ChatGPT: Evaluating answers on contrast media related questions and finetuning by providing the model with the ESUR guideline on contrast agents

OBJECTIVE

This study aimed to assess the feasibility of GPT-4 for answering questions related to contrast media with and without the context of the European Society of Urogenital Radiology (ESUR) guideline on contrast agents. The overarching goal was to determine whether contextual enrichment by providing guideline information improves answers of GPT-4 for clinical decision-making in radiology.

METHODS

A set of 64 questions, based on the ESUR guideline on contrast agents mirroring pertinent sections, was developed and posed to GPT-4 both directly and after providing the guideline using a plugin. Responses were graded by experienced radiologists for quality of information and accuracy in pinpointing information from the guideline as well as by radiology residents for utility, using Likert-scales.

RESULTS

GPT-4's performance improved significantly with the guideline. Without the guideline, average quality rating was 3.98, which increased to 4.33 with the guideline (p = 0036). In terms of accuracy, 82.3% of answers matched the information from the guideline. Utility scores also reflected a significant improvement with the guideline, with average scores of 4.1 (without) and 4.4 (with) (p = 0.008) with a Fleiss´ Kappa of 0.44.

CONCLUSION

GPT-4, when contextually enriched with a guideline, demonstrates enhanced capability in providing guideline-backed recommendations. This approach holds promise for real-time clinical decision-support, making guidelines more actionable. However, further refinements are necessary to maximize the potential of large language models (LLMs). Inherent limitations need to be addressed.

Integrating and Adopting AI in the Radiology Workflow: A Primer for Standards and Integrating the Healthcare Enterprise (IHE) Profiles

The deployment of artificial intelligence (AI) solutions in radiology practice creates new demands on existing imaging workflow. Accommodating custom integrations creates a substantial operational and maintenance burden. These custom integrations also increase the likelihood of unanticipated problems. Standards-based interoperability facilitates AI integration with systems from different vendors into a single environment by enabling seamless exchange between information systems in the radiology workflow. Integrating the Healthcare Enterprise (IHE) is an initiative to improve how computer systems share information across health care domains, including radiology. IHE integrates existing standards-such as Digital Imaging and Communications in Medicine, Health Level Seven, and health care lexicons and ontologies (ie, LOINC, RadLex, SNOMED Clinical Terms)-by mapping data elements from one standard to another. IHE Radiology manages profiles (standards-based implementation guides) for departmental workflow and information sharing across care sites, including profiles for scaling AI processing traffic and integrating AI results. This review focuses on the need for standards-based interoperability to scale AI integration in radiology, including a brief review of recent IHE profiles that provide a framework for AI integration. This review also discusses challenges and additional considerations for AI integration, including technical, clinical, and policy perspectives.

Evaluating the performance of Generative Pre-trained Transformer-4 (GPT-4) in standardizing radiology reports

OBJECTIVE

Radiology reporting is an essential component of clinical diagnosis and decision-making. With the advent of advanced artificial intelligence (AI) models like GPT-4 (Generative Pre-trained Transformer 4), there is growing interest in evaluating their potential for optimizing or generating radiology reports. This study aimed to compare the quality and content of radiologist-generated and GPT-4 AI-generated radiology reports.

METHODS

A comparative study design was employed in the study, where a total of 100 anonymized radiology reports were randomly selected and analyzed. Each report was processed by GPT-4, resulting in the generation of a corresponding AI-generated report. Quantitative and qualitative analysis techniques were utilized to assess similarities and differences between the two sets of reports.

RESULTS

The AI-generated reports showed comparable quality to radiologist-generated reports in most categories. Significant differences were observed in clarity (p = 0.027), ease of understanding (p = 0.023), and structure (p = 0.050), favoring the AI-generated reports. AI-generated reports were more concise, with 34.53 fewer words and 174.22 fewer characters on average, but had greater variability in sentence length. Content similarity was high, with an average Cosine Similarity of 0.85, Sequence Matcher Similarity of 0.52, BLEU Score of 0.5008, and BERTScore F1 of 0.8775.

CONCLUSION

The results of this proof-of-concept study suggest that GPT-4 can be a reliable tool for generating standardized radiology reports, offering potential benefits such as improved efficiency, better communication, and simplified data extraction and analysis. However, limitations and ethical implications must be addressed to ensure the safe and effective implementation of this technology in clinical practice.

