Big data, artificial intelligence, and structured reporting

Structured Reporting in Sleep Medicine

Background/Objectives: Somnological findings are often written as free texts, supported by questionnaires. The quality and structure of free-text reports (FTRs) vary between examiners and specialties, depending on the individual level of expertise and experience in sleep medicine. This study aimed to compare the quality of free-text reports (FTRs) and structured reports (SRs) from somnological consultations in otolaryngology for patients assessed for obstructive sleep apnea (OSA). Methods: This study compared free-text reports (FTRs) and structured reports (SRs) from 50 patients with suspected OSA, including medical history, clinical examination findings, and medical letters, all prepared by six examiners with similar experience levels. A web-based approach was used to develop a standardized template for structured somnological reporting. The completeness and time required for both FTRs and SRs were evaluated, and a questionnaire was administered to assess user satisfaction with each reporting method. Results: The completeness scores of SRs were significantly higher than those of FTRs (88% vs. 54.2%, p < 0.001). The mean time to complete an SR was significantly shorter than that for FTRs (10.2 vs. 16.8 min, p < 0.001). SRs had significantly higher user satisfaction compared to FTRs (VAS 8.3 vs. 2.2, p < 0.001). Conclusions: Compared to FTRs, SRs for OSA patients are more comprehensive and faster. The use of SR is more satisfactory for examiners and supports the learning effect.

What should be documented for an anterior shoulder dislocation? A Delphi study

BACKGROUND

Standardised consensus-based documentation following anterior shoulder dislocation in the ED might improve clinical quality, reduce heterogeneity in research and reduce workload. The aim of this study was to determine important elements and the extent of variability for the ED documentation following anterior shoulder dislocation.

METHODS

An expert panel of physicians who perform the documentation (emergency physicians) of diagnosis and management of anterior shoulder dislocation and those who may read it (orthopaedic surgeons and general practitioners) was recruited in a three-round Delphi design between May and November 2022. Important elements were identified for history, physical examination, additional examinations, reduction technique and miscellaneous. These were rated on a 0-9 Likert scale. Consensus was reached when ≥80% scored 7-9. Another, independent, outcome was high variability in opinion, defined as at least one score between 1 and 3 and one score between 7 and 9 on an item after the third round.

RESULTS

The expert panel consisted of 22 emergency physicians, 5 general practitioners and 3 orthopaedic surgeons. In the first round, 85 elements were identified, and consensus on importance was reached in 22 out of the 85 elements: medical history (5 out of 30), physical examination (5 out of 18), additional examinations (5 out of 9), reduction (5 out of 17) and miscellaneous (2 out of 11). High variability in importance was seen in 79 (93%) out of the 85 elements after the third round.

CONCLUSION

A consensus on 22 out of the 85 elements was reached and could be included in the ED documentation on anterior shoulder dislocation. Regardless of this consensus, high variability was observed in almost all the elements, highlighting the difference in opinions. Nevertheless, these elements could facilitate more concise communication among healthcare professionals and could facilitate homogenous datasets.

Explanation and elaboration of MedinAI: guidelines for reporting artificial intelligence studies in medicines, pharmacotherapy, and pharmaceutical services

The increasing adoption of artificial intelligence (AI) in medicines, pharmacotherapy, and pharmaceutical services necessitates clear guidance on reporting standards. While the MedinAI Statement (Bottacin in Int J Clin Pharm, https://doi.org/10.1007/s11096-025-01905-3, 2025) provides core guidelines for reporting AI studies in these fields, detailed explanations and practical examples are crucial for optimal implementation. This companion document was developed to offer comprehensive guidance and real-world examples for each guideline item. The document elaborates on all 14 items and 78 sub-items across four domains: core, ethical considerations in medication and pharmacotherapy, medicines as products, and services related to medicines and pharmacotherapy. Through clear, actionable guidance and diverse examples, this document enhances MedinAI's utility, enabling researchers and stakeholders to improve the quality and transparency of AI research reporting across various contexts, study designs, and development stages.

[Structured reporting in otorhinolaryngology]

Structured reporting (SR) is a valuable method for optimizing diagnosis and treatment in various specialist disciplines. While conventional free-text findings are often inconsistent and difficult to compare, structured documentation enables higher quality and completeness of findings. This helps to better manage the increasing complexity and raise therapeutic standards. Studies show that SR leads to a significant improvement in the quality of findings in various areas of otorhinolaryngology. SR also increases time efficiency and inter-rater reliability and contributes to the learning effect. Furthermore, SR increases user and referring physician satisfaction, especially in interdisciplinary application. Future multicenter studies are needed to provide further insights into the practical application and scientific evaluability of SR, also in combination with artificial intelligence.

Enhancing Radiological Reporting in Head and Neck Cancer: Converting Free-Text CT Scan Reports to Structured Reports Using Large Language Models

Objective  The aim of this study was to assess efficacy of large language models (LLMs) for converting free-text computed tomography (CT) scan reports of head and neck cancer (HNCa) patients into a structured format using a predefined template. Materials and Methods  A retrospective study was conducted using 150 CT reports of HNCa patients. A comprehensive structured reporting template for HNCa CT scans was developed, and the Generative Pre-trained Transformer 4 (GPT-4) was initially used to convert 50 CT reports into a structured format using this template. The generated structured reports were then evaluated by a radiologist for instances of missing or misinterpreted information and any erroneous additional details added by GPT-4. Following this assessment, the template was refined for improved accuracy. This revised template was then used for conversion of 100 other HNCa CT reports into structured format using GPT-4. These reports were then reevaluated in the same manner. Results  Initially, GPT-4 successfully converted all 50 free-text reports into structured reports. However, there were 10 places with missing information: tracheostomy tube ( n  = 3), noninclusion of involvement of sternocleidomastoid muscle ( n  = 2), extranodal tumor extension ( n  = 3), and contiguous involvement of the neck structures by nodal mass rather than the primary ( n  = 2). Few instances of nonsuspicious lung nodules were misinterpreted as metastases ( n  = 2). GPT-4 did not indicate any erroneous additional findings. Using the revised reporting template, GPT-4 converted all the 100 CT reports into a structured format with no repeated or additional mistakes. Conclusion  LLMs can be used for structuring free-text radiology reports using plain language prompts and a simple yet comprehensive reporting template. Key Points Structured radiology reports in oncological patients, although advantageous, are not used widely in practice due to perceived drawbacks like interference with routine radiology workflow and scan interpretation.We found that GPT-4 is highly efficient in converting conventional CT reports of HNCa patients to structured reports using a predefined template.This application of LLMs in radiology can help in enhancing the acceptability and clinical utility of structured radiology reports in oncological imaging. Summary Statement Large language models can successfully and accurately convert conventional radiology reports for oncology scans into a structured format using a comprehensive predefined template and thus can enhance the utility and integration of these reports in routine clinical practice.

Impact of AI on radiology: a EuroAIM/EuSoMII 2024 survey among members of the European Society of Radiology

In order to assess the perceptions and expectations of the radiology staff about artificial intelligence (AI), we conducted an online survey among ESR members (January-March 2024). It was designed considering that conducted in 2018, updated according to recent advancements and emerging topics, consisting of seven questions regarding demographics and professional background and 28 AI questions. Of 28,000 members contacted, 572 (2%) completed the survey. AI impact was predominantly expected on breast and oncologic imaging, primarily involving CT, mammography, and MRI, and in the detection of abnormalities in asymptomatic subjects. About half of responders did not foresee an impact of AI on job opportunities. For 273/572 respondents (48%), AI-only reports would not be accepted by patients; and 242/572 respondents (42%) think that the use of AI systems will not change the relationship between the radiological team and the patient. According to 255/572 respondents (45%), radiologists will take responsibility for any AI output that may influence clinical decision-making. Of 572 respondents, 274 (48%) are currently using AI, 153 (27%) are not, and 145 (25%) are planning to do so. In conclusion, ESR members declare familiarity with AI technologies, as well as recognition of their potential benefits and challenges. Compared to the 2018 survey, the perception of AI's impact on job opportunities is in general slightly less optimistic (more positive from AI users/researchers), while the radiologist's responsibility for AI outputs is confirmed. The use of large language models is declared not only limited to research, highlighting the need for education in AI and its regulations. CRITICAL RELEVANCE STATEMENT: This study critically evaluates the current impact of AI on radiology, revealing significant usage patterns and clinical implications, thereby guiding future integration strategies to enhance efficiency and patient care in clinical radiology. KEY POINTS: The survey examines ESR member's views about the impact of AI on radiology practice. AI use is relevant in CT and MRI, with varying impacts on job roles. AI tools enhance clinical efficiency but require radiologist oversight for patient acceptance.

