The EMory BrEast imaging Dataset (EMBED): A Racially Diverse, Granular Dataset of 3.4 Million Screening and Diagnostic Mammographic Images.

Supplemental material is available for this article. Keywords: Mammography, Breast, Machine Learning © RSNA, 2023.

Early Prediction of Hemodynamic Shock in Pediatric Intensive Care Units With Deep Learning on Thermal Videos.

Shock is one of the major killers in intensive care units, and early interventions can potentially reverse it. In this study, we advance a noncontact thermal imaging modality for continuous monitoring of hemodynamic shock working on 1,03,936 frames from 406 videos recorded longitudinally upon 22 pediatric patients. Deep learning was used to preprocess and extract the Center-to-Peripheral Difference (CPD) in temperature values from the videos. This time-series data along with the heart rate was finally analyzed using Long-Short Term Memory models to predict the shock status up to the next 6 h. Our models achieved the best area under the receiver operating characteristic curve of 0.81 ± 0.06 and area under the precision-recall curve of 0.78 ± 0.05 at 5 h, providing sufficient time to stabilize the patient. Our approach, thus, provides a reliable shock prediction using an automated decision pipeline that can provide better care and save lives.

Automatic Localization and Brand Detection of Cervical Spine Hardware on Radiographs Using Weakly Supervised Machine Learning.

Purpose: To develop an end-to-end pipeline to localize and identify cervical spine hardware brands on routine cervical spine radiographs. Materials and Methods: In this single-center retrospective study, patients who received cervical spine implants between 2014 and 2018 were identified. Information on the implant model was retrieved from the surgical notes. The dataset was filtered for implants present in at least three patients, which yielded five anterior and five posterior hardware models for classification. Images for training were manually annotated with bounding boxes for anterior and posterior hardware. An object detection model was trained and implemented to localize hardware on the remaining images. An image classification model was then trained to differentiate between five anterior and five posterior hardware models. Model performance was evaluated on a holdout test set with 1000 iterations of bootstrapping. Results: A total of 984 patients (mean age, 62 years ± 12 [standard deviation]; 525 women) were included for model training, validation, and testing. The hardware localization model achieved an intersection over union of 86.8% and an F1 score of 94.9%. For brand classification, an F1 score, sensitivity, and specificity of 98.7% ± 0.5, 98.7% ± 0.5, and 99.2% ± 0.3, respectively, were attained for anterior hardware, with values of 93.5% ± 2.0, 92.6% ± 2.0, and 96.1% ± 2.0, respectively, attained for posterior hardware. Conclusion: The developed pipeline was able to accurately localize and classify brands of hardware implants using a weakly supervised learning framework.Keywords: Spine, Convolutional Neural Network, Deep Learning Algorithms, Machine Learning Algorithms, Prostheses, Semisupervised Learning Supplemental material is available for this article. © RSNA, 2022See also commentary by Huisman and Lessmann in this issue.