CLINICAL RELEVANCE STATEMENT

The findings of this study suggest that GPT-4 (Generative Pre-trained Transformer 4), an advanced AI model, has the potential to significantly contribute to the standardization and optimization of radiology reporting, offering improved efficiency and communication in clinical practice.

KEY POINTS

• Large language model-generated radiology reports exhibited high content similarity and moderate structural resemblance to radiologist-generated reports. • Performance metrics highlighted the strong matching of word selection and order, as well as high semantic similarity between AI and radiologist-generated reports. • Large language model demonstrated potential for generating standardized radiology reports, improving efficiency and communication in clinical settings.

Explanation and Elaboration with Examples for CLEAR (CLEAR-E3): an EuSoMII Radiomics Auditing Group Initiative

Overall quality of radiomics research has been reported as low in literature, which constitutes a major challenge to improve. Consistent, transparent, and accurate reporting is critical, which can be accomplished with systematic use of reporting guidelines. The CheckList for EvaluAtion of Radiomics research (CLEAR) was previously developed to assist authors in reporting their radiomic research and to assist reviewers in their evaluation. To take full advantage of CLEAR, further explanation and elaboration of each item, as well as literature examples, may be useful. The main goal of this work, Explanation and Elaboration with Examples for CLEAR (CLEAR-E3), is to improve CLEAR's usability and dissemination. In this international collaborative effort, members of the European Society of Medical Imaging Informatics-Radiomics Auditing Group searched radiomics literature to identify representative reporting examples for each CLEAR item. At least two examples, demonstrating optimal reporting, were presented for each item. All examples were selected from open-access articles, allowing users to easily consult the corresponding full-text articles. In addition to these, each CLEAR item's explanation was further expanded and elaborated. For easier access, the resulting document is available at https://radiomic.github.io/CLEAR-E3/ . As a complementary effort to CLEAR, we anticipate that this initiative will assist authors in reporting their radiomics research with greater ease and transparency, as well as editors and reviewers in reviewing manuscripts.Relevance statement Along with the original CLEAR checklist, CLEAR-E3 is expected to provide a more in-depth understanding of the CLEAR items, as well as concrete examples for reporting and evaluating radiomic research.Key points• As a complementary effort to CLEAR, this international collaborative effort aims to assist authors in reporting their radiomics research, as well as editors and reviewers in reviewing radiomics manuscripts.• Based on positive examples from the literature selected by the EuSoMII Radiomics Auditing Group, each CLEAR item explanation was further elaborated in CLEAR-E3.• The resulting explanation and elaboration document with examples can be accessed at  https://radiomic.github.io/CLEAR-E3/ .

Competency based curriculum for cardiovascular magnetic resonance: A position statement of the Society for Cardiovascular Magnetic Resonance

This position statement guides cardiovascular magnetic resonance (CMR) imaging program directors and learners on the key competencies required for Level II and III CMR practitioners, whether trainees come from a radiology or cardiology background. This document is built upon existing curricula and was created and vetted by an international panel of cardiologists and radiologists on behalf of the Society for Cardiovascular Magnetic Resonance (SCMR).

Radiologic Technologists' Perceived Reasons for Inappropriate Imaging

PURPOSE

To examine whether radiologic technologists' perceptions of imaging appropriateness differed based on their primary imaging modality, work shift, shift length, and primary practice type.

METHODS

A national, cross-sectional study was conducted in the fourth quarter of 2019 using a simple, randomized sample of American Society of Radiologic Technologists (ASRT) members. Study participants were employed in health care settings in radiography, computed tomography (CT), mammography, or radiology leadership. Seven potential reasons for inappropriate imaging procedures (ie, patient expectations, provide patient with a feeling of being taken seriously, lack of time, expectations from relatives, compensation for insufficient clinical examination, normal findings would reassure the patient, and fear of lawsuits) were evaluated for relationships with their primary imaging modality, work shift, shift length, and primary practice type.

