To establish a prediction model and assess the risk factors for severe diabetic ketoacidosis (DKA) in adult patients during the ICU. With DKA hospitalization rates consistently increasing, in-hospital mortality has become a growing concern. DKA patients aged >18 years old in the US-based critical care database (Medical Information Mart for Intensive Care (MIMIC-IV)) were considered. Independent risk factors for in-hospital mortality were screened using extreme gradient boosting (XGBoost) and the Bayesian information criterion (BIC) optimal subset regression. One predictive model was developed using machine learning extreme gradient boosting (XGBoost), and the other one was a nomogram based on logistic regression to estimate risks of in-hospital mortality with severe DKA. Established models were assessed by using internal validation and external validation. The MIMIC-IV was split into training and testing samples in a 7:3 ratio. The eICU Collaborative Research Database and admissions data from the department of critical care medicine of the first affiliated hospital of Harbin medical university were used for independent validation. The discriminatory ability of the model was determined by illustrating a receiver operating curve (ROC) and calculating the C-index. Meanwhile, the calibration plot and Hosmer-Lemeshow goodness-of-fit test (HL test) was conducted to evaluate the performance of our new build model. Decision curve analysis (DCA) was performed to assess the clinical net benefit. Net Reclassification Improvement (NRI) was used to compare the predictive power of the two models. A multivariable model that included acute physiology score III (APS III), the highest levels of blood plasma osmolality (osmolarity_max), minimum osmolarity (osmolarity_min)/osmolarity _max, vasopressor, and the highest levels of blood lactate was represented as the nomogram. The C- index of the nomogram model was 0.915 (95% CI: 0.966-0.864) in the training dataset and 0.971 (95% CI: 0.992-0.950) in the internal validation. The nomogram's sensitivity was well according to all data's HL test (P > 0.05). DCA showed that our model was clinically valuable. The XGB (extreme gradient boosting) model achieved an AUC (area under the curve) of 0.950 (95% CI, 0.920-0.980); however, the nomogram model made was more effective than XGB based on NRI. The predictive XGB and nomogram models for predicting in-hospital patient deaths with DKA were effective. The forecast models can help clinical physicians promptly identify patients at high risk of DKA, prevent in-hospital deaths, and promptly intervene.

Pulmonary artery-vein segmentation is critical for disease diagnosis and surgical planning. Traditional methods rely on Computed Tomography Pulmonary Angiography (CTPA), which requires contrast agents with potential health risks. Non-contrast CT, a safer and more widely available approach, however, has long been considered impossible for this task. Here we propose High-abundant Pulmonary Artery-vein Segmentation (HiPaS), enabling accurate segmentation across both non-contrast CT and CTPA at multiple resolutions. HiPaS integrates spatial normalization with an iterative segmentation strategy, leveraging lower-level vessel segmentations as priors for higher-level segmentations. Trained on a multi-center dataset comprising 1073 CT volumes with manual annotations, HiPaS achieves superior performance (dice score: 91.8%, sensitivity: 98.0%) and demonstrates non-inferiority on non-contrast CT compared to CTPA. Furthermore, HiPaS enables large-scale analysis of 11,784 participants, revealing associations between vessel abundance and sex, age, and diseases, under lung-volume control. HiPaS represents a promising, non-invasive approach for clinical diagnostics and anatomical research.

Soft bioelectronics enable noninvasive, continuous monitoring of physiological signals, essential for precision health care. However, capturing biosignals during physical activity, particularly biomechanical signals like cardiac mechanics, remains challenging due to motion-induced interference. Inspired by starfish’s pentaradial symmetry, we introduce a starfish-like wearable bioelectronic system designed for high-fidelity signal monitoring during movement. The device, featuring five flexible, free-standing sensing arms connected to a central electronic hub, substantially reduces mechanical interference and enables high-fidelity acquisition of cardiac electrical (electrocardiogram) and mechanical (seismocardiogram and gyrocardiogram) signals during motion when coupled with signal compensation and machine learning. Using these three cardiac signal types as inputs, machine learning models deployed on smart devices achieve real-time, high-accuracy (more than 91%) diagnoses of heart conditions such as atrial fibrillation, myocardial infarction, and heart failure. These findings open previously undiscovered avenues by leveraging bioinspired device concepts combined with cutting-edge data science to boost bioelectronic performance and diagnostic precision.

Tremendous efforts have been made to improve diagnosis and treatment of COVID-19, but knowledge on long-term complications is limited. In particular, a large portion of survivors has respiratory complications, but currently, experienced radiologists and state-of-the-art artificial intelligence systems are not able to detect many abnormalities from follow-up computerized tomography (CT) scans of COVID-19 survivors. Here we propose Deep-LungParenchyma-Enhancing (DLPE), a computer-aided detection (CAD) method for detecting and quantifying pulmonary parenchyma lesions on chest CT. Through proposing a number of deep-learning-based segmentation models and assembling them in an interpretable manner, DLPE removes irrelevant tissues from the perspective of pulmonary parenchyma, and calculates the scan-level optimal window, which considerably enhances parenchyma lesions relative to the lung window. Aided by DLPE, radiologists discovered novel and interpretable lesions from COVID-19 inpatients and survivors, which were previously invisible under the lung window. Based on DLPE, we removed the scan-level bias of CT scans, and then extracted precise radiomics from such novel lesions. We further demonstrated that these radiomics have strong predictive power for key COVID-19 clinical metrics on an inpatient cohort of 1,193 CT scans and for sequelae on a survivor cohort of 219 CT scans. Our work sheds light on the development of interpretable medical artificial intelligence and showcases how artificial intelligence can discover medical findings that are beyond sight.