[Increased report completeness and satisfaction with structured neurotological reporting in the interdisciplinary assessment of vertigo]

BACKGROUND

Results of neurotological function diagnostics in the context of interdisciplinary vertigo assessment are usually formulated as free-text reports (FTR). These are often subject to high variability, which may lead to loss of information. The aim of the present study was to evaluate the completeness of structured reports (SR) and referrer satisfaction in the neurotological assessment of vertigo.

MATERIALS AND METHODS

Neurotological function diagnostics performed as referrals (n = 88) were evaluated retrospectively. On the basis of the available raw data, SRs corresponding to FTRs from clinical routine were created by means of a specific SR template for neurotological function diagnostics. FTRs and SRs were evaluated for completeness and referring physician satisfaction (n = 8) using a visual analog scale (VAS) questionnaire.

RESULTS

Compared to FTRs, SRs showed significantly increased overall completeness (73.7% vs. 51.7%, p < 0.001), especially in terms of patient history (92.5% vs. 66.7%, p < 0.001), description of previous findings (87.5% vs. 38%, p < 0.001), and neurotological (33.5% vs. 26.7%, p < 0.001) and audiometric function diagnostics (58% vs. 32.3%, p < 0.001). In addition, SR showed significantly increased referring physician satisfaction (VAS 8.8 vs. 4.9, p < 0.001).

CONCLUSION

Neurotological SRs enable a significantly increased report completeness with higher referrer satisfaction in the context of interdisciplinary assessment of vertigo. Furthermore, SRs are particularly suitable for scientific data analysis, especially in the context of big data analyses.

Retrospective Radiology Research: Do We Need Informed Patient Consent?

While knowledge of the population's view on the need for informed consent for retrospective radiology research may provide valuable insight into how an optimal balance can be achieved between patient rights versus an expedited advancement of radiology science, this is a topic that has been ignored in the literature so far. To investigate the view of the general population, survey data were collected from 2407 people representative of the Dutch population. The results indicate that for non-commercial institutions, especially hospitals (97.4 per cent), respondents agree with the retrospective use of imaging data, although they generally indicate that their explicit consent is required. However, most respondents (63.5 per cent) would never allow commercial firms to retrospectively use their imaging data. When including only respondents who completed the minimally required reading time of 12.3 s to understand the description about retrospective radiology research given in the survey (n = 770), almost all (98.9 per cent) mentioned to have no objections for their imaging data to be used by hospitals for retrospective research, with 57.9 per cent indicating their consent to be required and 41.0 per cent indicating that explicit patient consent to be unnecessary. We conclude that the general population permits retrospective radiology research by hospitals, and a substantial proportion indicates explicit patient consent to be unnecessary when understanding what retrospective radiology research entails. However, the general population's support for the unrestricted retrospective use of imaging data for research purposes without patient consent decreases for universities not linked to hospitals, other non-commercial institutions, government agencies, and particularly commercial firms.

Substantial variability in what is considered important in the radiological report for anterior shoulder instability: a Delphi study with Dutch musculoskeletal radiologists and orthopedic surgeons

Background

Standardized consensus-based radiological reports for shoulder instability may improve clinical quality, reduce heterogeneity, and reduce workload. Therefore, the aim of this study was to determine important elements for the x-ray, magnetic resonance imaging (MRI) arthrography (MRA), and computed tomography (CT) report, the extent of variability, and important MRI views and settings.

Methods

An expert panel of musculoskeletal radiologists and orthopedic surgeons was recruited in a three-round Delphi design. Important elements were identified for the x-ray, MRA, and CT report and important MRI views and setting. These were rated on a 0-9 Likert scale. High variability was defined as at least one score between 1-3 and 7-9. Consensus was reached when ≥80% scored an element 1-3 or 7-9.

Results

The expert panel consisted of 21 musculoskeletal radiologists and 15 orthopedic surgeons. The number of elements identified in the first round was seventeen for the x-ray report, 52 for MRA, 21 for CT, and 23 for the MRI protocol. The number of elements that reached consensus was five for x-ray, twenty for MRA, nine for CT, and two for the MRI protocol. High variability was observed in 76.5% (n = 13) x-ray elements, 85.0% (n = 45) MRA, 76.2% (n = 16) CT, and 85.7% (n = 18) MRI protocol.

Conclusion

Substantial variability was observed in the scoring of important elements in the radiological for the evaluation of anterior shoulder instability, regardless of modality. Consensus was reached for five elements in the x-ray report, twenty in the MRA report, and nine in the CT report. Finally, consensus was reached on two elements regarding MRA views and settings.

Navigating the ethical landscape of artificial intelligence in radiography: a cross-sectional study of radiographers' perspectives

BACKGROUND

The integration of artificial intelligence (AI) in radiography presents transformative opportunities for diagnostic imaging and introduces complex ethical considerations. The aim of this cross-sectional study was to explore radiographers' perspectives on the ethical implications of AI in their field and identify key concerns and potential strategies for addressing them.

METHODS

A structured questionnaire was distributed to a diverse group of radiographers in Saudi Arabia. The questionnaire included items on ethical concerns related to AI, the perceived impact on clinical practice, and suggestions for ethical AI integration in radiography. The data were analyzed using quantitative and qualitative methods to capture a broad range of perspectives.

RESULTS

Three hundred eighty-eight radiographers responded and had varying levels of experience and specializations. Most (44.8%) participants were unfamiliar with the integration of AI into radiography. Approximately 32.9% of radiographers expressed uncertainty regarding the importance of transparency and explanatory capabilities in the AI systems used in radiology. Many (36.9%) participants indicated that they believed that AI systems used in radiology should be transparent and provide justifications for their decision-making procedures. A significant preponderance (44%) of respondents agreed that implementing AI in radiology may increase ethical dilemmas. However, 27.8%expressed uncertainty in recognizing and understanding the potential ethical issues that could arise from integrating AI in radiology. Of the respondents, 41.5% stated that the use of AI in radiology required establishing specific ethical guidelines. However, a significant percentage (28.9%) expressed the opposite opinion, arguing that utilizing AI in radiology does not require adherence to ethical standards. In contrast to the 46.6% of respondents voicing concerns about patient privacy over AI implementation, 41.5% of respondents did not have any such apprehensions.

CONCLUSIONS

This study revealed a complex ethical landscape in the integration of AI in radiography, characterized by enthusiasm and apprehension among professionals. It underscores the necessity for ethical frameworks, education, and policy development to guide the implementation of AI in radiography. These findings contribute to the ongoing discourse on AI in medical imaging and provide insights that can inform policymakers, educators, and practitioners in navigating the ethical challenges of AI adoption in healthcare.

Detection of femoropopliteal arterial steno-occlusion at MR angiography: initial experience with artificial intelligence

BACKGROUND

This study evaluated a deep learning (DL) algorithm for detecting vessel steno-occlusions in patients with peripheral arterial disease (PAD). It utilised a private dataset, which was acquired and annotated by the authors through their institution and subsequently validated by two blinded readers.

METHODS

A single-centre retrospective study analysed 105 magnetic resonance angiography (MRA) images using an EfficientNet B0 DL model. Initially, inter-reader variability was assessed using the complete dataset. For a subset of these images (29 from the left side and 35 from the right side) where digital subtraction angiography (DSA) data was available as the ground truth, the model's accuracy and the area under the curve at receiver operating characteristics analysis (ROC-AUC) were evaluated.