RESULTS

Disparities in perceived reasons affecting imaging appropriateness were found. Providing the patient with a feeling of being taken seriously was related to primary practice type (P = .022). Lack of time was related to primary imaging modality (P = .005) and primary practice type (P = .006). Expectations from relatives was related to primary imaging modality (P = .016) and primary practice type (P = .027). Compensation for insufficient clinical examination was related to primary imaging modality (P < .001), shift length (P = .011), work shift (P = .002), and primary practice type (P < .001). Fear of lawsuits was related to primary imaging modality (P = .001)) and work shift (P = .002).

DISCUSSION

The study reveals that radiologic technologists' perceptions of patient-centered factors and defensive medicine-related factors differ among imaging modalities, shift types, and practice settings. However, more research is required to determine why radiologic technologists perceive these reasons to be present, investigate whether providers feel similarly, and determine perceptual alignment with evidence-based guidelines.

CONCLUSION

The findings suggest that attention should focus on the appropriateness of CT imaging procedures performed in hospitals during night shifts.

The reporting quality of natural language processing studies: systematic review of studies of radiology reports

BACKGROUND

Automated language analysis of radiology reports using natural language processing (NLP) can provide valuable information on patients' health and disease. With its rapid development, NLP studies should have transparent methodology to allow comparison of approaches and reproducibility. This systematic review aims to summarise the characteristics and reporting quality of studies applying NLP to radiology reports.

METHODS

We searched Google Scholar for studies published in English that applied NLP to radiology reports of any imaging modality between January 2015 and October 2019. At least two reviewers independently performed screening and completed data extraction. We specified 15 criteria relating to data source, datasets, ground truth, outcomes, and reproducibility for quality assessment. The primary NLP performance measures were precision, recall and F1 score.

RESULTS

Of the 4,836 records retrieved, we included 164 studies that used NLP on radiology reports. The commonest clinical applications of NLP were disease information or classification (28%) and diagnostic surveillance (27.4%). Most studies used English radiology reports (86%). Reports from mixed imaging modalities were used in 28% of the studies. Oncology (24%) was the most frequent disease area. Most studies had dataset size > 200 (85.4%) but the proportion of studies that described their annotated, training, validation, and test set were 67.1%, 63.4%, 45.7%, and 67.7% respectively. About half of the studies reported precision (48.8%) and recall (53.7%). Few studies reported external validation performed (10.8%), data availability (8.5%) and code availability (9.1%). There was no pattern of performance associated with the overall reporting quality.

CONCLUSIONS

There is a range of potential clinical applications for NLP of radiology reports in health services and research. However, we found suboptimal reporting quality that precludes comparison, reproducibility, and replication. Our results support the need for development of reporting standards specific to clinical NLP studies.

NCI Imaging Data Commons

The National Cancer Institute (NCI) Cancer Research Data Commons (CRDC) aims to establish a national cloud-based data science infrastructure. Imaging Data Commons (IDC) is a new component of CRDC supported by the Cancer Moonshot. The goal of IDC is to enable a broad spectrum of cancer researchers, with and without imaging expertise, to easily access and explore the value of deidentified imaging data and to support integrated analyses with nonimaging data. We achieve this goal by colocating versatile imaging collections with cloud-based computing resources and data exploration, visualization, and analysis tools. The IDC pilot was released in October 2020 and is being continuously populated with radiology and histopathology collections. IDC provides access to curated imaging collections, accompanied by documentation, a user forum, and a growing number of analysis use cases that aim to demonstrate the value of a data commons framework applied to cancer imaging research. SIGNIFICANCE: This study introduces NCI Imaging Data Commons, a new repository of the NCI Cancer Research Data Commons, which will support cancer imaging research on the cloud.

Appropriate use criteria for cardiovascular MRI: SIC - SIRM position paper Part 2 (myocarditis, pericardial disease, cardiomyopathies and valvular heart disease)

Cardiovascular magnetic resonance (CMR) has emerged as an accurate diagnostic technique for the evaluation of patients with cardiac disease in the majority of clinical settings, thanks to an established additional diagnostic and prognostic value. This document has been developed by a joined group of experts of the Italian Society of Cardiology (SIC) and Italian Society of Radiology (SIRM) to provide a summary about the current state of technology and clinical applications of CMR, to improve the clinical diagnostic pathways and to promote its inclusion in clinical practice. The writing committee consisted of members and experts of both societies in order to develop a more integrated approach in the field of cardiac imaging. This section 2 will cover myocarditis, pericardial disease, cardiomyopathies and valvular heart disease.

