Deep Learning Analysis of Chest Radiographs to Triage Patients with Acute Chest Pain Syndrome

Defining the cross-reactivity between peanut allergens Ara h 2 and Ara h 6 using monoclonal antibodies

In peanut allergy, Arachis hypogaea 2 (Ara h 2) and Arachis hypogaea 6 (Ara h 6) are two clinically relevant peanut allergens with known structural and sequence homology and demonstrated cross-reactivity. We have previously utilized X-ray crystallography and epitope binning to define the epitopes on Ara h 2. We aimed to quantitatively characterize the cross-reactivity between Ara h 2 and Ara h 6 on a molecular level using human monoclonal antibodies (mAbs) and structural characterization of allergenic epitopes. We utilized mAbs cloned from Ara h 2 positive single B cells isolated from peanut-allergic, oral immunotherapy-treated patients to quantitatively analyze cross-reactivity between recombinant Ara h 2 (rAra h 2) and Ara h 6 (rAra h 6) proteins using biolayer interferometry and indirect inhibitory ELISA. Molecular dynamics simulations assessed time-dependent motions and interactions in the antibody-antigen complexes. Three epitopes-conformational epitopes 1.1 and 3, and the sequential epitope KRELRNL/KRELMNL-are conserved between Ara h 2 and Ara h 6, while two more conformational and three sequential epitopes are not. Overall, mAb affinity was significantly lower to rAra h 6 than it was to rAra h 2. This difference in affinity was primarily due to increased dissociation of the antibodies from rAra h 6, a phenomenon explained by the higher conformational flexibility of the Ara h 6-antibody complexes in comparison to Ara h 2-antibody complexes. Our results further elucidate the cross-reactivity of peanut 2S albumins on a molecular level and support the clinical immunodominance of Ara h 2.

Patient Reidentification from Chest Radiographs: An Interpretable Deep Metric Learning Approach and Its Applications

Purpose

To train an explainable deep learning model for patient reidentification in chest radiograph datasets and assess changes in model-perceived patient identity as a marker for emerging radiologic abnormalities in longitudinal image sets.

Materials and Methods

This retrospective study used a set of 1 094 537 frontal chest radiographs and free-text reports from 259 152 patients obtained from six hospitals between 2006 and 2019, with validation on the public ChestX-ray14, CheXpert, and MIMIC-CXR datasets. A deep learning model was trained for patient reidentification and assessed on patient identity confirmation, retrieval of patient images from a database based on a query image, and radiologic abnormality prediction in longitudinal image sets. The representation learned was incorporated into a generative adversarial network, allowing visual explanations of the relevant features. Performance was evaluated with sensitivity, specificity, F1 score, Precision at 1, R-Precision, and area under the receiver operating characteristic curve (AUC) for normal and abnormal prediction.

Results

Patient reidentification was achieved with a mean F1 score of 0.996 ± 0.001 (2 SD) on the internal test set (26 152 patients) and F1 scores of 0.947-0.993 on the external test data. Database retrieval yielded a mean Precision at 1 score of 0.976 ± 0.005 at 299 × 299 resolution on the internal test set and Precision at 1 scores between 0.868 and 0.950 on the external datasets. Patient sex, age, weight, and other factors were identified as key model features. The model achieved an AUC of 0.73 ± 0.01 for abnormality prediction versus an AUC of 0.58 ± 0.01 for age prediction error.

Conclusion

The image features used by a deep learning patient reidentification model for chest radiographs corresponded to intuitive human-interpretable characteristics, and changes in these identifying features over time may act as markers for an emerging abnormality.Keywords: Conventional Radiography, Thorax, Feature Detection, Supervised Learning, Convolutional Neural Network, Principal Component Analysis Supplemental material is available for this article. © RSNA, 2023See also the commentary by Raghu and Lu in this issue.

The Feasibility and Performance of Total Hip Replacement Prediction Deep Learning Algorithm with Real World Data

(1) Background: Hip degenerative disorder is a common geriatric disease is the main causes to lead to total hip replacement (THR). The surgical timing of THR is crucial for post-operative recovery. Deep learning (DL) algorithms can be used to detect anomalies in medical images and predict the need for THR. The real world data (RWD) were used to validate the artificial intelligence and DL algorithm in medicine but there was no previous study to prove its function in THR prediction. (2) Methods: We designed a sequential two-stage hip replacement prediction deep learning algorithm to identify the possibility of THR in three months of hip joints by plain pelvic radiography (PXR). We also collected RWD to validate the performance of this algorithm. (3) Results: The RWD totally included 3766 PXRs from 2018 to 2019. The overall accuracy of the algorithm was 0.9633; sensitivity was 0.9450; specificity was 1.000 and the precision was 1.000. The negative predictive value was 0.9009, the false negative rate was 0.0550, and the F1 score was 0.9717. The area under curve was 0.972 with 95% confidence interval from 0.953 to 0.987. (4) Conclusions: In summary, this DL algorithm can provide an accurate and reliable method for detecting hip degeneration and predicting the need for further THR. RWD offered an alternative support of the algorithm and validated its function to save time and cost.