RESULTS

A total of 105 patient examinations (mean age, 75 years ±12 [mean ± standard deviation], 61 men) were evaluated. Radiologist-DL model agreement had a quadratic weighted Cohen κ ≥ 0.72 (left side) and ≥ 0.66 (right side). Radiologist inter-reader agreement was ≥ 0.90 (left side) and ≥ 0.87 (right side). The DL model achieved a 0.897 accuracy and a 0.913 ROC-AUC (left side) and 0.743 and 0.830 (right side). Radiologists achieved 0.931 and 0.862 accuracies, with 0.930 and 0.861 ROC-AUCs (left side), and 0.800 and 0.799 accuracies, with 0.771 ROC-AUCs (right side).

CONCLUSION

The DL model provided valid results in identifying arterial steno-occlusion in the superficial femoral and popliteal arteries on MRA among PAD patients. However, it did not reach the inter-reader agreement of two radiologists.

RELEVANCE STATEMENT

The tested DL model is a promising tool for assisting in the detection of arterial steno-occlusion in patients with PAD, but further optimisation is necessary to provide radiologists with useful support in their daily routine diagnostics.

KEY POINTS

• This study focused on the application of DL for arterial steno-occlusion detection in lower extremities on MRA. • A previously developed DL model was tested for accuracy and inter-reader agreement. • While the model showed promising results, it does not yet replace human expertise in detecting arterial steno-occlusion on MRA.

Constructing a clinical radiographic knee osteoarthritis database using artificial intelligence tools with limited human labor: A proof of principle

OBJECTIVE

To create a scalable and feasible retrospective consecutive knee osteoarthritis (OA) radiographic database with limited human labor using commercial and custom-built artificial intelligence (AI) tools.

METHODS

We applied four AI tools, two commercially available and two custom-built tools, to analyze 6 years of clinical consecutive knee radiographs from patients aged 35-79 at the University of Copenhagen Hospital, Bispebjerg-Frederiksberg Hospital, Denmark. The tools provided Kellgren-Lawrence (KL) grades, joint space widths, patella osteophyte detection, radiographic view detection, knee joint implant detection, and radiographic marker detection.

RESULTS

In total, 25,778 knee radiographs from 8575 patients were included in the database after excluding inapplicable radiographs, and 92.5% of the knees had a complete OA dataset. Using the four AI tools, we saved about 800 hours of radiologist reading time and only manually reviewed 16.0% of the images in the database.

CONCLUSIONS

This study shows that clinical knee OA databases can be built using AI with limited human reading time for uniform grading and measurements. The concept is scalable temporally and across geographic regions and could help diversify further OA research by efficiently including radiographic knee OA data from different populations globally. We can prevent data dredging and overfitting OA theories on existing trite cohorts by including various gene pools and continuous expansion of new clinical cohorts. Furthermore, the suggested tools and applied approaches provide an ability to retest previous hypotheses and test new hypotheses on real-life clinical data with current disease prevalence and trends.

ESR paper on structured reporting in radiology-update 2023

Structured reporting in radiology continues to hold substantial potential to improve the quality of service provided to patients and referring physicians. Despite many physicians' preference for structured reports and various efforts by radiological societies and some vendors, structured reporting has still not been widely adopted in clinical routine.While in many countries national radiological societies have launched initiatives to further promote structured reporting, cross-institutional applications of report templates and incentives for usage of structured reporting are lacking. Various legislative measures have been taken in the USA and the European Union to promote interoperable data formats such as Fast Healthcare Interoperability Resources (FHIR) in the context of the EU Health Data Space (EHDS) which will certainly be relevant for the future of structured reporting. Lastly, recent advances in artificial intelligence and large language models may provide innovative and efficient approaches to integrate structured reporting more seamlessly into the radiologists' workflow.The ESR will remain committed to advancing structured reporting as a key component towards more value-based radiology. Practical solutions for structured reporting need to be provided by vendors. Policy makers should incentivize the usage of structured radiological reporting, especially in cross-institutional setting.Critical relevance statement Over the past years, the benefits of structured reporting in radiology have been widely discussed and agreed upon; however, implementation in clinical routine is lacking due-policy makers should incentivize the usage of structured radiological reporting, especially in cross-institutional setting.Key points1. Various national societies have established initiatives for structured reporting in radiology.2. Almost no monetary or structural incentives exist that favor structured reporting.3. A consensus on technical standards for structured reporting is still missing.4. The application of large language models may help structuring radiological reports.5. Policy makers should incentivize the usage of structured radiological reporting.

Structured reporting in radiology enables epidemiological analysis through data mining: urolithiasis as a use case

PURPOSE

To investigate the epidemiology and distribution of disease characteristics of urolithiasis by data mining structured radiology reports.

METHODS

The content of structured radiology reports of 2028 urolithiasis CTs was extracted from the department's structured reporting (SR) platform. The investigated cohort represented the full spectrum of a tertiary care center, including mostly symptomatic outpatients as well as inpatients. The prevalences of urolithiasis in general and of nephro- and ureterolithasis were calculated. The distributions of age, sex, calculus size, density and location, and the number of ureteral and renal calculi were calculated. For ureterolithiasis, the impact of calculus characteristics on the degree of possible obstructive uropathy was calculated.

RESULTS

The prevalence of urolithiasis in the investigated cohort was 72%. Of those patients, 25% had nephrolithiasis, 40% ureterolithiasis, and 35% combined nephro- and ureterolithiasis. The sex distribution was 2.3:1 (M:F). The median patient age was 50 years (IQR 36-62). The median number of calculi per patient was 1. The median size of calculi was 4 mm, and the median density was 734 HU. Of the patients who suffered from ureterolithiasis, 81% showed obstructive uropathy, with 2nd-degree uropathy being the most common. Calculus characteristics showed no impact on the degree of obstructive uropathy.

CONCLUSION

SR-based data mining is a simple method by which to obtain epidemiologic data and distributions of disease characteristics, for the investigated cohort of urolithiasis patients. The added information can be useful for multiple purposes, such as clinical quality assurance, radiation protection, and scientific or economic investigations. To benefit from these, the consistent use of SR is mandatory. However, in clinical routine SR usage can be elaborate and requires radiologists to adapt.

[Report template from the German Society of Urology and the German Radiological Society for standardized, structured reporting of native computed tomography scans in the diagnosis of urinary stones]

Standardized structured radiological reporting (SSRB) has been promoted in recent years. The aims of SSRB include that reports be complete, clear, understandable, and stringent. Repetitions or superfluous content should be avoided. In addition, there are advantages in the presentation of chronological sequences, tracking and correlations with structured findings from other disciplines and also the use of artificial intelligence (AI)-based methods. The development of the presented template for SSRB of native computed tomography for urinary stones followed the "process for the creation of quality-assured and consensus-based report templates as well as subsequent continuous quality control and updating" proposed by the German Radiological Society (DRG). This includes several stages of drafts, consensus meetings and further developments. The final version was published on the DRG website ( www.befundung.drg.de ). The template will be checked annually by the steering group and adjusted as necessary. The template contains 6 organ domains (e.g., right kidney) for which entries can be made for a total of 21 different items, mostly with selection windows. If "no evidence of stones" is selected for an organ in the first query, the query automatically jumps to the next organ, so that the processing can be processed very quickly despite the potentially high total number of individual queries for all organs. The German, European, and North American Radiological Societies perceive the establishment of a standardized structured diagnosis of tomographic imaging methods not only in oncological radiology as one of the current central tasks. With the present template for the description of computed tomographic findings for urinary stone diagnostics, we are presenting the first version of a urological template. Further templates for urological diseases are to follow.

Natural language processing for automatic evaluation of free-text answers - a feasibility study based on the European Diploma in Radiology examination

BACKGROUND

Written medical examinations consist of multiple-choice questions and/or free-text answers. The latter require manual evaluation and rating, which is time-consuming and potentially error-prone. We tested whether natural language processing (NLP) can be used to automatically analyze free-text answers to support the review process.

METHODS

The European Board of Radiology of the European Society of Radiology provided representative datasets comprising sample questions, answer keys, participant answers, and reviewer markings from European Diploma in Radiology examinations. Three free-text questions with the highest number of corresponding answers were selected: Questions 1 and 2 were "unstructured" and required a typical free-text answer whereas question 3 was "structured" and offered a selection of predefined wordings/phrases for participants to use in their free-text answer. The NLP engine was designed using word lists, rule-based synonyms, and decision tree learning based on the answer keys and its performance tested against the gold standard of reviewer markings.

