A novel reporting workflow for automated integration of artificial intelligence results into structured radiology reports.

OBJECTIVES: Artificial intelligence (AI) has tremendous potential to help radiologists in daily clinical routine. However, a seamless, standardized, and time-efficient way of integrating AI into the radiology workflow is often lacking. This constrains the full potential of this technology. To address this, we developed a new reporting pipeline that enables automated pre-population of structured reports with results provided by AI tools. METHODS: Findings from a commercially available AI tool for chest X-ray pathology detection were sent to an IHE-MRRT-compliant structured reporting (SR) platform as DICOM SR elements and used to automatically pre-populate a chest X-ray SR template. Pre-populated AI results could be validated, altered, or deleted by radiologists accessing the SR template. We assessed the performance of this newly developed AI to SR pipeline by comparing reporting times and subjective report quality to reports created as free-text and conventional structured reports. RESULTS: Chest X-ray reports with the new pipeline could be created in significantly less time than free-text reports and conventional structured reports (mean reporting times: 66.8 s vs. 85.6 s and 85.8 s, respectively; both p < 0.001). Reports created with the pipeline were rated significantly higher quality on a 5-point Likert scale than free-text reports (p < 0.001). CONCLUSION: The AI to SR pipeline offers a standardized, time-efficient way to integrate AI-generated findings into the reporting workflow as parts of structured reports and has the potential to improve clinical AI integration and further increase synergy between AI and SR in the future. CRITICAL RELEVANCE STATEMENT: With the AI-to-structured reporting pipeline, chest X-ray reports can be created in a standardized, time-efficient, and high-quality manner. The pipeline has the potential to improve AI integration into daily clinical routine, which may facilitate utilization of the benefits of AI to the fullest. KEY POINTS: • A pipeline was developed for automated transfer of AI results into structured reports. • Pipeline chest X-ray reporting is faster than free-text or conventional structured reports. • Report quality was also rated higher for reports created with the pipeline. • The pipeline offers efficient, standardized AI integration into the clinical workflow.

Implementation of structured reporting in clinical routine: a review of 7 years of institutional experience.

BACKGROUND: To evaluate the implementation process of structured reporting (SR) in a tertiary care institution over a period of 7 years. METHODS: We analysed the content of our image database from January 2016 to December 2022 and compared the numbers of structured reports and free-text reports. For the ten most common SR templates, usage proportions were calculated on a quarterly basis. Annual modality-specific SR usage was calculated for ultrasound, CT, and MRI. During the implementation process, we surveyed radiologists and clinical referring physicians concerning their views on reporting in radiology. RESULTS: As of December 2022, our reporting platform contained more than 22,000 structured reports. Use of the ten most common SR templates increased markedly since their implementation, leading to a mean SR usage of 77% in Q4 2022. The highest percentages of SR usage were shown for trauma CT, focussed assessment with ultrasound for trauma (FAST), and prostate MRI: 97%, 95%, and 92%, respectively, in 2022. Overall modality-specific SR usage was 17% for ultrasound, 13% for CT, and 6% for MRI in 2022. Both radiologists and referring physicians were more satisfied with structured reports and rated SR better than free-text reporting (FTR) on various attributes. CONCLUSIONS: The increasing SR usage during the period under review and the positive attitude towards SR among both radiologists and clinical referrers show that SR can be successfully implemented. We therefore encourage others to take this step in order to benefit from the advantages of SR. KEY POINTS: 1. Structured reporting usage increased markedly since its implementation at our institution in 2016. 2. Mean usage for the ten most popular structured reporting templates was 77% in 2022. 3. Both radiologists and referring physicians preferred structured reports over free-text reports. 4. Our data shows that structured reporting can be successfully implemented. 5. We strongly encourage others to implement structured reporting at their institutions.

Fully automated AI-based splenic segmentation for predicting survival and estimating the risk of hepatic decompensation in TACE patients with HCC.