An International Survey of Quality and Safety Programs in Radiology

PURPOSE

The aim of this study was to determine the status of radiology quality improvement programs in a variety of selected nations worldwide.

METHODS

A survey was developed by select members of the International Economics Committee of the American College of Radiology on quality programs and was distributed to committee members. Members responded on behalf of their country. The 51-question survey asked about 12 different quality initiatives which were grouped into 4 themes: departments, users, equipment, and outcomes. Respondents reported whether a designated type of quality initiative was used in their country and answered subsequent questions further characterizing it.

RESULTS

The response rate was 100% and represented Australia, Canada, China, England, France, Germany, India, Israel, Japan, the Netherlands, Russia, and the United States. The most frequently reported quality initiatives were imaging appropriateness (91.7%) and disease registries (91.7%), followed by key performance indicators (83.3%) and morbidity and mortality rounds (83.3%). Peer review, equipment accreditation, radiation dose monitoring, and structured reporting were reported by 75.0% of respondents, followed by 58.3% of respondents for quality audits and critical incident reporting. The least frequently reported initiatives included Lean/Kaizen exercises and physician performance assessments, implemented by 25.0% of respondents.

CONCLUSION

There is considerable diversity in the quality programs used throughout the world, despite some influence by national and international organizations, from whom further guidance could increase uniformity and optimize patient care in radiology.

Thyroid Ultrasound Reports: Will the Thyroid Imaging, Reporting, and Data System Improve Natural Language Processing Capture of Critical Thyroid Nodule Features?

BACKGROUND

Critical thyroid nodule features are contained in unstructured ultrasound (US) reports. The Thyroid Imaging, Reporting, and Data System (TI-RADS) uses five key features to risk stratify nodules and recommend appropriate intervention. This study aims to analyze the quality of US reporting and the potential benefit of Natural Language Processing (NLP) systems in efficiently capturing TI-RADS features from text reports.

MATERIALS AND METHOD

This retrospective study used free-text thyroid US reports from an academic center (A) and community hospital (B). Physicians created "gold standard" annotations by manually extracting TI-RADS features and clinical recommendations from reports to determine how often they were included. Similar annotations were created using an automated NLP system and compared with the gold standard.

RESULTS

Two hundred eighty-two reports contained 409 nodules at least 1-cm in maximum diameter. The gold standard identified three nodules (0.7%) which contained enough information to calculate a complete TI-RADS score. Shape was described most often (92.7% of nodules), whereas margins were described least often (11%). A median number of two TI-RADS features are reported per nodule. The NLP system was significantly less accurate than the gold standard in capturing echogenicity (27.5%) and margins (58.9%). One hundred eight nodule reports (26.4%) included clinical management recommendations, which were included more often at site A than B (33.9 versus 17%, P < 0.05).

CONCLUSIONS

These results suggest a gap between current US reporting styles and those needed to implement TI-RADS and achieve NLP accuracy. Synoptic reporting should prompt more complete thyroid US reporting, improved recommendations for intervention, and better NLP performance.

ACR Appropriateness Criteria® Gestational Trophoblastic Disease

Gestational trophoblastic disease (GTD), a rare complication of pregnancy, includes both benign and malignant forms, the latter collectively referred to as gestational trophoblastic neoplasia (GTN). When metastatic, the lungs are the most common site of initial spread. Beta-human chorionic gonadotropin, elaborated to some extent by all forms of GTD, is useful in facilitating disease detection, diagnosis, monitoring treatment response, and follow-up. Imaging evaluation depends on whether GTD manifests in one of its benign forms or whether it has progressed to GTN. Transabdominal and transvaginal ultrasound with duplex Doppler evaluation of the pelvis are usually appropriate diagnostic procedures in either of these circumstances, and in posttreatment surveillance. The appropriateness of more extensive imaging remains dependent on a diagnosis of GTN and on other factors. The use of imaging to assess complications, typically hemorrhagic, should be guided by the location of clinical signs and symptoms. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

Adding Value in Radiology Reporting

The major goal of the radiology report is to deliver timely, accurate, and actionable information to the patient care team and the patient. Structured reporting offers multiple advantages over traditional free-text reporting, including reduction in diagnostic error, comprehensiveness, adherence to national consensus guidelines, revenue capture, data collection, and research. Various technological innovations enhance integration of structured reporting into everyday clinical practice. This review discusses the benefits of innovations in radiology reporting to the clinical decision process, the patient experience, the cost of imaging, and the overall contributions to the health of the population. Future directions, including the use of artificial intelligence, are reviewed.