RESULTS

After implementing the NLP approach in Python, F1 scores were calculated as a measure of NLP performance: 0.26 (unstructured question 1, n = 96), 0.33 (unstructured question 2, n = 327), and 0.5 (more structured question, n = 111). The respective precision/recall values were 0.26/0.27, 0.4/0.32, and 0.62/0.55.

CONCLUSION

This study showed the successful design of an NLP-based approach for automatic evaluation of free-text answers in the EDiR examination. Thus, as a future field of application, NLP could work as a decision-support system for reviewers and support the design of examinations being adjusted to the requirements of an automated, NLP-based review process.

CLINICAL RELEVANCE STATEMENT

Natural language processing can be successfully used to automatically evaluate free-text answers, performing better with more structured question-answer formats. Furthermore, this study provides a baseline for further work applying, e.g., more elaborated NLP approaches/large language models.

KEY POINTS

• Free-text answers require manual evaluation, which is time-consuming and potentially error-prone. • We developed a simple NLP-based approach - requiring only minimal effort/modeling - to automatically analyze and mark free-text answers. • Our NLP engine has the potential to support the manual evaluation process. • NLP performance is better on a more structured question-answer format.

Application of Deep Learning in Generating Structured Radiology Reports: A Transformer-Based Technique

Since radiology reports needed for clinical practice and research are written and stored in free-text narrations, extraction of relative information for further analysis is difficult. In these circumstances, natural language processing (NLP) techniques can facilitate automatic information extraction and transformation of free-text formats to structured data. In recent years, deep learning (DL)-based models have been adapted for NLP experiments with promising results. Despite the significant potential of DL models based on artificial neural networks (ANN) and convolutional neural networks (CNN), the models face some limitations to implement in clinical practice. Transformers, another new DL architecture, have been increasingly applied to improve the process. Therefore, in this study, we propose a transformer-based fine-grained named entity recognition (NER) architecture for clinical information extraction. We collected 88 abdominopelvic sonography reports in free-text formats and annotated them based on our developed information schema. The text-to-text transfer transformer model (T5) and Scifive, a pre-trained domain-specific adaptation of the T5 model, were applied for fine-tuning to extract entities and relations and transform the input into a structured format. Our transformer-based model in this study outperformed previously applied approaches such as ANN and CNN models based on ROUGE-1, ROUGE-2, ROUGE-L, and BLEU scores of 0.816, 0.668, 0.528, and 0.743, respectively, while providing an interpretable structured report.

[Image interpretation and the radiological report]

BACKGROUND

The radiological report is the cornerstone of communication between radiologists and referring physicians and patients, respectively. The report is comprised of image interpretation on the one hand and communication of this interpretation on the other hand.

OBJECTIVES AND METHODS

To outline different types of radiological reports (regarding content as well as structure) and their communication. To this end, current guidelines are summarized and clinical examples are presented.

RESULTS

The radiological report is typically a written piece of free text prose and highly individualized regarding its quality, precision, and structure. In order to improve the understanding of the written report, additional material (e.g., annotations, images, tables) can be supplemented (multimedia-enhanced reporting). In terms of standardization, national and international radiological associations promote structured reporting in radiology. However, this is not without issues.

CONCLUSION

Effective communication should improve patient care and it should be clear and provided in a timely manner. As communication in clinical reality is often hampered by various factors, internal standard operating procedures (SOPs) should be developed to improve communication workflows. to improve communication procedures.

A study of non small cell lung cancer (NSCLC) patients with brain metastasis: A single centre experience

BACKGROUND

Lung cancer is the leading cause of cancer death with the majority of cases being non-small cell lung cancer (NSCLC) [1]. A common complication of NSCLC is brain metastasis (BM) [2, 3], where the prognosis remains poor despite new treatments. Real world data complements data gained from clinical trials, providing information on patients excluded from prospective research [4]. However, information from patient notes may prove incomplete and difficult to extract. We developed an algorithm to identify patients in our clinical database with brain metastasis from the electronic health record (EHR).

METHODS

We retrospectively extracted data from the EHR of patients managed at a large teaching hospital between 2007 and 2018. Using the ICD-10 code C34, for lung cancer, our algorithm used phrases associated with BMs to search the unstructured text of radiology reports. Summary statistics and univariant analysis was performed for overall survival.

RESULTS

818 patients were identified as potentially having BM and 453 patients were confirmed on clinical review of their records. The median age of patients was 69 years, 50% were female and 66% had a performance status of >2. 12.2% had an identifiable mutation and 11.5% were identified as PD-L1 positive. In the first line setting, 65% of patients received symptomatic treatment, 23% received systemic anticancer therapy (SACT), 6.1% surgery and 10% radiotherapy, of which 6.5% had external beam and 3.5% stereotactic radiosurgery. Regarding those treated with SACT, 35% had an intracranial response to treatment (3% had complete response, 32% had a partial response). Median survival was 2 months (1.9 - 2.4 months 95% CI).

CONCLUSION

The real-world prognosis for NSCLC patients with BMs is poor. By using an algorithm, we have reported outcomes on a comprehensive cohort of patients which helps identify those for whom an active treatment approach is appropriate.

Pathological Reporting of Radical Prostatectomy Specimens Following ICCR Recommendation: Impact of Electronic Reporting Tool Implementation on Quality and Interdisciplinary Communication in a Large University Hospital

Prostate cancer represents one of the most common malignant tumors in male patients in Germany. The pathological reporting of radical prostatectomy specimens following a structured process constitutes an excellent prototype for the introduction of software-based standardized structured reporting in pathology. This can lead to reports of higher quality and could create a fundamental improvement for future AI applications. A software-based reporting template was used to generate standardized structured pathological reports of radical prostatectomy specimens of patients treated at the University Hospital Klinikum rechts der Isar of Technische Universität München, Germany. Narrative reports (NR) and standardized structured reports (SSR) were analyzed with regard to completeness, and clinicians' satisfaction with each report type was evaluated. SSR show considerably higher completeness than NR. A total of 10 categories out of 32 were significantly more complete in SSR than in NR (p < 0.05). Clinicians awarded overall high scores in NR and SSR reports. One rater acknowledged a significantly higher level of clarity and time saving when comparing SSR to NR. Our findings highlight that the standardized structured reporting of radical prostatectomy specimens, qualifying as level 5 reports, significantly increases objectively measured content quality and the level of completeness. The implementation of nationwide SSR in Germany, particularly in oncologic pathology, can serve pathologists, clinicians, and patients.

How does DICOM support big data management? Investigating its use in medical imaging community

The diagnostic imaging field is experiencing considerable growth, followed by increasing production of massive amounts of data. The lack of standardization and privacy concerns are considered the main barriers to big data capitalization. This work aims to verify whether the advanced features of the DICOM standard, beyond imaging data storage, are effectively used in research practice. This issue will be analyzed by investigating the publicly shared medical imaging databases and assessing how much the most common medical imaging software tools support DICOM in all its potential. Therefore, 100 public databases and ten medical imaging software tools were selected and examined using a systematic approach. In particular, the DICOM fields related to privacy, segmentation and reporting have been assessed in the selected database; software tools have been evaluated for reading and writing the same DICOM fields. From our analysis, less than a third of the databases examined use the DICOM format to record meaningful information to manage the images. Regarding software, the vast majority does not allow the management, reading and writing of some or all the DICOM fields. Surprisingly, if we observe chest computed tomography data sharing to address the COVID-19 emergency, there are only two datasets out of 12 released in DICOM format. Our work shows how the DICOM can potentially fully support big data management; however, further efforts are still needed from the scientific and technological community to promote the use of the existing standard, encouraging data sharing and interoperability for a concrete development of big data analytics.

Computational healthcare: Present and future perspectives (Review)

Artificial intelligence (AI) has been developed through repeated new discoveries since around 1960. The use of AI is now becoming widespread within society and our daily lives. AI is also being introduced into healthcare, such as medicine and drug development; however, it is currently biased towards specific domains. The present review traces the history of the development of various AI-based applications in healthcare and compares AI-based healthcare with conventional healthcare to show the future prospects for this type of care. Knowledge of the past and present development of AI-based applications would be useful for the future utilization of novel AI approaches in healthcare.