OBJECTIVES: Splenic volume (SV) was proposed as a relevant prognostic factor for patients with hepatocellular carcinoma (HCC). We trained a deep-learning algorithm to fully automatically assess SV based on computed tomography (CT) scans. Then, we investigated SV as a prognostic factor for patients with HCC undergoing transarterial chemoembolization (TACE). METHODS: This retrospective study included 327 treatment-naïve patients with HCC undergoing initial TACE at our tertiary care center between 2010 and 2020. A convolutional neural network was trained and validated on the first 100 consecutive cases for spleen segmentation. Then, we used the algorithm to evaluate SV in all 327 patients. Subsequently, we evaluated correlations between SV and survival as well as the risk of hepatic decompensation during TACE. RESULTS: The algorithm showed Sørensen Dice Scores of 0.96 during both training and validation. In the remaining 227 patients assessed with the algorithm, spleen segmentation was visually approved in 223 patients (98.2%) and failed in four patients (1.8%), which required manual re-assessments. Mean SV was 551 ml. Survival was significantly lower in patients with high SV (10.9 months), compared to low SV (22.0 months, p = 0.001). In contrast, overall survival was not significantly predicted by axial and craniocaudal spleen diameter. Furthermore, patients with a hepatic decompensation after TACE had significantly higher SV (p < 0.001). CONCLUSION: Automated SV assessments showed superior survival predictions in patients with HCC undergoing TACE compared to two-dimensional spleen size estimates and identified patients at risk of hepatic decompensation. Thus, SV could serve as an automatically available, currently underappreciated imaging biomarker. KEY POINTS: • Splenic volume is a relevant prognostic factor for prediction of survival in patients with HCC undergoing TACE, and should be preferred over two-dimensional surrogates for splenic size. • Besides overall survival, progression-free survival and hepatic decompensation were significantly associated with splenic volume, making splenic volume a currently underappreciated prognostic factor prior to TACE. • Splenic volume can be fully automatically assessed using deep-learning methods; thus, it is a promising imaging biomarker easily integrable into daily radiological routine.

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.

A proof of concept for epidemiological research using structured reporting with pulmonary embolism as a use case.

OBJECTIVE: This paper studies the possibilities of an integrated IT-based workflow for epidemiological research in pulmonary embolism (PE) using freely available tools and structured reporting (SR). METHODS: We included a total of 521 consecutive cases which had been referred to the radiology department for CT pulmonary angiography with suspected PE. Free-text reports were transformed into structured reports using a freely available IHE Management of Radiology Report Templates-compliant reporting platform. D-dimer values were retrieved from the hospitals laboratory results system. All information was stored in the platform's database and visualized using freely available tools. For further analysis, we directly accessed the platform's database with an advanced analytics tool (RapidMiner). RESULTS: Results: We were able to develop an integrated workflow for epidemiological statistics from reports obtained in clinical routine. The report data allowed for automated calculation of epidemiological parameters. Prevalence of PE was 27.6%. The mean age in patients with and without PE did not differ (62.8 years and 62.0 years, respectively, p = 0.987). As expected, there was a significant difference in mean D-dimer values (10.13 and 3.12 mg l-1 fibrinogen equivalent units, respectively, p < 0.001). CONCLUSION: SR can make data obtained from clinical routine more accessible. Designing practical workflows is feasible using freely available tools and allows for the calculation of epidemiological statistics on a near realtime basis. Therefore, radiologists should push for the implementation of SR in clinical routine. Summary sentence: Implementing practical workflows that allow for the calculation of epidemiological statistics using SR and freely available tools is easily feasible. Advances in knowledge: Theoretical benefits of SR have long been discussed, but practical implementation demonstrating those benefits has been lacking. Here, we present a first experience providing proof that SR will make data from clinical routine more accessible.

Guidelines Regarding §16 of the German Transplantation Act - Initial Experiences with Structured Reporting.

Purpose To transfer the report sheet from the guidelines regarding the German Transplantation Act to a standards-compliant report template and to evaluate it in the clinical routine. Materials and Methods The template was developed using the freely available software brackets.io. It was implemented in the clinical routine using a reporting platform developed in-house. Interfaces to the department RIS and PACS allowed for integration into the usual reporting workflow. The evaluation period was 70 days. Results Developing the template for implementation of the guidelines was possible without any difficulties. The content of the report sheet provided in the guidelines was transferred one to one. Additionally, a text field was included to allow for further remarks. In the period under review, 7 radiologists performed 44 evaluations in line with §â€Š16 of the German Transplantation Act. Users of the template, referring physicians and the employees of the transplantation office reported a high degree of satisfaction. Conclusion Implementing report sheets that are required by law (e. g. in the guidelines regarding §â€Š16 of the German Transplantation Act) in the clinical routine electronically is easy and achieves a high degree of acceptance. The standard supported by the German Radiological Society (IHE - "Management of radiology report templates") allows for a quick response to the growing demand for structured and standardized reporting. Key Points · Report sheets as required by law can easily be incorporated electronically into the clinical routine.. · Templates for structured reporting as supported by the German Radiological Society allow for a quick response to the growing demand for standardized reporting.. · Radiologists as well as referring physicians report a high degree of satisfaction with the electronic version of the report sheet.. Citation Format · Pinto dos Santos D, Arnhold G, Mildenberger P et al. Guidelines Regarding §16 of the German Transplantation Act - Initial Experiences with Structured Reporting. Fortschr Röntgenstr 2017; 189: 1145 - 1151.