based malignancy scores from the malignancy scores per image predicted by AI-SONIC Thyroid v. 5.3.0.2 (Demetics Medical Technology Ltd., Hangzhou, China) system, and we assessed its diagnostic efficacy on nodules of different sizes or tumor types with per-nodule analysis using performance metrics.

Results

Of the 397 thyroid nodules, 272 thyroid nodules were overrepresented by malignant nodules according to the results of the surgical pathological examinations. Taking the median of the malignancy scores per image to estimate the nodule-based score with a cutoff value of 0.56 optimized for the means of sensitivity and specificity, the AI-based automated detection system demonstrated slightly lower sensitivity, significantly higher specificity (almost independent of nodule size), and similar accuracy to that of the senior radiologist. Both the AI system and the senior radiologist demonstrated higher sensitivity in diagnosing smaller nodules (≤25 mm) and comparable diagnostic performances for larger nodules. The mean diagnostic time per nodule of the AI system was 0.146 s, which was in sharp contrast to the 2.8 to 4.5 min of the radiologists.

Conclusions

Using our optimization strategy to achieve nodule-based diagnosis, the AI-SONIC Thyroid automated diagnostic system demonstrated an overall diagnostic accuracy equivalent to that of senior radiologists. Thus, it is expected that it can be used as a reliable auxiliary diagnostic method by radiologists for the screening and preoperative evaluation of malignant thyroid nodules.

Visual hindsight bias for abnormal mammograms in radiologists

Purpose

Hindsight bias-where people falsely believe they can accurately predict something once they know about it-is a pervasive decision-making phenomenon, including in the interpretation of radiological images. Evidence suggests it is not only a decision-making phenomenon but also a visual perception one, where prior information about an image enhances our visual perception of the contents of that image. The current experiment investigates to what extent expert radiologists perceive mammograms with visual abnormalities differently when they know what the abnormality is (a visual hindsight bias), above and beyond being biased at a decision level.

Approach

N=40 experienced mammography readers were presented with a series of unilateral abnormal mammograms. After each case, they were asked to rate their confidence on a 6-point scale that ranged from confident mass to confident calcification. We used the random image structure evolution method, where the images repeated in an unpredictable order and with varied noise, to ensure any biases were visual, not cognitive.

Results

Radiologists who first saw an original image with no noise were more accurate in the max noise level condition [area under the curve (AUC)=0.60] than those who first saw the degraded images (AUC=0.55; difference: p=0.005), suggesting that radiologists' visual perception of medical images is enhanced by prior visual experience with the abnormality.

Conclusions

Overall, these results provide evidence that expert radiologists experience not only decision level but also visual hindsight bias, and have potential implications for negligence lawsuits.

Comparison of Diagnostic Performance in Mammography Assessment: Radiologist with Reference to Clinical Information Versus Standalone Artificial Intelligence Detection

We compared diagnostic performances between radiologists with reference to clinical information and standalone artificial intelligence (AI) detection of breast cancer on digital mammography. This study included 392 women (average age: 57.3 ± 12.1 years, range: 30−94 years) diagnosed with malignancy between January 2010 and June 2021 who underwent digital mammography prior to biopsy. Two radiologists assessed mammographic findings based on clinical symptoms and prior mammography. All mammographies were analyzed via AI. Breast cancer detection performance was compared between radiologists and AI based on how the lesion location was concordant between each analysis method (radiologists or AI) and pathological results. Kappa coefficient was used to measure the concordance between radiologists or AI analysis and pathology results. Binominal logistic regression analysis was performed to identify factors influencing the concordance between radiologists’ analysis and pathology results. Overall, the concordance was higher in radiologists’ diagnosis than on AI analysis (kappa coefficient: 0.819 vs. 0.698). Impact of prior mammography (odds ratio (OR): 8.55, p < 0.001), clinical symptom (OR: 5.49, p < 0.001), and fatty breast density (OR: 5.18, p = 0.008) were important factors contributing to the concordance of lesion location between radiologists’ diagnosis and pathology results.