[Structured reporting in radiology : German and European radiology societies' point of view]

Zahlreiche Publikationen belegen den herausragenden Wert einer strukturierten Befundung sowohl für die Kommunikation mit zuweisenden klinischen Kollegen als auch für die Weiterverwendung der Befunddaten in anderen Kontexten. Obwohl das Thema bereits seit vielen Jahren in der Radiologie bekannt ist, hat sich die strukturierte Befundung noch nicht flächendeckend in der klinischen Routine etablieren können. Alle größeren radiologischen Fachgesellschaften haben sich klar für die strukturierte Befundung ausgesprochen und verfolgen etliche Initiativen auf diesem Gebiet. Dazu zählt der Aufbau frei zugänglicher Sammlungen von Befundvorlagen und die Qualitätssicherung derselben sowie die Pflege und Entwicklung standardisierter Begriffslexika. Im vorliegenden Artikel werden insbesondere die Aktivitäten der Deutschen Röntgengesellschaft und der European Society of Radiology dargestellt sowie ein kurzer Überblick über Vor- und Nachteile und verfügbare Ressourcen gegeben.

[Integration of structured reporting into the routine radiological workflow]

Klinisches/methodisches Problem

Strukturierte Befundung ist seit Jahren eines der meist diskutierten Themen in der Radiologie. Aktuell herrscht ein Mangel an nutzerfreundlichen Softwarelösungen, welche in die bestehende IT-Infrastruktur der Kliniken und Praxen integriert sind und effiziente Dateneingaben erlauben.

Radiologische Standardverfahren

Radiologische Befunde werden meist als Freitext über Spracherkennungssysteme diktiert oder per Tastatur eingegeben. Zudem werden Textbausteine für die Erstellung von Normalbefunden verwendet und bei Bedarf durch Freitextinhalte ergänzt.

Methodische Innovationen

Softwarebasierte Befundungssysteme können Spracherkennungssysteme mit radiologischen Befundvorlagen in Form von interaktiven Entscheidungsbäumen vereinen. Eine technische Integration in RIS(Radiologieinformationssystem)-, PACS(„picture archiving and communication system“)- und AV(„advanced visualization“)-Systeme über Programmierschnittstellen und Interoperabilitätsstandards ermöglicht effiziente Prozesse und die Generierung maschinenlesbarer Befunddaten.

Leistungsfähigkeit

Strukturierte, semantisch annotierte klinische Daten, die über ein strukturiertes Befundungssystem erhoben werden, stehen unmittelbar für epidemiologische Datenauswertungen und kontinuierliches KI(Künstliche Intelligenz)-Training zur Verfügung.

Bewertung

Der Einsatz der strukturierten Befundung in der radiologischen Routinediagnostik ist mit einer initialen Umstellungsphase verbunden. Eine erfolgreiche Implementierung setzt eine enge Verzahnung der technischen Infrastruktur mehrerer Systeme voraus.

Empfehlung für die Praxis

Durch die Nutzung einer hybriden, softwarebasierten Befundungslösung können radiologische Befunde mit unterschiedlichen Stufen der Struktur generiert werden. Klinische Fragestellungen oder Informationen können aus klinischen Subsystemen semiautomatisch übertragen werden, um vermeidbare Fehler zu eliminieren und die Produktivität zu erhöhen.

Human, All Too Human? An All-Around Appraisal of the "Artificial Intelligence Revolution" in Medical Imaging

Artificial intelligence (AI) has seen dramatic growth over the past decade, evolving from a niche super specialty computer application into a powerful tool which has revolutionized many areas of our professional and daily lives, and the potential of which seems to be still largely untapped. The field of medicine and medical imaging, as one of its various specialties, has gained considerable benefit from AI, including improved diagnostic accuracy and the possibility of predicting individual patient outcomes and options of more personalized treatment. It should be noted that this process can actively support the ongoing development of advanced, highly specific treatment strategies (e.g., target therapies for cancer patients) while enabling faster workflow and more efficient use of healthcare resources. The potential advantages of AI over conventional methods have made it attractive for physicians and other healthcare stakeholders, raising much interest in both the research and the industry communities. However, the fast development of AI has unveiled its potential for disrupting the work of healthcare professionals, spawning concerns among radiologists that, in the future, AI may outperform them, thus damaging their reputations or putting their jobs at risk. Furthermore, this development has raised relevant psychological, ethical, and medico-legal issues which need to be addressed for AI to be considered fully capable of patient management. The aim of this review is to provide a brief, hopefully exhaustive, overview of the state of the art of AI systems regarding medical imaging, with a special focus on how AI and the entire healthcare environment should be prepared to accomplish the goal of a more advanced human-centered world.

A Hybrid Reporting Platform for Extended RadLex Coding Combining Structured Reporting Templates and Natural Language Processing

Structured reporting is a favorable and sustainable form of reporting in radiology. Among its advantages are better presentation, clearer nomenclature, and higher quality. By using MRRT-compliant templates, the content of the categorized items (e.g., select fields) can be automatically stored in a database, which allows further research and quality analytics based on established ontologies like RadLex® linked to the items. Additionally, it is relevant to provide free-text input for descriptions of findings and impressions in complex imaging studies or for the information included with the clinical referral. So far, however, this unstructured content cannot be categorized. We developed a solution to analyze and code these free-text parts of the templates in our MRRT-compliant reporting platform, using natural language processing (NLP) with RadLex® terms in addition to the already categorized items. The established hybrid reporting concept is working successfully. The NLP tool provides RadLex® codes with modifiers (affirmed, speculated, negated). Radiologists can confirm or reject codes provided by NLP before finalizing the structured report. Furthermore, users can suggest RadLex® codes from free text that is not correctly coded with NLP or can suggest to change the modifier. Analyzing free-text fields took 1.23 s on average. Hybrid reporting enables coding of free-text information in our MRRT-compliant templates and thus increases the amount of categorized data that can be stored in the database. This enhances the possibilities for further analyses, such as correlating clinical information with radiological findings or storing high-quality structured information for machine-learning approaches.

[Current developments on digitalization : Analysis of quality and economics in healthcare]

Hintergrund

Im deutschen Gesundheitssystem und damit auch im Fachgebiet der Urologie gewinnen ökonomische Rahmenbedingungen zunehmend an Bedeutung und parallel dazu werden digitale Anwendungen vermehrt eingesetzt.

Fragestellung

Die Fragestellung betrifft die gesundheitsökonomische Auseinandersetzung mit den Rahmenbedingungen der Digitalisierung im deutschen Gesundheitssystem sowie ausgewählter Anwendungsbereiche in der Urologie.

Material und Methoden

Das Gutachten des Sachverständigenrates zur Begutachtung der Entwicklung im Gesundheitswesen (SVR) wird analysiert und eine systematische Literaturanalyse zum Einsatz der strukturierten Befundung und Analyse ausgewählter Literatur zu telemedizinischen Anwendungen in der Urologie unter gesundheitsökonomischen Gesichtspunkten durchgeführt.

Ergebnisse

Als zentrale Hemmnisse bei der Digitalisierung des deutschen Gesundheitswesens identifiziert der SVR dessen Regulierung und Komplexität sowie den Umgang mit Datenschutz und -sicherheit. Der Einsatz strukturierter Befundung kann Qualität, Effektivität und Effizienz der Befundung in der Urologie steigern. Im Hinblick auf die Kosten können signifikante Einsparungen mit zunehmender Digitalisierung in der Medizin realisiert werden.

Schlussfolgerungen

Aus medizinischer und gesundheitsökonomischer Perspektive besteht bei der Ausgestaltung von Rahmenbedingungen für digitale Anwendungen im deutschen Gesundheitssystem hinsichtlich der Informationssicherheit und des Datenschutzes weiterer Gestaltungsbedarf. Bei zielgerichtetem Einsatz von digitalen Anwendungen wie der strukturierten Befundung und der Telemedizin können optimale Voraussetzungen für den zunehmenden Einsatz von künstlicher Intelligenz im Fachgebiet der Urologie geschaffen werden.

