Deep Learning-Based Natural Language Processing in Radiology:The Impact of Report Complexity, Disease Prevalence, Dataset Size, and Algorithm Type on Model Performance

In radiology, natural language processing (NLP) allows the extraction of valuable information from radiology reports. It can be used for various downstream tasks such as quality improvement, epidemiological research, and monitoring guideline adherence. Class imbalance, variation in dataset size, variation in report complexity, and algorithm type all influence NLP performance but have not yet been systematically and interrelatedly evaluated. In this study, we investigate these factors on the performance of four types [a fully connected neural network (Dense), a long short-term memory recurrent neural network (LSTM), a convolutional neural network (CNN), and a Bidirectional Encoder Representations from Transformers (BERT)] of deep learning-based NLP. Two datasets consisting of radiologist-annotated reports of both trauma radiographs (n = 2469) and chest radiographs and computer tomography (CT) studies (n = 2255) were split into training sets (80%) and testing sets (20%). The training data was used as a source to train all four model types in 84 experiments (Fracture-data) and 45 experiments (Chest-data) with variation in size and prevalence. The performance was evaluated on sensitivity, specificity, positive predictive value, negative predictive value, area under the curve, and F score. After the NLP of radiology reports, all four model-architectures demonstrated high performance with metrics up to > 0.90. CNN, LSTM, and Dense were outperformed by the BERT algorithm because of its stable results despite variation in training size and prevalence. Awareness of variation in prevalence is warranted because it impacts sensitivity and specificity in opposite directions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10916-021-01761-4

Promises of artificial intelligence in neuroradiology:a systematic technographic review

Purpose To conduct a systematic review of the possibilities of artificial intelligence (AI) in neuroradiology by performing an objective, systematic assessment of available applications. To analyse the potential impacts of AI applications on the work of neuroradiologists. Methods We identified AI applications offered on the market during the period 2017–2019. We systematically collected and structured information in a relational database and coded for the characteristics of the applications, their functionalities for the radiology workflow and their potential impacts in terms of ‘supporting’, ‘extending’ and ‘replacing’ radiology tasks. Results We identified 37 AI applications in the domain of neuroradiology from 27 vendors, together offering 111 functionalities. The majority of functionalities ‘support’ radiologists, especially for the detection and interpretation of image findings. The second-largest group of functionalities ‘extends’ the possibilities of radiologists by providing quantitative information about pathological findings. A small but noticeable portion of functionalities seek to ‘replace’ certain radiology tasks. Conclusion Artificial intelligence in neuroradiology is not only in the stage of development and testing but also available for clinical practice. The majority of functionalities support radiologists or extend their tasks. None of the applications can replace the entire radiology profession, but a few applications can do so for a limited set of tasks. Scientific validation of the AI products is more limited than the regulatory approval

Tunable critical field in Rashba superconductor thin-films

The upper critical field in type II superconductors is limited by the Pauli paramagnetic limit. In superconductors with strong Rashba spin-orbit coupling this limit can be overcome by forming a helical state. Here we quantitatively study the magnetic field-temperature phase diagram of finite-size superconductors with Rashba spin-orbit coupling. We discuss the effect of finite size and shape anisotropy. We demonstrate that the critical field is controllable by intrinsic parameters such as spin-orbit coupling strength and tunable parameters such as sample geometry and applied field direction. Our study opens new avenues for the design of superconducting spin-valves.Comment: 5 pages, 4 figures, supplemental material

Extension of the recorded host range of Caribbean Christmas tree worms (<i>Spirobranchus</i> spp.) with two Scleractinians, a Zoantharian, and an Ascidian

Caribbean Christmas tree worms (Annelida: Polychaeta: Serpulidae: Spirobranchus) are considered host generalists in their associations with anthozoan (Scleractinia) and hydrozoan (Millepora) stony corals [...

Multiple Andreev reflections in two-dimensional Josephson junctions with broken time-reversal symmetry

Andreev bound states (ABS) occur in Josephson junctions when the total phase of the Andreev and normal reflections is a multiple of $2\pi$. In ballistic junctions with an applied voltage bias, a quasi-particle undergoes multiple Andreev reflections before entering the leads, resulting in peaks in the current-voltage $I(V)$ curve. Here we present a general model for Josephson junctions with spin-active interlayers i.e., magnetic or topological materials with broken time-reversal symmetry. We investigate how ABS change the peak positions and shape of $I(V)$, which becomes asymmetric for a single incident angle. We show how the angle-resolved $I(V)$ curve becomes a spectroscopic tool for the chirality and degeneracy of ABS.Comment: 5 pages, 3 figure

