PACS-integrated machine learning breast density classifier: clinical validation.

OBJECTIVE: To test the performance of a novel machine learning-based breast density tool. The tool utilizes a convolutional neural network to predict the BI-RADS based density assessment of a study. The clinical density assessments of 33,000 mammographic examinations (164,000 images) from one academic medical center (Site A) were used for training. MATERIALS AND METHODS: This was an IRB approved HIPAA compliant study performed at two academic medical centers. The validation data set was composed of 500 studies from one site (Site A) and 700 from another (Site B). At Site A, each study was assessed by three breast radiologists and the majority (consensus) assessment was used as truth. At Site B, if the tool agreed with the clinical reading, then it was considered to have correctly predicted the clinical reading. In cases where the tool and the clinical reading disagreed, then the study was evaluated by three radiologists and the consensus reading was used as the clinical reading. RESULTS: For the classification into the four categories of the Breast Imaging Reporting and Data System (BI-RADS®), the AI classifier had an accuracy of 84.6% at Site A and 89.7% at Site B. For binary classification (dense vs. non-dense), the AI classifier had an accuracy of 94.4% at Site A and 97.4% at Site B. In no case did the classifier disagree with the consensus reading by more than one category. CONCLUSIONS: The automated breast density tool showed high agreement with radiologists' assessments of breast density.

Clinical implementation of artificial intelligence in neuroradiology with development of a novel workflow-efficient picture archiving and communication system-based automated brain tumor segmentation and radiomic feature extraction.

Purpose: Personalized interpretation of medical images is critical for optimum patient care, but current tools available to physicians to perform quantitative analysis of patient's medical images in real time are significantly limited. In this work, we describe a novel platform within PACS for volumetric analysis of images and thus development of large expert annotated datasets in parallel with radiologist performing the reading that are critically needed for development of clinically meaningful AI algorithms. Specifically, we implemented a deep learning-based algorithm for automated brain tumor segmentation and radiomics extraction, and embedded it into PACS to accelerate a supervised, end-to- end workflow for image annotation and radiomic feature extraction. Materials and methods: An algorithm was trained to segment whole primary brain tumors on FLAIR images from multi-institutional glioma BraTS 2021 dataset. Algorithm was validated using internal dataset from Yale New Haven Health (YHHH) and compared (by Dice similarity coefficient [DSC]) to radiologist manual segmentation. A UNETR deep-learning was embedded into Visage 7 (Visage Imaging, Inc., San Diego, CA, United States) diagnostic workstation. The automatically segmented brain tumor was pliable for manual modification. PyRadiomics (Harvard Medical School, Boston, MA) was natively embedded into Visage 7 for feature extraction from the brain tumor segmentations. Results: UNETR brain tumor segmentation took on average 4 s and the median DSC was 86%, which is similar to published literature but lower than the RSNA ASNR MICCAI BRATS challenge 2021. Finally, extraction of 106 radiomic features within PACS took on average 5.8 ± 0.01 s. The extracted radiomic features did not vary over time of extraction or whether they were extracted within PACS or outside of PACS. The ability to perform segmentation and feature extraction before radiologist opens the study was made available in the workflow. Opening the study in PACS, allows the radiologists to verify the segmentation and thus annotate the study. Conclusion: Integration of image processing algorithms for tumor auto-segmentation and feature extraction into PACS allows curation of large datasets of annotated medical images and can accelerate translation of research into development of personalized medicine applications in the clinic. The ability to use familiar clinical tools to revise the AI segmentations and natively embedding the segmentation and radiomic feature extraction tools on the diagnostic workstation accelerates the process to generate ground-truth data.

Practical Guide to Natural Language Processing for Radiology.

Natural language processing (NLP) is the subset of artificial intelligence focused on the computer interpretation of human language. It is an invaluable tool in the analysis, aggregation, and simplification of free text. It has already demonstrated significant potential in the analysis of radiology reports. There are abundant open-source libraries and tools available that facilitate its application to the benefit of radiology. Radiologists who understand its limitations and potential will be better positioned to evaluate NLP models, understand how they can improve clinical workflow, and facilitate research endeavors involving large amounts of human language. The advent of increasingly affordable and powerful computer processing, the large quantities of medical and radiologic data, and advances in machine learning algorithms have contributed to the large potential of NLP. In turn, radiology has significant potential to benefit from the ability of NLP to convert relatively standardized radiology reports to machine-readable data. NLP benefits from standardized reporting, but because of its ability to interpret free text by using context clues, NLP does not necessarily depend on it. An overview and practical approach to NLP is featured, with specific emphasis on its applications to radiology. A brief history of NLP, the strengths and challenges inherent to its use, and freely available resources and tools are covered to guide further exploration and study within the field. Particular attention is devoted to the recent development of the Word2Vec and BERT (Bidirectional Encoder Representations from Transformers) language models, which have exponentially increased the power and utility of NLP for a variety of applications. Online supplemental material is available for this article. ©RSNA, 2021.