Structured Reporting of Acute Ischemic Stroke - Consensus-Based Reporting Templates for Non-Contrast Cranial Computed Tomography, CT Angiography, and CT Perfusion

PURPOSE

 Structured reporting is an essential step in establishing standardized quality standards in diagnostic radiology. The German Society of Radiology and the German Society of Neuroradiology aim to provide templates for the structured reporting of different radiological examinations.

METHOD

 The Information Technology working group of the German Society of Radiology developed structured templates for the radiological reporting of different indications in consensus with specialist support by experts.

RESULTS

 We present a template for the structured reporting of examinations of patients with acute ischemic stroke by non-contrast computed tomography, CT angiography, and CT perfusion. This template is provided on the website www.befundung.drg.de for free use.

CONCLUSION

 Implementation of the structured template may increase quality and provide a minimum standard for radiological reports in patients with acute ischemic stroke.

KEY POINTS

· The German Society of Radiology and the German Society of Neuroradiology are providing support for the development of structured templates in German.. · We present a template for the structured reporting of examinations of patients with acute ischemic stroke by non-contrast computed tomography, CT angiography, and CT perfusion. This template is provided on the website www.befundung.drg.de for free use.. · Implementation of the structured template may increase quality and provide a minimum standard for radiological reports in patients with acute ischemic stroke..

CITATION FORMAT

· Brendle C, Bender B, Selo N et al. Structured Reporting of Acute Ischemic Stroke - Consensus-Based Reporting Templates for Non-Contrast Cranial Computed Tomography, CT Angiography, and CT Perfusion. Fortschr Röntgenstr 2021; 193: 1315 - 1317.

AI-based clinical decision-making systems in palliative medicine: ethical challenges

BACKGROUND

Improving palliative care (PC) is demanding due to the increase in people with PC needs over the next few years. An early identification of PC needs is fundamental in the care approach: it provides effective patient-centred care and could improve outcomes such as patient quality of life, reduction of the overall length of hospitalisation, survival rate prolongation, the satisfaction of both the patients and caregivers and cost-effectiveness.

METHODS

We reviewed literature with the objective of identifying and discussing the most important ethical challenges related to the implementation of AI-based data processing services in PC and advance care planning.

RESULTS

AI-based mortality predictions can signal the need for patients to obtain access to personalised communication or palliative care consultation, but they should not be used as a unique parameter to activate early PC and initiate an ACP. A number of factors must be included in the ethical decision-making process related to initiation of ACP conversations, among which are autonomy and quality of life, the risk of worsening healthcare status, the commitment by caregivers, the patients' psychosocial and spiritual distress and their wishes to initiate EOL discussions CONCLUSIONS: Despite the integration of artificial intelligence (AI)-based services into routine healthcare practice could have a positive effect of promoting early activation of ACP by means of a timely identification of PC needs, from an ethical point of view, the provision of these automated techniques raises a number of critical issues that deserve further exploration.

Emerging role of artificial intelligence in nuclear medicine

The role of artificial intelligence is increasing in all branches of medicine. The emerging role of artificial intelligence applications in nuclear medicine is going to improve the nuclear medicine clinical workflow in the coming years. Initial research outcomes are suggestive of increasing role of artificial intelligence in nuclear medicine workflow, particularly where selective automation tasks are of concern. Artificial intelligence-assisted planning, dosimetry and procedure execution appear to be areas for rapid and significant development. The role of artificial intelligence in more directly imaging-related tasks, such as dose optimization, image corrections and image reconstruction, have been particularly strong points of artificial intelligence research in nuclear medicine. Natural Language Processing (NLP)-based text processing task is another area of interest of artificial intelligence implementation in nuclear medicine.

Artificial Intelligence for Screening Chinese Electronic Medical Record and Biobank Information

Objective: To establish a structured and integrated platform of clinical data and biobank data, and a client to retrieve these data. Study Design: Initially, the hospital information system (HIS) and biobank information system (BIS) were integrated through the patients' ID numbers. Then, natural language processing (NLP) was used to process the integrated unstructured clinical information. A query interface was designed for this system, which enabled researchers to retrieve clinical or biobank data. Finally, several queries were listed and manually checked to test the retrieval performance of the system. Results: The construction of the biobank screening system (BSS) was completed, and the data were structured. The BSS took an average of 2 seconds to perform a search for target patients/samples. The retrieval results were consistent with the HIS and BIS. For complex queries, we manually checked the retrieved patients/samples, and the system's accuracy was 100%. Conclusion: This NLP-based system improved biological sample screening and using of clinical data. We will continue to improve this system, enhance resource sharing, and promote the development of translational medicine.

Artificial Intelligence and Machine Learning in Radiology: Current State and Considerations for Routine Clinical Implementation

Although artificial intelligence (AI) has been a focus of medical research for decades, in the last decade, the field of radiology has seen tremendous innovation and also public focus due to development and application of machine-learning techniques to develop new algorithms. Interestingly, this innovation is driven simultaneously by academia, existing global medical device vendors, and-fueled by venture capital-recently founded startups. Radiologists find themselves once again in the position to lead this innovation to improve clinical workflows and ultimately patient outcome. However, although the end of today's radiologists' profession has been proclaimed multiple times, routine clinical application of such AI algorithms in 2020 remains rare. The goal of this review article is to describe in detail the relevance of appropriate imaging data as a bottleneck for innovation, provide insights into the many obstacles for technical implementation, and give additional perspectives to radiologists who often view AI solely from their clinical role. As regulatory approval processes for such medical devices are currently under public discussion and the relevance of imaging data is transforming, radiologists need to establish themselves as the leading gatekeepers for evolution of their field and be aware of the many stakeholders and sometimes conflicting interests.

Structured Reporting of Rectal Cancer Staging and Restaging: A Consensus Proposal

Simple Summary

Structured reporting in oncologic imaging is becoming necessary and has recently been recognized by major scientific societies. Structured reports collect all Patient Clinical Data, Clinical Evaluations and relevant key findings of Rectal Cancer, both in staging and restaging, and can facilitate clinical decision-making.

Abstract

Background: Structured reporting (SR) in oncologic imaging is becoming necessary and has recently been recognized by major scientific societies. The aim of this study was to build MRI-based structured reports for rectal cancer (RC) staging and restaging in order to provide clinicians all critical tumor information. Materials and Methods: A panel of radiologist experts in abdominal imaging, called the members of the Italian Society of Medical and Interventional Radiology, was established. The modified Delphi process was used to build the SR and to assess the level of agreement in all sections. The Cronbach’s alpha (Cα) correlation coefficient was used to assess the internal consistency of each section and to measure the quality analysis according to the average inter-item correlation. The intraclass correlation coefficient (ICC) was also evaluated. Results: After the second Delphi round of the SR RC staging, the panelists’ single scores and sum of scores were 3.8 (range 2–4) and 169, and the SR RC restaging panelists’ single scores and sum of scores were 3.7 (range 2–4) and 148, respectively. The Cα correlation coefficient was 0.79 for SR staging and 0.81 for SR restaging. The ICCs for the SR RC staging and restaging were 0.78 (p < 0.01) and 0.82 (p < 0.01), respectively. The final SR version was built and included 53 items for RC staging and 50 items for RC restaging. Conclusions: The final version of the structured reports of MRI-based RC staging and restaging should be a helpful and promising tool for clinicians in managing cancer patients properly. Structured reports collect all Patient Clinical Data, Clinical Evaluations and relevant key findings of Rectal Cancer, both in staging and restaging, and can facilitate clinical decision-making.

Comparative analysis of machine learning algorithms for computer-assisted reporting based on fully automated cross-lingual RadLex mappings

Computer-assisted reporting (CAR) tools were suggested to improve radiology report quality by context-sensitively recommending key imaging biomarkers. However, studies evaluating machine learning (ML) algorithms on cross-lingual ontological (RadLex) mappings for developing embedded CAR algorithms are lacking. Therefore, we compared ML algorithms developed on human expert-annotated features against those developed on fully automated cross-lingual (German to English) RadLex mappings using 206 CT reports of suspected stroke. Target label was whether the Alberta Stroke Programme Early CT Score (ASPECTS) should have been provided (yes/no:154/52). We focused on probabilistic outputs of ML-algorithms including tree-based methods, elastic net, support vector machines (SVMs) and fastText (linear classifier), which were evaluated in the same 5 × fivefold nested cross-validation framework. This allowed for model stacking and classifier rankings. Performance was evaluated using calibration metrics (AUC, brier score, log loss) and -plots. Contextual ML-based assistance recommending ASPECTS was feasible. SVMs showed the highest accuracies both on human-extracted- (87%) and RadLex features (findings:82.5%; impressions:85.4%). FastText achieved the highest accuracy (89.3%) and AUC (92%) on impressions. Boosted trees fitted on findings had the best calibration profile. Our approach provides guidance for choosing ML classifiers for CAR tools in fully automated and language-agnostic fashion using bag-of-RadLex terms on limited expert-labelled training data.

