The natural language processing of radiology requests and reports of chest imaging:Comparing five transformer models’ multilabel classification and a proof-of-concept study

Background: Radiology requests and reports contain valuable information about diagnostic findings and indications, and transformer-based language models are promising for more accurate text classification. Methods: In a retrospective study, 2256 radiologist-annotated radiology requests (8 classes) and reports (10 classes) were divided into training and testing datasets (90% and 10%, respectively) and used to train 32 models. Performance metrics were compared by model type (LSTM, Bertje, RobBERT, BERT-clinical, BERT-multilingual, BERT-base), text length, data prevalence, and training strategy. The best models were used to predict the remaining 40,873 cases’ categories of the datasets of requests and reports. Results: The RobBERT model performed the best after 4000 training iterations, resulting in AUC values ranging from 0.808 [95% CI (0.757–0.859)] to 0.976 [95% CI (0.956–0.996)] for the requests and 0.746 [95% CI (0.689–0.802)] to 1.0 [95% CI (1.0–1.0)] for the reports. The AUC for the classification of normal reports was 0.95 [95% CI (0.922–0.979)]. The predicted data demonstrated variability of both diagnostic yield for various request classes and request patterns related to COVID-19 hospital admission data. Conclusion: Transformer-based natural language processing is feasible for the multilabel classification of chest imaging request and report items. Diagnostic yield varies with the information in the requests

Large atom number Bose-Einstein condensate of sodium

We describe the setup to create a large Bose-Einstein condensate containing more than 120x10^6 atoms. In the experiment a thermal beam is slowed by a Zeeman slower and captured in a dark-spot magneto-optical trap (MOT). A typical dark-spot MOT in our experiments contains 2.0x10^10 atoms with a temperature of 320 microK and a density of about 1.0x10^11 atoms/cm^3. The sample is spin polarized in a high magnetic field, before the atoms are loaded in the magnetic trap. Spin polarizing in a high magnetic field results in an increase in the transfer efficiency by a factor of 2 compared to experiments without spin polarizing. In the magnetic trap the cloud is cooled to degeneracy in 50 s by evaporative cooling. To suppress the 3-body losses at the end of the evaporation the magnetic trap is decompressed in the axial direction.Comment: 11 pages, 12 figures, submitted to Review Of Scientific Instrument

Cavity Optomechanical Magnetometer

A cavity optomechanical magnetometer is demonstrated where the magnetic field induced expansion of a magnetostrictive material is transduced onto the physical structure of a highly compliant optical microresonator. The resulting motion is read out optically with ultra-high sensitivity. Detecting the magnetostrictive deformation of Terfenol-D with a toroidal whispering gallery mode (TWGM) resonator a peak sensitivity of 400 nT/Hz^.5 was achieved with theoretical modelling predicting that sensitivities of up to 500 fT/Hz^.5 may be possible. This chip-based magnetometer combines high-sensitivity and large dynamic range with small size and room temperature operation

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

The small heat shock protein 20 RSI2 interacts with and is required for stability and function of tomato resistance protein I-2

Race-specific disease resistance in plants depends on the presence of resistance (R) genes. Most R genes encode NB-ARC-LRR proteins that carry a C-terminal leucine-rich repeat (LRR). Of the few proteins found to interact with the LRR domain, most have proposed (co)chaperone activity. Here, we report the identification of RSI2 (Required for Stability of I-2) as a protein that interacts with the LRR domain of the tomato R protein I-2. RSI2 belongs to the family of small heat shock proteins (sHSPs or HSP20s). HSP20s are ATP-independent chaperones that form oligomeric complexes with client proteins to prevent unfolding and subsequent aggregation. Silencing of RSI2-related HSP20s in Nicotiana benthamiana compromised the hypersensitive response that is normally induced by auto-active variants of I-2 and Mi-1, a second tomato R protein. As many HSP20s have chaperone properties, the involvement of RSI2 and other R protein (co)chaperones in I-2 and Mi-1 protein stability was examined. RSI2 silencing compromised the accumulation of full-length I-2 in planta, but did not affect Mi-1 levels. Silencing of heat shock protein 90 (HSP90) and SGT1 led to an almost complete loss of full-length I-2 accumulation and a reduction in Mi-1 protein levels. In contrast to SGT1 and HSP90, RSI2 silencing led to accumulation of I-2 breakdown products. This difference suggests that RSI2 and HSP90/SGT1 chaperone the I-2 protein using different molecular mechanisms. We conclude that I-2 protein function requires RSI2, either through direct interaction with, and stabilization of I-2 protein or by affecting signalling components involved in initiation of the hypersensitive response

Measurement of the 3s3p 3P1 lifetime in magnesium using a magneto-optical trap

We demonstrate an accurate method for measuring the lifetime of long-lived metastable magnetic states using a magneto-optical trap (MOT). Through optical pumping, the metastable (3s3p) (3)P(1) level is populated in a standard MOT. During the optical pumping process, a fraction of the population is captured in the magnetic quadrupole field of the MOT. When the metastable atoms decay to the (3s(2)) (1)S(0) ground state they are recaptured into the MOT. In this system no alternative cascading transition is possible. The lifetime of the metastable level is measured directly as an exponential load time of the MOT. We have experimentally tested our method by measuring the lifetime of the (3s3p) (3)P(1) of (24)Mg. This lifetime has been measured numerous times previously, but with quite different results. Using our method we find the (3s3p) (3)P(1) lifetime to be (4.4 +/- 0.2) ms. Theoretical values point toward a lower value for the lifetime

Vortex Motion Noise in Micrometre-Sized Thin Films of the Amorphous Nb0.7Ge0.3 Weak-Pinning Superconductor

We report high-resolution measurements of voltage (V) noise in the mixed state of micrometre-sized thin films of amorphous Nb0.7Ge0.3, which is a good representative of weak-pinning superconductors. There is a remarkable difference between the noise below and above the irreversibility field Birr. Below Birr, in the presence of measurable pinning, the noise at small applied currents resembles shot noise, and in the regime of flux flow at larger currents decreases with increasing voltage due to a progressive ordering of the vortex motion. At magnetic fields B between Birr and the upper critical field Bc2 flux flow is present already at vanishingly small currents. In this regime the noise scales with (1-B/Bc2)^2 V^2 and has a frequency (f) spectrum of 1/f type. We interpret this noise in terms of the properties of strongly driven depinned vortex systems at high vortex density.Comment: 8 pages, 5 figures, version accepted for publication in PR

Susceptibility calculations for alternating antiferromagnetic chains

Earlier work of Duffy and Barr consisting of exact calculations on alternating antiferromagnetic Heisenberg spin‐1/2 chains is extended to longer chains of up to 12 spins, and subsequent extrapolations of thermodynamic properties, particularly the susceptibility, are extended to the weak alternation region close to the uniform limit. This is the region of interest in connection with the recent experimental discovery of spin‐Peierls systems. The extrapolated susceptibility curves are compared with corresponding curves calculated from the model of Bulaevskii, which has been used extensively in approximate theoretical treatments of a variety of phenomena. Qualitative agreement is observed in the uniform limit and persists for all degrees of alternation, but quantitative differences of about 10% are present over the whole range, including the isolated dimer limit. Potential application of the new susceptibility calculations to experiment is discussed

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