Artificial Intelligence Outcome Prediction in Neonates with Encephalopathy (AI-OPiNE).

Purpose To develop a deep learning algorithm to predict 2-year neurodevelopmental outcomes in neonates with hypoxic-ischemic encephalopathy using MRI and basic clinical data. Materials and Methods In this study, MRI data of term neonates with encephalopathy in the High-dose Erythropoietin for Asphyxia and Encephalopathy (HEAL) trial (ClinicalTrials.gov: NCT02811263), who were enrolled from 17 institutions between January 25, 2017, and October 9, 2019, were retrospectively analyzed. The harmonized MRI protocol included T1-weighted, T2-weighted, and diffusion tensor imaging. Deep learning classifiers were trained to predict the primary outcome of the HEAL trial (death or any neurodevelopmental impairment at 2 years) using multisequence MRI and basic clinical variables, including sex and gestational age at birth. Model performance was evaluated on test sets comprising 10% of cases from 15 institutions (in-distribution test set, n = 41) and 10% of cases from two institutions (out-of-distribution test set, n = 41). Model performance in predicting additional secondary outcomes, including death alone, was also assessed. Results For the 414 neonates (mean gestational age, 39 weeks ± 1.4 [SD]; 232 male, 182 female), in the study cohort, 198 (48%) died or had any neurodevelopmental impairment at 2 years. The deep learning model achieved an area under the receiver operating characteristic curve (AUC) of 0.74 (95% CI: 0.60, 0.86) and 63% accuracy in the in-distribution test set and an AUC of 0.77 (95% CI: 0.63, 0.90) and 78% accuracy in the out-of-distribution test set. Performance was similar or better for predicting secondary outcomes. Conclusion Deep learning analysis of neonatal brain MRI yielded high performance for predicting 2-year neurodevelopmental outcomes. Keywords: Convolutional Neural Network (CNN), Prognosis, Pediatrics, Brain, Brain Stem Clinical trial registration no. NCT02811263 Supplemental material is available for this article. © RSNA, 2024 See also commentary by Rafful and Reis Teixeira in this issue.

A publicly available deep learning model and dataset for segmentation of breast, fibroglandular tissue, and vessels in breast MRI.

Breast density, or the amount of fibroglandular tissue (FGT) relative to the overall breast volume, increases the risk of developing breast cancer. Although previous studies have utilized deep learning to assess breast density, the limited public availability of data and quantitative tools hinders the development of better assessment tools. Our objective was to (1) create and share a large dataset of pixel-wise annotations according to well-defined criteria, and (2) develop, evaluate, and share an automated segmentation method for breast, FGT, and blood vessels using convolutional neural networks. We used the Duke Breast Cancer MRI dataset to randomly select 100 MRI studies and manually annotated the breast, FGT, and blood vessels for each study. Model performance was evaluated using the Dice similarity coefficient (DSC). The model achieved DSC values of 0.92 for breast, 0.86 for FGT, and 0.65 for blood vessels on the test set. The correlation between our model's predicted breast density and the manually generated masks was 0.95. The correlation between the predicted breast density and qualitative radiologist assessment was 0.75. Our automated models can accurately segment breast, FGT, and blood vessels using pre-contrast breast MRI data. The data and the models were made publicly available.

MRI-based Deep Learning Assessment of Amyloid, Tau, and Neurodegeneration Biomarker Status across the Alzheimer Disease Spectrum.

Background PET can be used for amyloid-tau-neurodegeneration (ATN) classification in Alzheimer disease, but incurs considerable cost and exposure to ionizing radiation. MRI currently has limited use in characterizing ATN status. Deep learning techniques can detect complex patterns in MRI data and have potential for noninvasive characterization of ATN status. Purpose To use deep learning to predict PET-determined ATN biomarker status using MRI and readily available diagnostic data. Materials and Methods MRI and PET data were retrospectively collected from the Alzheimer's Disease Imaging Initiative. PET scans were paired with MRI scans acquired within 30 days, from August 2005 to September 2020. Pairs were randomly split into subsets as follows: 70% for training, 10% for validation, and 20% for final testing. A bimodal Gaussian mixture model was used to threshold PET scans into positive and negative labels. MRI data were fed into a convolutional neural network to generate imaging features. These features were combined in a logistic regression model with patient demographics, APOE gene status, cognitive scores, hippocampal volumes, and clinical diagnoses to classify each ATN biomarker component as positive or negative. Area under the receiver operating characteristic curve (AUC) analysis was used for model evaluation. Feature importance was derived from model coefficients and gradients. Results There were 2099 amyloid (mean patient age, 75 years ± 10 [SD]; 1110 male), 557 tau (mean patient age, 75 years ± 7; 280 male), and 2768 FDG PET (mean patient age, 75 years ± 7; 1645 male) and MRI pairs. Model AUCs for the test set were as follows: amyloid, 0.79 (95% CI: 0.74, 0.83); tau, 0.73 (95% CI: 0.58, 0.86); and neurodegeneration, 0.86 (95% CI: 0.83, 0.89). Within the networks, high gradients were present in key temporal, parietal, frontal, and occipital cortical regions. Model coefficients for cognitive scores, hippocampal volumes, and APOE status were highest. Conclusion A deep learning algorithm predicted each component of PET-determined ATN status with acceptable to excellent efficacy using MRI and other available diagnostic data. © RSNA, 2023 Supplemental material is available for this article.

Chest tube output, duration, and length of stay are similar for pneumothorax and hemothorax seen only on computed tomography vs. chest radiograph.

PURPOSE: Whole-body computed tomography (CT) for blunt trauma patients is common. Chest CT (CCT) identifies "occult" pneumo- (PTX) and hemothorax (HTX) not seen on chest radiograph (CXR), one-third of whom get chest tubes, while CXR identifies "non-occult" PTX/HTX. To assess chest tube value for occult injury vs. expectant management, we compared output, duration, and length of stay (LOS) for chest tubes placed for occult vs. non-occult (CXR-visible) injury. METHODS: We compared chest tube output and duration, and patient length of stay for occult vs. non-occult PTX/HTX. This was a retrospective analysis of 5451 consecutive Level I blunt trauma patients, from 2010 to 2013. RESULTS: Of these blunt trauma patients, 402 patients (7.4%) had PTX, HTX or both, and both CXR and CCT. One third (n = 136, 33.8%) had chest tubes placed in 163 hemithoraces (27 bilateral). Non-occult chest tube output for all patients was 1558 ± 1919 cc (n = 54), similar to occult at 1123 ± 1076 cc (n = 109, p = 0.126). Outputs were similar for HTX-only patients, with non-occult (n = 34) at 1917 ± 2130 cc, vs. occult (n = 54) at 1449 ± 1131 cc (p = 0.24). Chest tube duration for all patients was 6.3 ± 4.9 days for non-occult vs. 5.0 ± 3.3 for occult (p = 0.096). LOS was similar between all occult injury patients (n = 46) and non-occult (n = 90, 17.0 ± 15.8 vs. 13.7 ± 11.9 days, p = 0.23). CONCLUSION: Mature clinical judgment may dictate which patients need chest tubes and explain the similarity between groups.