To buy or not to buy-evaluating commercial AI solutions in radiology (the ECLAIR guidelines)

Abstract

Artificial intelligence (AI) has made impressive progress over the past few years, including many applications in medical imaging. Numerous commercial solutions based on AI techniques are now available for sale, forcing radiology practices to learn how to properly assess these tools. While several guidelines describing good practices for conducting and reporting AI-based research in medicine and radiology have been published, fewer efforts have focused on recommendations addressing the key questions to consider when critically assessing AI solutions before purchase. Commercial AI solutions are typically complicated software products, for the evaluation of which many factors are to be considered. In this work, authors from academia and industry have joined efforts to propose a practical framework that will help stakeholders evaluate commercial AI solutions in radiology (the ECLAIR guidelines) and reach an informed decision. Topics to consider in the evaluation include the relevance of the solution from the point of view of each stakeholder, issues regarding performance and validation, usability and integration, regulatory and legal aspects, and financial and support services.

Key Points

• Numerous commercial solutions based on artificial intelligence techniques are now available for sale, and radiology practices have to learn how to properly assess these tools.

• We propose a framework focusing on practical points to consider when assessing an AI solution in medical imaging, allowing all stakeholders to conduct relevant discussions with manufacturers and reach an informed decision as to whether to purchase an AI commercial solution for imaging applications.

• Topics to consider in the evaluation include the relevance of the solution from the point of view of each stakeholder, issues regarding performance and validation, usability and integration, regulatory and legal aspects, and financial and support services.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-020-07684-x.

Structured Reporting in the Characterization of Renal Cysts by Contrast-Enhanced Ultrasound (CEUS) Using the Bosniak Classification System-Improvement of Report Quality and Interdisciplinary Communication

BACKGROUND

This study aims to evaluate the potential benefits of structured reporting (SR) compared to conventional free-text reporting (FTR) in contrast-enhanced ultrasound (CEUS) of cystic renal lesions, based on the Bosniak classification.

METHODS

Fifty patients with cystic renal lesions who underwent CEUS were included in this single-center study. FTR created in clinical routine were compared to SR retrospectively generated by using a structured reporting template. Two experienced urologists evaluated the reports regarding integrity, effort for information extraction, linguistic quality, and overall quality.

RESULTS

The required information could easily be extracted by the reviewers in 100% of SR vs. 82% of FTR (p < 0.001). The reviewers trusted the information given by SR significantly more with a mean of 5.99 vs. 5.52 for FTR (p < 0.001). SR significantly improved the linguistic quality (6.0 for SR vs. 5.68 for FTR (p < 0.001)) and the overall report quality (5.98 for SR vs. 5.58 for FTR (p < 0.001)).

CONCLUSIONS

SR significantly increases the quality of radiologic reports in CEUS examinations of cystic renal lesions compared to conventional FTR and represents a promising approach to facilitate interdisciplinary communication in the future.

Structured Reporting Using CEUS LI-RADS for the Diagnosis of Hepatocellular Carcinoma (HCC)-Impact and Advantages on Report Integrity, Quality and Interdisciplinary Communication

Simple Summary

Contrast-enhanced ultrasound (CEUS) is an increasingly accepted imaging modality for visualizing hepatocellular carcinoma (HCC) and is recommended as a secondary imaging option by most leading hepatology societies. In recent years, the use of structured reporting (SR) has been recommended by several societies to standardize report content and improve report quality of various diagnostic modalities when compared to conventional free-text reports (FTR). Our single-center study aimed to evaluate the use of SR using a CEUS LI-RADS software template in CEUS examinations of 50 HCC patients. SR significantly increased report integrity, satisfaction of the referring physicians, linguistic quality and overall report quality compared to FTR. Therefore, the use of SR in CEUS examinations of HCC patients may represent a valuable tool to facilitate clinical decision-making and improve interdisciplinary communication in the future.

Abstract

Background: Our retrospective single-center study aims to evaluate the impact of structured reporting (SR) using a CEUS LI-RADS template on report quality compared to conventional free-text reporting (FTR) in contrast-enhanced ultrasound (CEUS) for the diagnosis of hepatocellular carcinoma (HCC). Methods: We included 50 patients who underwent CEUS for HCC staging. FTR created after these examinations were compared to SR retrospectively generated by using template-based online software with clickable decision trees. The reports were evaluated regarding report completeness, information extraction, linguistic quality and overall report quality by two readers specialized in internal medicine and visceral surgery. Results: SR significantly increased report completeness with at least one key feature missing in 31% of FTR vs. 2% of SR (p < 0.001). Information extraction was considered easy in 98% of SR vs. 86% of FTR (p = 0.004). The trust of referring physicians in the report was significantly increased by SR with a mean of 5.68 for SR vs. 4.96 for FTR (p < 0.001). SR received significantly higher ratings regarding linguistic quality (5.79 for SR vs. 4.83 for FTR (p < 0.001)) and overall report quality (5.75 for SR vs. 5.01 for FTR (p < 0.001)). Conclusions: Using SR instead of conventional FTR increases the overall quality of reports in CEUS examinations of HCC patients and may represent a valuable tool to facilitate clinical decision-making and improve interdisciplinary communication in the future.

[Artificial Intelligence in Radiology - Definition, Potential and Challenges]

Artificial Intelligence in Radiology - Definition, Potential and Challenges Abstract. Artificial Intelligence (AI) is omnipresent. It has neatly permeated our daily life, even if we are not always fully aware of its ubiquitous presence. The healthcare sector in particular is experiencing a revolution which will change our daily routine considerably in the near future. Due to its advanced digitization and its historical technical affinity radiology is especially prone to these developments. But what exactly is AI and what makes AI so potent that established medical disciplines such as radiology worry about their future job perspectives? What are the assets of AI in radiology today - and what are the major challenges? This review article tries to give some answers to these questions.

Structured Reporting of Computed Tomography in the Staging of Neuroendocrine Neoplasms: A Delphi Consensus Proposal

BACKGROUND

Structured reporting (SR) in radiology is becoming increasingly necessary and has been recognized recently by major scientific societies. This study aims to build structured CT-based reports in Neuroendocrine Neoplasms during the staging phase in order to improve communication between the radiologist and members of multidisciplinary teams.

MATERIALS AND METHODS

A panel of expert radiologists, members of the Italian Society of Medical and Interventional Radiology, was established. A Modified Delphi process was used to develop the SR and to assess a level of agreement for all report sections. Cronbach's alpha (Cα) correlation coefficient was used to assess internal consistency for each section and to measure quality analysis according to the average inter-item correlation.

RESULTS

The final SR version was built by including n=16 items in the "Patient Clinical Data" section, n=13 items in the "Clinical Evaluation" section, n=8 items in the "Imaging Protocol" section, and n=17 items in the "Report" section. Overall, 54 items were included in the final version of the SR. Both in the first and second round, all sections received more than a good rating: a mean value of 4.7 and range of 4.2-5.0 in the first round and a mean value 4.9 and range of 4.9-5 in the second round. In the first round, the Cα correlation coefficient was a poor 0.57: the overall mean score of the experts and the sum of scores for the structured report were 4.7 (range 1-5) and 728 (mean value 52.00 and standard deviation 2.83), respectively. In the second round, the Cα correlation coefficient was a good 0.82: the overall mean score of the experts and the sum of scores for the structured report were 4.9 (range 4-5) and 760 (mean value 54.29 and standard deviation 1.64), respectively.

CONCLUSIONS

The present SR, based on a multi-round consensus-building Delphi exercise following in-depth discussion between expert radiologists in gastro-enteric and oncological imaging, derived from a multidisciplinary agreement between a radiologist, medical oncologist and surgeon in order to obtain the most appropriate communication tool for referring physicians.