Inter- and Intra-Observer Agreement When Using a Diagnostic Labeling Scheme for Annotating Findings on Chest X-rays-An Early Step in the Development of a Deep Learning-Based Decision Support System

Consistent annotation of data is a prerequisite for the successful training and testing of artificial intelligence-based decision support systems in radiology. This can be obtained by standardizing terminology when annotating diagnostic images. The purpose of this study was to evaluate the annotation consistency among radiologists when using a novel diagnostic labeling scheme for chest X-rays. Six radiologists with experience ranging from one to sixteen years, annotated a set of 100 fully anonymized chest X-rays. The blinded radiologists annotated on two separate occasions. Statistical analyses were done using Randolph's kappa and PABAK, and the proportions of specific agreements were calculated. Fair-to-excellent agreement was found for all labels among the annotators (Randolph's Kappa, 0.40-0.99). The PABAK ranged from 0.12 to 1 for the two-reader inter-rater agreement and 0.26 to 1 for the intra-rater agreement. Descriptive and broad labels achieved the highest proportion of positive agreement in both the inter- and intra-reader analyses. Annotating findings with specific, interpretive labels were found to be difficult for less experienced radiologists. Annotating images with descriptive labels may increase agreement between radiologists with different experience levels compared to annotation with interpretive labels.

Early stage of radiological expertise modulates resting-state local coherence in the inferior temporal lobe

Background

The visual system and its inherent functions undergo experience-dependent changes through the lifespan, enabling acquisition of new skills. Previous fMRI studies using tasks reported increased specialization in a number of cortical regions subserving visual expertise. Although ample studies focused on representation of long-term visual expertise in the brain, i.e. in terms of year, monthly-based early-stage representation of visual expertise remains unstudied. Given that spontaneous neuronal oscillations actively encode previous experience, we propose brain representations in the resting state is fundamentally important.

Objective

The current study aimed to investigate how monthly-based early-stage visual expertise are represented in the resting state using the expertise model of radiologists.

Methods

In particular, we investigated the altered local clustering pattern of spontaneous brain activity using regional homogeneity (ReHo). A cohort group of radiology interns (n = 22) after one-month training in X-ray department and matched laypersons (n = 22) were recruited after rigorous behavioral assessment.

Results

The results showed higher ReHo in the right hippocampus (HIP) and the right ventral anterior temporal lobe (vATL) (corrected by Alphasim correction, P < 0.05). Moreover, ReHo in the right HIP correlated with the number of cases reviewed during intern radiologists' training (corrected by Alphasim correction, P < 0.05).

Conclusions

In sum, our results demonstrated that the early stage of visual expertise is more concerned with stabilizing visual feature and domain-specific knowledge into long-term memory. The results provided novel evidence regarding how early-stage visual expertise is represented in the resting brain, which help further elaborate how human visual expertise is acquired. We propose that our current study may provide novel ideas for developing new training protocols in medical schools.

Canadian Radiology Gender Pay Gap-Reality or Myth?

Introduction: Prior studies on Canadian physicians' income have demonstrated a gender pay gap (GPG); however, there is a paucity of data in the Radiology specialty. A cross-sectional study was conducted to determine if practicing Canadian radiologists' self-reported income is related to gender, controlling for demographic and work variables. Methods: English and French online surveys were distributed by email and social media to radiologists and trainees (May-July 2021). The association between Gender (controlling for Ethnicity variables, Region, having Children, Full-/Part-Time work, and Academic position) and Self-Reported Income was examined using chi-square tests. Pearson correlations examined relationships between opinion variables. Analyses were conducted using SPSS V28.0. A priori significance was P < .05. Study had ethics approval. Results: Four hundred and fifty-four practicing Canadian radiologists responded. Majority were women (51.2%, n = 227), a non-visible Minority (71.7%, n = 317), and from Western Provinces (67.8%, n = 308). Significant relationship was established between Self-Reported Income and Gender (χ2 = 10.44, df = 2, P < .05). More men (70.6%, n = 120) than women (56.4%, n = 110), reported income "greater than $500 000"; fewer men (20.6%, n = 35) than women (35.9%, n = 70) reported "$300 000-$500 000"; a similar percent of men (8.8%, n = 15) and women (7.7%, n = 15) reported "less than $300 000." No relationship was found between self-reported income and gender for ethnicity variables, those without children, part-time, or non-academic radiologists. The opinion "Addressing the GPG is important" correlated to "Canadian Association of Radiologists should collect demographic data" (r = 0.63). Responses were low for ethnic minorities and non-western provinces. Conclusion: Our results suggest a GPG exists in Canadian radiology and is an important first step for future studies.