Radiological approach to COVID-19 pneumonia with an emphasis on chest CT

Coronavirus disease 2019 (COVID-19) has recently become a worldwide outbreak with several millions of people infected and more than 160.000 deaths. A fast and accurate diagnosis in this outbreak is critical to isolate and treat patients. Radiology plays an important role in the diagnosis and management of the patients. Among various imaging modalities, chest CT has received attention with its higher sensitivity and specificity rates. Shortcomings of the real-time reverse transcriptase-polymerase chain reaction test, including inappropriate sample collection and analysis methods, initial false negative results, and limited availability has led to widespread use of chest CT in the diagnostic algorithm. This review summarizes the role of radiology in COVID-19 pneumonia, diagnostic accuracy of imaging, and chest CT findings of the disease.

Computer-aided diagnosis system of thyroid nodules ultrasonography: Diagnostic performance difference between computer-aided diagnosis and 111 radiologists

To evaluate the diagnostic efficiency of computer-aided diagnosis (CAD) system and 111 radiologists with different experience in identifying benign and malignant thyroid nodules, and to summarize the ultrasound features that may affect the diagnostic of CAD and radiologists.Fifty thyroid nodules and 111 radiologists were enrolled in this study. All the 50 nodules were diagnosed by the 111 radiologists and the CAD system simultaneously. The diagnostic performance of the CAD system, senior and junior radiologists with the maximum accuracy were calculated and compared. Interobserver agreement for different ultrasound characteristics between the CAD and senior radiologist were analyzed.CAD system showed a higher specificity than junior radiologist (87.5% vs 70.4%, P = .03), and a lower sensitivity than the senior radiologist and junior radiologist but the statistics were not significant (76.9% vs 86.9%, P > .5; 76.9% vs 82.6%, P > .5). The CAD system and senior radiologist got larger AUC than junior radiologist but the differences were not statistically significant (0.82 vs 0.76, respectively; P = .5). The interobserver agreement for the US characteristics between the CAD system and senior radiologist were: substantial agreement for hypoechoic and taller than wide (kappa value = 0.66, 0.78), and moderate agreement for irregular margin and micro-calcifications (kappa value = 0.52, 0.42).The CAD system achieved equal diagnostic accuracy to the senior radiologists and higher accuracy than the junior radiologists. The interobserver agreements in the US features between the CAD system and senior radiologist were substantial agreement for hypoechoic and taller than wide; moderate agreement for irregular margin and micro-calcifications. The location of a thyroid nodule and the feature of macrocalcification with wide acoustic shadow may influence the analysis of the CAD system.

Immune-related (IR)-pneumonitis during the COVID-19 pandemic: multidisciplinary recommendations for diagnosis and management

Immune-related (IR)-pneumonitis is a rare and potentially fatal toxicity of anti-PD(L)1 immunotherapy. Expert guidelines for the diagnosis and management of IR-pneumonitis include multidisciplinary input from medical oncology, pulmonary medicine, infectious disease, and radiology specialists. Severe acute respiratory syndrome coronavirus 2 is a recently recognized respiratory virus that is responsible for causing the COVID-19 global pandemic. Symptoms and imaging findings from IR-pneumonitis and COVID-19 pneumonia can be similar, and early COVID-19 viral testing may yield false negative results, complicating the diagnosis and management of both entities. Herein, we present a set of multidisciplinary consensus recommendations for the diagnosis and management of IR-pneumonitis in the setting of COVID-19 including: (1) isolation procedures, (2) recommended imaging and interpretation, (3) adaptations to invasive testing, (4) adaptations to the management of IR-pneumonitis, (5) immunosuppression for steroid-refractory IR-pneumonitis, and (6) management of suspected concurrent IR-pneumonitis and COVID-19 infection. There is an emerging need for the adaptation of expert guidelines for IR-pneumonitis in the setting of the global COVID-19 pandemic. We propose a multidisciplinary consensus on this topic, in this position paper.