Contextual Structured Reporting in Radiology:Implementation and Long-Term Evaluation in Improving the Communication of Critical Findings

Structured reporting contributes to the completeness of radiology reports and improves quality. Both the content and the structure are essential for successful implementation of structured reporting. Contextual structured reporting is tailored to a specific scenario and can contain information retrieved from the context. Critical findings detected by imaging need urgent communication to the referring physician. According to guidelines, the occurrence of this communication should be documented in the radiology reports and should contain when, to whom and how was communicated. In free-text reporting, one or more of these required items might be omitted. We developed a contextual structured reporting template to ensure complete documentation of the communication of critical findings. The WHEN and HOW items were included automatically, and the insertion of the WHO-item was facilitated by the template. A pre- and post-implementation study demonstrated a substantial improvement in guideline adherence. The template usage improved in the long-term post-implementation study compared with the short-term results. The two most often occurring categories of critical findings are "infection / inflammation" and "oncology", corresponding to the a large part of urgency level 2 (to be reported within 6 h) and level 3 (to be reported within 6 days), respectively. We conclude that contextual structured reporting is feasible for required elements in radiology reporting and for automated insertion of context-dependent data. Contextual structured reporting improves guideline adherence for communication of critical findings

Spin-transport in superconductors

Spin-transport in superconductors is a subject of fundamental and technical importance with the potential for applications in superconducting-based cryogenic memory and logic. Research in this area is rapidly intensifying with recent discoveries establishing the field of superconducting spintronics. In this perspective we provide an overview of the experimental state-of-the-art with a particular focus on local and nonlocal spin-transport in superconductors, and propose device schemes to demonstrate the viability of superconducting spin-based devices.Comment: Accepted in APL Perspective

Machine learning based natural language processing of radiology reports in orthopaedic trauma

OBJECTIVES: To compare different Machine Learning (ML) Natural Language Processing (NLP) methods to classify radiology reports in orthopaedic trauma for the presence of injuries. Assessing NLP performance is a prerequisite for downstream tasks and therefore of importance from a clinical perspective (avoiding missed injuries, quality check, insight in diagnostic yield) as well as from a research perspective (identification of patient cohorts, annotation of radiographs). METHODS: Datasets of Dutch radiology reports of injured extremities (n = 2469, 33% fractures) and chest radiographs (n = 799, 20% pneumothorax) were collected in two different hospitals and labeled by radiologists and trauma surgeons for the presence or absence of injuries. NLP classification was applied and optimized by testing different preprocessing steps and different classifiers (Rule-based, ML, and Bidirectional Encoder Representations from Transformers (BERT)). Performance was assessed by F1-score, AUC, sensitivity, specificity and accuracy. RESULTS: The deep learning based BERT model outperforms all other classification methods which were assessed. The model achieved an F1-score of (95 ± 2)% and accuracy of (96 ± 1)% on a dataset of simple reports (n= 2469), and an F1 of (83 ± 7)% with accuracy (93 ± 2)% on a dataset of complex reports (n= 799). CONCLUSION: BERT NLP outperforms traditional ML and rule-base classifiers when applied to Dutch radiology reports in orthopaedic trauma

Plant sterols cause macrothrombocytopenia in a mouse model of sitosterolemia

Mutations in either ABCG5 or ABCG8 cause sitosterolemia, an inborn error of metabolism characterized by high plasma plant sterol concentrations. Recently, macrothrombocytopenia was described in a number of sitosterolemia patients, linking hematological dysfunction to disturbed sterol metabolism. Here, we demonstrate that macrothrombocytopenia is an intrinsic feature of murine sitosterolemia. Abcg5-deficient (Abcg5(-/-)) mice showed a 68% reduction in platelet count, and platelets were enlarged compared with wild-type controls. Macrothrombocytopenia was not due to decreased numbers of megakaryocytes or their progenitors, but defective megakaryocyte development with deterioration of the demarcation membrane system was evident. Lethally irradiated wild-type mice transplanted with bone marrow from Abcg5(-/-) mice displayed normal platelets, whereas Abcg5(-/-) mice transplanted with wild-type bone marrow still showed macrothrombocytopenia. Treatment with the sterol absorption inhibitor ezetimibe rapidly reversed macrothrombocytopenia in Abcg5(-/-) mice concomitant with a strong decrease in plasma plant sterols. Thus, accumulation of plant sterols is responsible for development of macrothrombocytopenia in sitosterolemia, and blocking intestinal plant sterol absorption provides an effective means of treatment