The role of structured reporting and structured operation planning in functional endoscopic sinus surgery

Computed tomography (CT) scans represent the gold standard in the planning of functional endoscopic sinus surgeries (FESS). Yet, radiologists and otolaryngologists have different perspectives on these scans. In general, residents often struggle with aspects involved in both reporting and operation planning. The aim of this study was to compare the completeness of structured reports (SR) of preoperative CT images and structured operation planning (SOP) to conventional reports (CR) and conventional operation planning (COP) to potentially improve future treatment decisions on an individual level. In total, 30 preoperative CT scans obtained for surgical planning of patients scheduled for FESS were evaluated using SR and CR by radiology residents. Subsequently, otolaryngology residents performed a COP using free texts and a SOP using a specific template. All radiology reports and operation plannings were evaluated by two experienced FESS surgeons regarding their completeness for surgical planning. User satisfaction of otolaryngology residents was assessed by using visual analogue scales. Overall radiology report completeness was significantly higher using SRs regarding surgically important structures compared to CRs (84.4 vs. 22.0%, p<0.001). SOPs produced significantly higher completeness ratings (97% vs. 39.4%, p<0.001) regarding pathologies and anatomical variances. Moreover, time efficiency was not significantly impaired by implementation of SR (148 s vs. 160 s, p = 0.61) and user satisfaction was significantly higher for SOP (VAS 8.1 vs. 4.1, p<0.001). Implementation of SR and SOP results in a significantly increased completeness of radiology reports and operation planning for FESS. Consequently, the combination of both facilitates surgical planning and may decrease potential risks during FESS.

Artificial Intelligence and Big Data

Medical images play an important role in ophthalmology and radiology. Medical image analysis has greatly benefited from the application of "deep learning" techniques in clinical and experimental radiology. Clinical applications and their relevance for radiological imaging in ophthalmology are presented.

Proposed Implementation of Blockchain in British Columbia's Health Care Data Management

Background

There are several challenges such as information silos and lack of interoperability with the current electronic medical record (EMR) infrastructure in the Canadian health care system. These challenges can be alleviated by implementing a blockchain-based health care data management solution.

Objective

This study aims to provide a detailed overview of the current health data management infrastructure in British Columbia for identifying some of the gaps and inefficiencies in the Canadian health care data management system. We explored whether blockchain is a viable option for bridging the existing gaps in EMR solutions in British Columbia’s health care system.

Methods

We constructed the British Columbia health care data infrastructure and health information flow based on publicly available information and in partnership with an industry expert familiar with the health systems information technology network of British Columbia’s Provincial Health Services Authorities. Information flow gaps, inconsistencies, and inefficiencies were the target of our analyses.

Results

We found that hospitals and clinics have several choices for managing electronic records of health care information, such as different EMR software or cloud-based data management, and that the system development, implementation, and operations for EMRs are carried out by the private sector. As of 2013, EMR adoption in British Columbia was at 80% across all hospitals and the process of entering medical information into EMR systems in British Columbia could have a lag of up to 1 month. During this lag period, disease progression updates are continually written on physical paper charts and not immediately updated in the system, creating a continuous lag period and increasing the probability of errors and disjointed notes. The current major stumbling block for health care data management is interoperability resulting from the use of a wide range of unique information systems by different health care facilities.

Conclusions

Our analysis of British Columbia’s health care data management revealed several challenges, including information silos, the potential for medical errors, the general unwillingness of parties within the health care system to trust and share data, and the potential for security breaches and operational issues in the current EMR infrastructure. A blockchain-based solution has the highest potential in solving most of the challenges in managing health care data in British Columbia and other Canadian provinces.

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.

Structured reporting of chest CT in COVID-19 pneumonia: a consensus proposal

OBJECTIVES

The need of a standardized reporting scheme and language, in imaging of COVID-19 pneumonia, has been welcomed by major scientific societies. The aim of the study was to build the reporting scheme of chest CT in COVID-19 pneumonia.

METHODS

A team of experts, of the Italian Society of Medical and Interventional Radiology (SIRM), has been recruited to compose a consensus panel. They used a modified Delphi process to build a reporting scheme and expressed a level of agreement for each section of the report. To measure the internal consistency of the panelist ratings for each section of the report, a quality analysis based on the average inter-item correlation was performed with Cronbach's alpha (Cα) correlation coefficient.

RESULTS

The overall mean score of the experts and the sum of score were 3.1 (std.dev. ± 0.11) and 122 in the second round, and improved to 3.75 (std.dev. ± 0.40) and 154 in the third round. The Cronbach's alpha (Cα) correlation coefficient was 0.741 (acceptable) in the second round and improved to 0.789 in the third round. The final report was built in the management of radiology report template (MRRT) and includes n = 4 items in the procedure information, n = 5 items in the clinical information, n = 16 in the findings, and n = 3 in the impression, with overall 28 items.

CONCLUSIONS

The proposed structured report could be of help both for expert radiologists and for the less experienced who are faced with the management of these patients. The structured report is conceived as a guideline, to recommend the key items/findings of chest CT in COVID-19 pneumonia.

Dose independent characterization of renal stones by means of dual energy computed tomography and machine learning: an ex-vivo study

OBJECTIVES

To predict the main component of pure and mixed kidney stones using dual-energy computed tomography and machine learning.

METHODS

200 kidney stones with a known composition as determined by infrared spectroscopy were examined using a non-anthropomorphic phantom on a spectral detector computed tomography scanner. Stones were of either pure (monocrystalline, n = 116) or compound (dicrystalline, n = 84) composition. Image acquisition was repeated twice using both, normal and low-dose protocols, respectively (ND/LD). Conventional images and low and high keV virtual monoenergetic images were reconstructed. Stones were semi-automatically segmented. A shallow neural network was trained using data from ND1 acquisition split into training (70%), testing (15%) and validation-datasets (15%). Performance for ND2 and both LD acquisitions was tested. Accuracy on a per-voxel and a per-stone basis was calculated.

RESULTS

Main components were: Whewellite (n = 80), weddellite (n = 21), Ca-phosphate (n = 39), cysteine (n = 20), struvite (n = 13), uric acid (n = 18) and xanthine stones (n = 9). Stone size ranged from 3 to 18 mm. Overall accuracy for predicting the main component on a per-voxel basis attained by ND testing dataset was 91.1%. On independently tested acquisitions, accuracy was 87.1-90.4%.

CONCLUSIONS

Even in compound stones, the main component can be reliably determined using dual energy CT and machine learning, irrespective of dose protocol.

KEY POINTS

• Spectral Detector Dual Energy CT and Machine Learning allow for an accurate prediction of stone composition. • Ex-vivo study demonstrates the dose independent assessment of pure and compound stones. • Lowest accuracy is reported for compound stones with struvite as main component.

Structured report data can be used to develop deep learning algorithms: a proof of concept in ankle radiographs

BACKGROUND

Data used for training of deep learning networks usually needs large amounts of accurate labels. These labels are usually extracted from reports using natural language processing or by time-consuming manual review. The aim of this study was therefore to develop and evaluate a workflow for using data from structured reports as labels to be used in a deep learning application.

MATERIALS AND METHODS

We included all plain anteriorposterior radiographs of the ankle for which structured reports were available. A workflow was designed and implemented where a script was used to automatically retrieve, convert, and anonymize the respective radiographs of cases where fractures were either present or absent from the institution's picture archiving and communication system (PACS). These images were then used to retrain a pretrained deep convolutional neural network. Finally, performance was evaluated on a set of previously unseen radiographs.

RESULTS

Once implemented and configured, completion of the whole workflow took under 1 h. A total of 157 structured reports were retrieved from the reporting platform. For all structured reports, corresponding radiographs were successfully retrieved from the PACS and fed into the training process. On an unseen validation subset, the model showed a satisfactory performance with an area under the curve of 0.850 (95% CI 0.634-1.000) for detection of fractures.

CONCLUSION

We demonstrate that data obtained from structured reports written in clinical routine can be used to successfully train deep learning algorithms. This highlights the potential role of structured reporting for the future of radiology, especially in the context of deep learning.