Optimisation in daily practice - it's more than just radiation dose

On this editorial, the importance of optimisation of diagnostic imaging and radiation therapy in terms of diagnostic and therapeutic tests and treatments, education and service provision are discussed.

Multiparametric radiomics methods for breast cancer tissue characterization using radiological imaging

BACKGROUND AND PURPOSE

Multiparametric radiological imaging is vital for detection, characterization, and diagnosis of many different diseases. Radiomics provide quantitative metrics from radiological imaging that may infer potential biological meaning of the underlying tissue. However, current methods are limited to regions of interest extracted from a single imaging parameter or modality, which limits the amount of information available within the data. This limitation can directly affect the integration and applicable scope of radiomics into different clinical settings, since single image radiomics are not capable of capturing the true underlying tissue characteristics in the multiparametric radiological imaging space. To that end, we developed a multiparametric imaging radiomic (mpRad) framework for extraction of first and second order radiomic features from multiparametric radiological datasets.

METHODS

We developed five different radiomic techniques that extract different aspects of the inter-voxel and inter-parametric relationships within the high-dimensional multiparametric magnetic resonance imaging breast datasets. Our patient cohort consisted of 138 breast patients, where, 97 patients had malignant lesions and 41 patients had benign lesions. Sensitivity, specificity, receiver operating characteristic (ROC) and areas under the curve (AUC) analysis were performed to assess diagnostic performance of the mpRad parameters. Statistical significance was set at p < 0.05.

RESULTS

The mpRad features successfully classified malignant from benign breast lesions with excellent sensitivity and specificity of 82.5% and 80.5%, respectively, with Area Under the receiver operating characteristic Curve (AUC) of 0.87 (0.81-0.93). mpRad provided a 9-28% increase in AUC metrics over single radiomic parameters.

CONCLUSIONS

We have introduced the mpRad framework that extends radiomic analysis from single images to multiparametric datasets for better characterization of the underlying tissue biology.

PET/CT radiomics in breast cancer: Mind the step

The aim of the present review was to assess the current status of positron emission tomography/computed tomography (PET/CT) radiomics research in breast cancer, and in particular to analyze the strengths and weaknesses of the published papers in order to identify challenges and suggest possible solutions and future research directions. Various combinations of the terms "breast", "radiomic", "PET", "radiomics", "texture", and "textural" were used for the literature search, extended until 8 July 2019, within the PubMed/MEDLINE database. Twenty-six articles fulfilling the inclusion/exclusion criteria were retrieved in full text and analyzed. The studies had technical and clinical objectives, including diagnosis, biological characterization (correlation with histology, molecular subtypes and IHC marker expression), prediction of response to neoadjuvant chemotherapy, staging, and outcome prediction. We reviewed and discussed the selected investigations following the radiomics workflow steps related to the clinical, technical, analysis, and reporting issues. Most of the current evidence on the clinical role of PET/CT radiomics in breast cancer is at the feasibility level. Harmonized methods in image acquisition, post-processing and features calculation, predictive models and classifiers trained and validated on sufficiently representative datasets, adherence to consensus guidelines, and transparent reporting will give validity and generalizability to the results.

PRELIMINARY STAFF DOSE ASSESSMENT FOR COMMON FLUOROSCOPY GUIDED PROCEDURES AT KORLE-BU TEACHING HOSPITAL, ACCRA, GHANA

Preliminary studies on effective and eye lens doses of six Radiologists, four Cardiologists have been conducted for a period of 3 months. Electronic dosemeters positioned under and over lead apron of staff were used for the dosimetry. The estimated effective dose per month to Cardiologist and Radiologist were 0.01-0.07 mSv and 0.03-0.14 mSv, respectively. The estimated eye lens doses per month to Cardiologists and Radiologists were also 0.15-0.30 mSv and 0.53-3.39 mSv, respectively. The effective doses per month to staff were below the ICRP acceptable limit of 1.67 mSv/month but the upper limit of the range of estimated eye lens dose exceeded the ICRP acceptable limit by a factor of 2. Regular use of protective goggles and consistent eye lens dose monitoring is encouraged at the hospital for dose optimization.