Current state of radiology report release in electronic patient portals.

OBJECTIVE: The aim of our study is to evaluate the current practice patterns of radiology report release into electronic patient portals. METHODS: A survey to assess details of radiology report release was distributed to members of The Association of Administrators in Academic Radiology across the United States. Numerical analysis was used to calculate the frequencies and percentages for the clinical site, frequency and pattern of patient portal use were calculated. Statistical analysis determined the percentages and frequencies for the clinical site, frequency and pattern of patient portal use, as well as statistical differences. RESULTS: A total of 31 (response rate = 28%, 31/108) at least partially completed surveys were received. Most (29/31, 94%) sites reported having a patient portal available with 80% (12/15) reporting < 50% patient utilization. There were no significant (p > 0.05) geographical differences noted in percentage utilization. Seventy-eight percent (21/27) of sites reported some form of automatic radiology report release into their portal. Mean delay was 4 days (range 0-7) from report completion to portal release. No correlation (r = 2) was seen between percentage of patient utilization of portals and timing of radiology report release. CONCLUSION: Most academic centers across the country have patient portals, however, most of these centers report less than 50% utilization of the portals by patients. While variability in radiology report release in patient portals was noted, the majority (78%) of academic medical centers have some form of automatic report release with average delay of 4 days between report completion to portal release.

Impact of Radiologist-Driven Change-Order Requests on Outpatient CT and MRI Examinations.

PURPOSE: To assess impact of electronic medical record-embedded radiologist-driven change-order request on outpatient CT and MRI examinations. METHODS: Outpatient CT and MRI requests where an order change was requested by the protocoling radiologist in our tertiary care center, from April 11, 2017, to January 3, 2018, were analyzed. Percentage and categorization of requested order change, provider acceptance of requested change, patient and provider demographics, estimated radiation exposure reduction, and cost were analyzed. P < .05 was used for statistical significance. RESULTS: In 79,310 outpatient studies in which radiologists determined protocol, change-order requests were higher for MRI (5.2%, 1,283 of 24,553) compared with CT (2.9%, 1,585 of 54,757; P < .001). Provider approval of requested change was equivalent for CT (82%, 1,299 of 1,585) and MRI (82%, 1,052 of 1,283). Change requests driven by improper contrast media utilization were most common and different between CT (76%, 992 of 1,299) and MRI (65%, 688 of 1,052; P < .001). Changing without and with intravenous contrast orders to with contrast only was most common for CT (39%, 505 of 1,299) and with and without intravenous contrast to without contrast only was most common for MRI (26%, 274 of 1,052; P < .001). Of approved changes in CT, 51% (661 of 1,299) resulted in lower radiation exposure. Approved changes frequently resulted in less costly examinations (CT 67% [799 of 1,198], MRI 48% [411 of 863]). CONCLUSION: Outpatient CT and MRI orders are deemed incorrect in 2.9% to 5% of cases. Radiologist-driven change-order request for CT and MRI are well accepted by ordering providers and decrease radiation exposure associated with imaging.

Thoracic imaging in the ICU.

Imaging in the ICU plays a crucial role in patient care. The portable chest radiograph (CXR) is the most commonly requested radiographic examination, and, despite its limitations, it often reveals abnormalities that may not be detected clinically. Recent advances in CT technology have made it possible to obtain diagnostic-quality images even in the most dyspneic patient. This article reviews the significant contribution thoracic imaging makes in diagnosing and managing critically ill patients.

Pneumothorax in patients with acute respiratory distress syndrome: pathophysiology, detection, and treatment.

Pneumothorax is a frequent and potentially fatal complication of mechanical ventilation in patients with acute respiratory distress syndrome (ARDS). Prompt recognition and treatment of pneumothoraces is necessary to minimize morbidity and mortality. The radiologic and clinical signs of pneumothoraces in ARDS patients may have unusual and subtle features. Furthermore, small pneumothoraces in these patients can cause severe hemodynamic or pulmonary compromise. Sparse clinical literature exists on when or how to treat pneumothoraces once they develop in patients with ARDS. In this article, the authors review the pathogenesis, radiologic signs, clinical significance, and treatment of pneumothoraces in ARDS patients. Treatment options include traditional tube thoracostomy, open thoracotomy, and image-guided percutaneous catheters.